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The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device's portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting highquality and high-yield crops. [ABSTRACT FROM AUTHOR]

Nitrogen is an essential nutrient for plants and a major determinant of plant growth and crop yield. Plants acquire nitrogen mainly in the form of nitrate and ammonium. Both nitrogen sources affect plant responses and signaling pathways in a different way, but these signaling pathways interact, complicating the study of nitrogen responses. Extensive transcriptome analyses and the construction of gene regulatory networks, mainly in response to nitrate, have significantly advanced our understanding of nitrogen signaling and responses in model plants and crops. In this study, we aimed to generate a more comprehensive gene regulatory network for the major crop, rice, by incorporating the interactions between ammonium and nitrate. To achieve this, we assessed transcriptome changes in rice roots and shoots over an extensive time course under single or combined applications of the two nitrogen sources. This dataset enabled us to construct a holistic co-expression network and identify potential key regulators of nitrogen responses. Next to known transcription factors, we identified multiple new candidates, including the transcription factors OsRLI and OsEIL1, which we demonstrated to induce the primary nitrate-responsive genes OsNRT1.1b and OsNIR1. Our network thus serves as a valuable resource to obtain novel insights in nitrogen signaling. [ABSTRACT FROM AUTHOR]

Introduction: Exogenous melatonin (MT) can promote horticultural crops growth under stress conditions. Methods: In this study, the effects of exogenous MT on the accumulation of selenium (Se) in grape were studied under Se stress. Results and discussion: Under Se stress, exogenous MT increased the biomass, content of photosynthetic pigments and antioxidant enzyme activity of grapevines. Compared with Se treatment, MT increased the root biomass, shoot biomass, chlorophyll a content, chlorophyll b content, carotenoids, superoxide dismutase activity, and peroxidase activity by 18.11%, 7.71%, 25.70%, 25.00%, 25.93%, 5.73%, and 9.41%, respectively. Additionally, MT increased the contents of gibberellin, auxin, and MT in grapevines under Se stress, while it decreased the content of abscisic acid. MT increased the contents of total Se, organic Se and inorganic Se in grapevines. Compared with Se treatment, MT increased the contents of total Se in the roots and shoots by 48.82% and 135.66%, respectively. A transcriptome sequencing analysis revealed that MT primarily regulated the cellular, metabolic, and bioregulatory processes of grapevine under Se stress, and the differentially expressed genes (DEGs) were primarily enriched in pathways, such as aminoacyl-tRNA biosynthesis, spliceosome, and flavonoid biosynthesis. These involved nine DEGs and nine metabolic pathways in total. Moreover, a field experiment showed that MT increased the content of Se in grapes and improved their quality. Therefore, MT can alleviate the stress of Se in grapevines and promote their growth and the accumulation of Se. [ABSTRACT FROM AUTHOR]

Studies are being conducted to develop strategies to reduce the adverse effects of salinity stress. In the present study, it was aimed to determine the interactive effects of salinity stress with biochar on plant growth-the physiological and biochemical attributes of forage peas (Pisum sativum ssp. arvense L.). Salt applications were carried out with irrigation water at concentrations of 0, 25, 50, 75, and 100 mM NaCl. The experiment was conducted using a randomized complete block design with three applications [control: 0 (B0), 2.5% biochar (B1), and 5% biochar (B2)], five salt doses [0 (S0), 25 (S1), 50 (S2), 75 (S3), and 100 (S4) mM NaCl], and three replications, arranged in a 3 × 5 factorial arrangement. In the salt-stressed environment, the highest plant height (18.75 cm) and stem diameter (1.71 mm) in forage pea seedlings were obtained with the application of B1. The root fresh (0.59 g/plant) and dry weight (0.36 g/plant) were determined to be the highest in the B1 application, both in non-saline and saline environments. A decrease in plant chlorophyll content in forage pea plants was observed parallel to the increasing salt levels. Specifically, lower H2O2, MDA, and proline content were determined at all salt levels with biochar applications, while in the B0 application these values were recorded at the highest levels. Furthermore, in the study, it was observed that the CAT, POD, and SOD enzyme activities were at their lowest levels at all salt levels with the biochar application, while in the B0 application, these values were determined to be at the highest levels. There was a significant decrease in plant mineral content, excluding Cl and Na, parallel to the increasing salt levels. The findings of the study indicate that biochar amendment can enhance forage peas' growth by modulating the plant physiology and biochemistry under salt stress. Considering the plant growth parameters, no significant difference was detected between 2.5% and 5% biochar application. Therefore, application of 2.5 biochar may be recommended. [ABSTRACT FROM AUTHOR]

Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate fourdimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird's-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (R² = 0.95, RMSE = 7.6 cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPARf), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts. [ABSTRACT FROM AUTHOR]

Introduction: Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. Methods: In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. Results: The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. Discussion: These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances. [ABSTRACT FROM AUTHOR]

Plant diseases are caused by various microorganisms such as bacteria, fungi, viruses, and nematodes. These diseases impact crop growth, reduce produce quality, and lead to financial losses. Plant disease can be caused by single pathogens or by interactions called "disease complexes", involving two or more pathogens. In these cases, the disease severity caused by the pathogens combined is greater than the sum of the disease caused by each pathogen alone. disease complexes formed among plant-parasitic nematodes (PPNs) with bacteria, fungi, or viruses, can occur. PPNs either enhance the other pathogen incidence and severity or are necessary for disease symptoms to be expressed. PPNs can do so by being wounding agents, vectors, modifiers of plant biochemistry and physiology, or altering the rhizosphere microbiome. This review identifies several PPNs-plant pathogens disease complexes in crop production to discuss how understanding such interactions is key for improving management practices. [ABSTRACT FROM AUTHOR]

The high-throughput and full-time acquisition of images of crop growth processes, and the analysis of the morphological parameters of their features, is the foundation for achieving fast breeding technology, thereby accelerating the exploration of germplasm resources and variety selection by crop breeders. The evolution of embryonic soybean radicle characteristics during germination is an important indicator of soybean seed vitality, which directly affects the subsequent growth process and yield of soybeans. In order to address the time-consuming and labor-intensive manual measurement of embryonic radicle characteristics, as well as the issue of large errors, this paper utilizes continuous time-series crop growth vitality monitoring system to collect fulltime sequence images of soybean germination. By introducing the attention mechanism SegNext_Attention, improving the Segment module, and adding the CAL module, a YOLOv8-segANDcal model for the segmentation and extraction of soybean embryonic radicle features and radicle length calculation was constructed. Compared to the YOLOv8-seg model, the model respectively improved the detection and segmentation of embryonic radicles by 2% and 1% in mAP50-95, and calculated the contour features and radicle length of the embryonic radicles, obtaining the morphological evolution of the embryonic radicle contour features over germination time. This model provides a rapid and accurate method for crop breeders and agronomists to select crop varieties. [ABSTRACT FROM AUTHOR]

Introduction: The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current largescale rice production process. Methods: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. Results: The research results indicate that the training set R² and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R² and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. Discussion: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization. [ABSTRACT FROM AUTHOR]

The expanding cannabis production sector faces economic challenges, intensified by freshwater scarcity in the main US production areas. Greenhouse cultivation harnesses sunlight to reduce production costs, yet the impact of greenhouse light levels on crucial production components, such as plant growth, branching, and water use efficiency (WUE), remains poorly understood. This study aimed to assess the effects of combined sunlight and supplemental lighting on the crop’s main production components and leaf gas exchange of Cannabis sativa ‘Suver Haze’ in the vegetative stage. Within a greenhouse, LED lighting provided at intensities of ~150, 300, 500, and 700 µmol m-2 s-1 (18-hour photoperiod), combined with solar radiation, resulted in average daily light integrals of 17.9, 29.8, 39.5, and 51.8 mol m-2 d-1. Increasing light levels linearly increased biomass, leaf area, and the number of branches per plant and square meter, with respective rates of 0.26 g, 32.5 cm², and 0.41 branches per mole of additional light. As anticipated, crop evapotranspiration increased by 1.8-fold with the increase in light intensity yet crop WUE improved by 1.6-fold when comparing the lowest and highest light treatments. Moreover, water requirements per unit of plant biomass decreased from 0.37 to 0.24 liters per gram when lighting increased from ~18 to 52 mol m-2 d-1, marking a 35% reduction in evapotranspiration. These results were supported by increments in leaf photosynthesis and WUE with light enhancement. Furthermore, our findings indicate that even 52 mol m-2 d-1 of supplemental lighting did not saturate any of the crop responses to light and can be economically viable for cannabis nurseries. In conclusion, light supplementation strongly enhanced photosynthesis and plant growth while increasing WUE. Additionally, a comprehensive discussion highlights the shared physiological mechanisms governing WUE in diverse plant species and their potential for water conservation under enhanced lighting conditions. [ABSTRACT FROM AUTHOR]

Nitrogen (N) and phosphorus (P) fertilizers change the morphological structure and effectiveness of P in the soil, which in turn affects crop growth, yield, and quality. However, the effects and mechanism of combined N and P application on the content of P fractions and the transformation of effective forms in alfalfa (Medicago sativa L.) production is unclear. This experiment was conducted with four levels of N: 0 (N0), 60 (N1), 120 (N2) and 180 kg·ha-1 (N3); and two levels of P (P2O5): 0 (P0) and 100 kg·ha-1 (P1). The results indicated that, under the same N level, P application significantly increased soil total N, and total P, available P, and content of various forms of inorganic P when compared to no P application, while decreasing the content of various forms of organic P and pH value. In general, under P0 conditions, soil total N content tended to increase with increasing N application, while total P, available P content, pH, inorganic P content in all forms, and organic P content in all forms showed a decreasing trend. When compared to no N application, insoluble P (Fe-P, O-P, Ca10-P) of the N application treatments was reduced 2.80 - 22.72, 2.96 - 20.42, and 5.54 - 20.11%, respectively. Under P1 conditions, soil total N and O-P tended to increase with increasing N application, while, pH, Ca2-P, Al-P, Fe-P, Ca10-P, and organic P content of each form tended to decrease. Total P, available P, and labile organic P (LOP) of N application reduced 0.34 - 8.58, 4.76 - 19.38, and 6.27 - 14.93%, respectively, when compared to no application. Nitrogen fertilization reduced the soil Ca2-P ratio, while P fertilization reduced soil Fe-P, moderately resistant organic P (MROP), and highly resistant P (HROP) ratios, and combined N and P elevated the Ca8-P to LOP ratio. The results of redundancy analysis showed that soil total N content, available P content, and pH were the key factors affecting the conversion of P fractions in the soil. Nitrogen and P reduced the proportion of soil insoluble P, promoted the activation of soil organic P, resulting in accumulation of slow-acting P in the soil, thereby improving the efficiency of soil P in alfalfa production. [ABSTRACT FROM AUTHOR]

Introduction: The ratoon rice planting area is gradually expanding, but there has been relatively little research on ratoon rice grains contaminated with Cd. Methods: In this study, five ratoon rice varieties were selected and divided into three groups according to early-maturity (growth duration: 100-110 days), mid-maturity (growth duration: 110-120 days) and late-maturity (growth duration: 120-130 days) varieties. Field experiments were done to study the differences in Cd accumulation among ratoon rice varieties with different growth duration. Results: The results showed that the Cd accumulation and concentration of grains spikelet at each growth stage in the main crop were in the order of late-maturity > mid-maturity > early-maturity varieties. However, the trends in Cd concentration and accumulation in grains spikelet during the ratoon crop were the opposite. Analysis found that as the growth duration of the variety extended, the accumulated temperature and daily average temperature in the main crop increased, which significantly increased the translocation factors of Cd from root, stem, and leaf to grains spikelet, and increased the daily average Cd accumulation rate in grains spikelet. The daily average temperature in the ratoon crop increased as the growth duration shortened. The early-maturity variety had higher Cd accumulation in stubble, which promoted the translocation of Cd from the root, stem, and leaf of the plant to the grains spikelet. Discussion: Therefore, appropriately shortening the growth duration of the main crop and extending the growth duration of the ratoon crop are important ways to reduce Cd accumulation in ratoon rice in areas with mild Cd pollution. [ABSTRACT FROM AUTHOR]

Root-knot nematodes (Meloidogyne spp., RKN) are among the most destructive endoparasitic nematodes worldwide, often leading to a reduction of crop growth and yield. Insights into the dynamics of host-RKN interactions, especially in varied biotic and abiotic environments, could be pivotal in devising novel RKN mitigation measures. Plant growth-promoting bacteria (PGPB) involves different plant growth-enhancing activities such as biofertilization, pathogen suppression, and induction of systemic resistance. We summarized the up-todate knowledge on the role of PGPB and abiotic factors such as soil pH, texture, structure, moisture, etc. in modulating RKN-host interactions. RKN are directly or indirectly affected by different PGPB, abiotic factors interplay in the interactions, and host responses to RKN infection. We highlighted the tripartite (host-RKNPGPB) phenomenon with respect to (i) PGPB direct and indirect effect on RKNhost interactions; (ii) host influence in the selection and enrichment of PGPB in the rhizosphere; (iii) how soil microbes enhance RKN parasitism; (iv) influence of host in RKN-PGPB interactions, and (v) the role of abiotic factors in modulating the tripartite interactions. Furthermore, we discussed how different agricultural practices alter the interactions. Finally, we emphasized the importance of incorporating the knowledge of tripartite interactions in the integrated RKN management strategies. [ABSTRACT FROM AUTHOR]

Chickpea, being an important grain legume crop, is often confronted with the adverse effects of high temperatures at the reproductive stage of crop growth, drastically affecting yield and overall productivity. The current study deals with an extensive evaluation of chickpea genotypes, focusing on the traits associated with yield and their response to heat stress. Notably, we observed significant variations for these traits under both normal and high-temperature conditions, forming a robust basis for genetic research and breeding initiatives. Furthermore, the study revealed that yield-related traits exhibited high heritability, suggesting their potential suitability for marker-assisted selection. We carried out singlenucleotide polymorphism (SNP) genotyping using the genotyping-bysequencing (GBS) method for a genome-wide association study (GWAS). Overall, 27 marker--trait associations (MTAs) linked to yield-related traits, among which we identified five common MTAs displaying pleiotropic effects after applying a stringent Bonferroni-corrected p-value threshold of <0.05 [-log10(p) > 4.95] using the BLINK (Bayesian-information and linkagedisequilibrium iteratively nested keyway) model. Through an in-depth in silico analysis of these markers against the CDC Frontier v1 reference genome, we discovered that the majority of the SNPs were located at or in proximity to genecoding regions. We further explored candidate genes situated near these MTAs, shedding light on the molecular mechanisms governing heat stress tolerance and yield enhancement in chickpeas such as indole-3-acetic acid--amido synthetase GH3.1 with GH3 auxin-responsive promoter and pentatricopeptide repeat-containing protein, etc. The harvest index (HI) trait was associated with marker Ca3:37444451 encoding aspartic proteinase ortholog sequence of Oryza sativa subsp. japonica and Medicago truncatula, which is known for contributing to heat stress tolerance. These identified MTAs and associated candidate genes may serve as valuable assets for breeding programs dedicated to tailoring chickpea varieties resilient to heat stress and climate change. [ABSTRACT FROM AUTHOR]

Phytocannabinoids represent the hallmark of the secondary metabolism of Cannabis sativa. The content of major phytocannabinoids is closely related to genetic variation as well as abiotic elicitors such as temperature, drought, and saline stress. The present study aims to evaluate hemp response to saline irrigation supplied as NaCl solutions with an electrical conductivity (EC) of 2.0, 4.0, and 6.0 dS m-1 (S1, S2, and S3, respectively) compared to a tap water control (S0). In addition, the potential beneficial effect of a plant-based biostimulant (a legume protein hydrolysate) inmitigating the detrimental effects of saline irrigation on crop growth and phytocannabinoid composition was investigated. Sodium chloride saline irrigation significantly reduced biomass production only with S2 and S3 treatments, in accordancewith an induced nutrient imbalance, as evidenced by the mineral profile of leaves. Multivariate analysis revealed that the phytocannabinoid composition, both in inflorescences and leaves, was affected by the salinity level of the irrigation water. Interestingly, higher salinity levels (S2-S3) resulted in the predominance of cannabidiol (CBD), compared to lower salinity ones (S0-S1). Plant growth and nitrogen uptake were significantly increased by the biostimulant application, with significant mitigation of the detrimental effect of saline irrigations [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R²=0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R²=0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops. [ABSTRACT FROM AUTHOR]

Introduction: Weeds are one of the main factors affecting crop growth, making weed control a pressing global problem. In recent years, interest in intelligent mechanical weed-control equipment has been growing. Methods: We propose a semantic segmentation network, RDS_Unet, based on corn seedling fields built upon an improved U-net network. This network accurately recognizes weeds even under complex environmental conditions, facilitating the use of mechanical weeding equipment for reducing weed density. Our research utilized field-grown maize seedlings and accompanying weeds in expansive fields. We integrated the U-net semantic segmentation network, employing ResNeXt-50 for feature extraction in the encoder stage. In the decoder phase, Layer 1 uses deformable convolution with adaptive offsets, replacing traditional convolution. Furthermore, concurrent spatial and channel squeeze and excitation is incorporated after ordinary convolutional layers in Layers 2, 3, and 4. Results: Compared with existing classical semantic segmentation models such as U-net, Pspnet, and DeeplabV3, our model demonstrated superior performance on our specially constructed seedling grass semantic segmentation dataset, CGSSD, during the maize seedling stage. The Q6mean intersection over union (MIoU), precision, and recall of this network are 82.36%, 91.36%, and 89.45%, respectively. Compared to those of the original network, the proposed network achieves improvements of 5.91, 3.50, and 5.49 percentage points in the MIoU, precision, and recall, respectively. The detection speed is 12.6 frames per second. In addition, ablation experiments further confirmed the impactful contribution of each improvement component on the overall semantic segmentation performance. Discussion: This study provides theoretical and technical support for the automated operation of intelligent mechanical weeding devices. [ABSTRACT FROM AUTHOR]

This study aimed to identify the most influential soil and environmental factors for predicting wheat yield (WY) in a part of irrigated croplands in southwest Iran, using the FAO-Agro-Climate method and machine learning algorithms (MLAs). A total of 60 soil samples and wheat grain (1 m × 1 m) in 1200 ha of Pasargad plain were collected and analyzed in the laboratory. Attainable WY was assessed using the FAO method for the area. Pearson correlation analysis was used to select the best set of soil properties for modeling. Topographic attributes and vegetation indices were used as proxies of landscape components and cover crop to map actual WY in the study area. Two well-known MLAs, random forest (RF) and artificial neural networks (ANNs), were utilized to prepare an actual continuous WY map. The k-fold method was used to determine the uncertainty of WY prediction and quantify the quality of prediction accuracy. Results showed that soil organic carbon (SOC) and total nitrogen (TN) had a positive and significant correlation with WY. The SOC, TN, normalized different vegetation index (NDVI), and channel network base level (CHN) were recognized as the most important predictors and justifying more than 50% of actual WY. The ANNs outperformed the RF algorithm with an R² of 0.75, RMSE of 400 (kg ha-1), and RPD of 2.79, according to statistical indices. The uncertainty analysis showed that themaximum uncertainty of the predictionmap [400 (kg ha-1)] was very low compared to the mean value [4937 (kg ha-1)] of WY map. Calculation yield gap using the FAO-agro-climatic model showed that the average yield gap of the region was about 50% of actual yield. The findings of this study demonstrated that integrating simulated attainable crop growth using crop model and a set of soil and environmental covariates with the ANNs algorithm can effectively predict WY gaps in large areas with acceptable and reasonable accuracy. The study emphasizes that the implementation of efficient management practices has the potential to enhance agricultural production in the study area and similar regions. These results represent a significant advancement of sustainable agriculture and provide valuable insights for ensuring global food security. [ABSTRACT FROM AUTHOR]

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models. Hence, this study aimed to develop a novel method for estimating canopy chlorophyll content (represented by SPAD values) in maize (Zea mays L.) canopies. Firstly, we investigated the spatiotemporal distribution patterns of maize canopy SPAD values under varying nitrogen application rates and different growth stages. The results revealed a non-uniform, "bell-shaped" curve distribution of maize canopy SPAD values in the vertical direction. Nitrogen application significantly influenced the distribution structure of SPAD values within the canopy. Secondly, we achieved satisfactory results by fitting the Lorentz peak distribution function to the SPAD values of different leaf positions in maize. The fitting performance, evaluated using R² and RMSE, ranged from 0.69 to 0.98 and 0.45 to 3.59, respectively, for the year 2021, and from 0.69 to 0.77 and 2.38 to 6.51, respectively, for the year 2022.Finally, based on the correlation between canopy SPAD values and vegetation indices (VIs) at different growth stages, we identified the sensitive leaf positions for the selected CCCI (Canopy Chlorophyll Index) in each growth stage. The 6th (r = 0.662), 4th (r = 0.816), 12th (r = 0.722), and 12th (r = 0.874) leaf positions exhibited the highest correlations. Compared to the estimation model using canopy wide SPAD values, the model based on sensitive leaf positions showed improved accuracy, with increases of 34%, 3%, 20%, and 3% for each growth stage, respectively. In conclusion, the findings of this study contribute to the enhancement of chlorophyll content estimation models in crop canopies and provide valuable insights for the integration of crop growth models with remote sensing methods. [ABSTRACT FROM AUTHOR]

Weeds can compete with crops for sunlight, water, space and various nutrients, which can affect the growth of crops. In recent years, people have started to use self-driving agricultural equipment, robots, etc. for weeding work and use of drones for weed identification and spraying of weeds with herbicides, and the effectiveness of these mobile weeding devices is largely limited by the superiority of weed detection capability. To improve the weed detection capability of mobile weed control devices, this paper proposes a lightweight weed segmentation network model DCSAnet that can be better applied to mobile weed control devices. The whole network model uses an encoder-decoder structure and the DCA module as the main feature extraction module. The main body of the DCA module is based on the reverse residual structure of MobileNetV3, effectively combines asymmetric convolution and depthwise separable convolution, and uses a channel shuffle strategy to increase the randomness of feature extraction. In the decoding stage, feature fusion utilizes the high-dimensional feature map to guide the aggregation of low-dimensional feature maps to reduce feature loss during fusion and increase the accuracy of the model. To validate the performance of this network model on the weed segmentation task, we collected a soybean field weed dataset containing a large number of weeds and crops and used this dataset to conduct an experimental study of DCSAnet. The results showed that our proposed DCSAnet achieves an MIoU of 85.95% with a model parameter number of 0.57 M and the highest segmentation accuracy in comparison with other lightweight networks, which demonstrates the effectiveness of the model for the weed segmentation task. [ABSTRACT FROM AUTHOR]

Sap from the fresh seaweed Kappaphycus alvarezii (KA) has been reported to improve crop growth, quality, and stress alleviation. However, limited studies are reported for the minimally processed aqueous homogenates (MPHs) derived from dry seaweeds. The present investigation was envisaged to characterize the MPHs from the red seaweed KA and a brown seaweed (SW) and also assess the effect of foliar application on maize ( )crop performance when applied alone or in proportions ranging from 0% to 100%. Two doses (0.35% and 0.7%) were compared with control. Both the MPHs contained several compounds like retronecine, tyrosyl-glycine, hexyl 2- furoate, 1-phosphatidyl-1D-myo-inositol, 12-(2,3-dihydroxycyclopentyl)-2- dodecanone, and trihomomethionine and many others that have known bioactivity for enhancing plant growth and providing stress tolerance. Both doses of MPHs enhanced crop growth and yield; however, the best response was in general observed at a lower dose. The MPH of SW at 100% gave the highest seed yield at a lower dose, which was also on par with that obtained under a lower dose of 100% KA. Other combinations, 80:20 and 40:60 KA : SW, were also found to give comparable yields. The highest dose of 100% MPH of SW was found on par with control, a phenomenon that was investigated in detail with respect to metabolites and antioxidant profile in leaves as well as membrane modeling. Higher ROS and certain sugar and organic acids were observed in 100% MPH of SW at a higher dose, although none of the antioxidant enzymes were significantly affected, nor was there any change in membrane characteristics of the leaf with respect to control as well as lower dose. Improvements in the seed yield were attributed to improved photosynthate production on account of higher dry matter accumulation in the MPH-treated plants, which may also be attributed to the presence of bioactive compounds in the biostimulants. In the future, it is imperative to direct scientific investigations towards the quantification and identification of themost effective concentrations of these compounds within MPHs to optimize plant responses. The study indicated the beneficial use of the MPHs towards increasing crop production by employing optimum dose as foliar spray to crops. [ABSTRACT FROM AUTHOR]

In recent years, hydroponic greenhouse cultivation has gained increasing popularity: the combination of hydroponics’ highly efficient use of resources with a controlled environment and an extended growing season provided by greenhouses allows for optimized, year-round plant growth. In this direction, precise and effective irrigation management is critical for achieving optimal crop yield while ensuring an economical use of water resources. This study explores techniques for explaining and predicting daily water consumption by utilizing only easily readily available meteorological data and the progressively growing records of the water consumption dataset. In situations where the dataset is limited in size, the conventional purely data-based approaches that rely on statistically benchmarking time series models tend to be too uncertain. Therefore, the objective of this study is to explore the potential contribution of crop models’ main concepts in constructing more robust models, even when plant measurements are not available. Two strategies were developed for this purpose. The first strategy utilized the Greenlab model, employing reference parameter values from previously published papers and re-estimating, for identifiability reasons, only a limited number of parameters. The second strategy adopted key principles from crop growth models to propose a novel modeling approach, which involved deriving a Stochastic Segmentation of input Energy (SSiE) potentially absorbed by the elementary photosynthetically active parts of the plant. Several model versions were proposed and adjusted using the maximum likelihood method. We present a proof-of-concept of our methodology applied to the ekstasis Tomato, with one recorded time series of daily water uptake. This method provides an estimate of the plant’s dynamic pattern of light interception, which can then be applied for the prediction of water consumption. The results indicate that the SSiE models could become valuable tools for extracting crop information efficiently from routine greenhouse measurements with further development and testing. This, in turn, could aid in achieving more precise irrigation management. [ABSTRACT FROM AUTHOR]

Salt and alkaline stresses often occur together, severely threatening plant growth and crop yields. Salt stress induces osmotic stress, ionic stress, and secondary stresses, such as oxidative stress. Plants under saline-alkali stress must develop suitable mechanisms for adapting to the combined stress. Sustained plant growth requires maintenance of ion and pH homeostasis. In this review, we focus on the mechanisms of ion and pH homeostasis in plant cells under salinealkali stress, including regulation of ion sensing, ion uptake, ion exclusion, ion sequestration, and ion redistribution among organs by long-distance transport. We also discuss outstanding questions in this field. [ABSTRACT FROM AUTHOR]

Introduction: Intercropping and straw mulching are sustainable agricultural practices that can positively affect crop growth and development, especially together. Methods: A split-plot experimental design was used to investigate the effects of intercropping and straw mulching on crop growth, crop yield, nitrogen uptake, and photosynthetic characteristics. The main plot focused on three planting patterns: soybean monoculture (S), maize monoculture (M), and maize/soybean intercropping (I). The subplot structure consisted of four levels of straw mulching (0, 4.8, 7.2, 9.6 t ha-1). Results: Interaction and variance analyses showed that straw mulching, intercropping, and their interaction had significant effects on plant height, stem diameter, leaf area index, chlorophyll content, nitrogen uptake, photosynthetic characteristics, and crop yield. Based on two-year averages for maize and soybean, the net photosynthetic rate (Pn) was up to 51.6% higher, stomatal conductance (Sc) was up to 44.0% higher, transpiration rate (Tr) was up to 46.6% higher, and intercellular carbon dioxide concentration (Ci) was up to 25.7% lower relative to no mulching. The maximum increases of Pn, Sc, and Tr of intercropped maize were 15.48%, 17.28%, and 23.94%, respectively, and the maximum Ci was 17.75% lower than that of monoculture maize. The maximum increase of Pn, Sc, and Tr of monoculture soybean was 24.58%, 16.90%, and 17.91%, respectively, and the maximum Ci was 13.85% lower than that of intercropped soybean. The nitrogen uptake of maize and soybean in the mulching treatment was 24.3% higher than that in the non-mulching treatment; the nitrogen uptake of intercropped maize was 34.2% higher than that of monoculture maize, and the nitrogen uptake of monoculture soybean was 15.0% higher than that of intercropped soybean. The yield of maize and soybean in the mulching treatment was 66.6% higher than that in the nonmulching treatment, the maize yield under intercropping was 15.4% higher than that under monoculture, and the yield of monoculture soybean was 9.03% higher than that of intercropped soybean. Discussion: The growth index and photosynthesis of crops are important parts of yield formation. The results of this study confirmed that straw mulching, intercropping, and their interaction can ultimately increase crop yield by improving crop growth, nitrogen uptake, and photosynthesis. This result can be used as the theoretical basis for the combined application of these measures in agriculture. [ABSTRACT FROM AUTHOR]

Floods and droughts on farmland seriously damage agricultural production. Porous fiber materials (PFM) made from mineral rocks have high porosity, permeability, and water retention and are utilized widely in green roofs and agricultural production. Therefore, studying the impact of PFM on the improvement of farmland is of great importance for soil and water conservation. We set 64 extreme rainfalls to analyze the impact of PFM on soil water content (SWC), runoff, nutrient loss, microorganism, and plant growth. The results showed that PFM can effectively reduce runoff and improve soil water distribution, and enhance the soil water holding capacity. Furthermore, PFM reduced the loss of nitrogen and phosphorus by 18.3% to 97% in the runoff, and the soil erosion of summer corn was more strongly influenced by lower vegetation cover, compared with winter wheat. Finally, when PFM was buried in the soil, the wheat yield increased by -6.7%-20.4%, but the corn yield in some PFM groups decreased by 5.1% to 42.5% under short-duration irrigation conditions. Our study emphasizes that the effectiveness of PFM depends mainly on the following: First, PFM with high porosity can increase soil water holding capacity and timely replenish the water lost from the surrounding soil. Second, PFM with high permeability can increase infiltration during rainfall and decrease runoff and nutrient loss, reducing the risk of farmland flooding and pollution. Finally, PFM consists of gold ions and alkali metal oxides, which can stabilize agglomerates and improve soil enzyme activity, thereby increasing the relative abundance of some microbial strains and promoting crop growth. However, when the rainfall amount was low or PFM volume was large, PFM could not store water sufficiently during rainfall, which seriously reduced the maximum saturated moisture content and water absorption performance. Meanwhile, the PFM could not release water in time and replenish the soil water deficit, which increased drought risk. In conclusion, the appropriate volume of PFM and irrigation system may enhance soil water storage capacity, minimize agricultural pollution, and promote crop production. [ABSTRACT FROM AUTHOR]

Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVCUAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVCUAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data. [ABSTRACT FROM AUTHOR]

Introduction: Organic soil amendments such as filter mud (FM) and biochar (BC) can potentially influence the abundance and composition of metabolites. However, our current understanding of the stimulatory effects of FM and BC's long-term impact on stress-regulating metabolites, such as abscisic acid (ABA), jasmonic acid (JA), melatonin, and phenyllactic acid (PLA), and these substrates regulatory effects on disease-causing bacteria in sugarcane ratooning field, which is susceptible to nutrients depletion, diseases, etc., remain poorly understood. Additionally, little is known about how the long-term interaction of these substrates and compounds influences sugarcane ratooning soil enzyme activities, nutrient cycling, and crop growth performance. Methods: To answer these questions, we adopted metabolomics tools combined with high-throughput sequencing to explore the stimulatory effects of the long-term addition of FM and BC on metabolites (e.g., PLA and abscisic aldehyde) and quantify these substrates' regulatory effects on disease-causing bacteria, soil enzyme activities, nutrient cycling, and crop growth performance. Results: The result revealed that ratoon crop weight, stem diameter, sugar content, as well as soil physico-chemical properties, including soil nitrate (NH3 +-N), organic matter (OM), total nitrogen (TN), total carbon (TC), and bglucosidase, marked a significant increase under the BC and FM-amended soils. Whereas soil available potassium (AK), NO3 -N, cellulase activity, and phosphatase peaked under the BC-amended soil, primarily due to the enduring effects of these substrates and metabolites. Furthermore, BC and FM-amended soils enriched specific stress-regulating metabolites, including JA, melatonin, abscisic aldehyde, etc. The sustained effects of both BC and FM-amended soils suppressed disease-causing bacteria, eventually promoting ratooning soil growth conditions. A number of key bioactive compounds had distinct associations with several beneficial bacteria and soil physico-chemical properties. Discussion: This study proves that long-term BC and FM application is one of the eco-friendly strategies to promote ratoon crop growth and soil quality through the enrichment of stress-regulating metabolites and the suppression of diseasecausing bacteria. [ABSTRACT FROM AUTHOR]

Drought is one of the most stressful environments limiting crop growth and yield throughout the world. Therefore, most efforts have been made to document drought-derived genetic and physiological responses and to find better ways to improve drought tolerance. The interaction among them is unclear and/or less investigated. Therefore, the current study is to find a clue of metabolic connectivity among them in rice root experiencing different levels of drought condition. We selected 19 genes directly involved in abscisic acid (ABA) metabolism (6), suberization (6), and aquaporins (AQPs) activity (7) and analyzed the relatively quantitative gene expression using qRT-PCR from rice roots. In addition, we also analyzed proline, chlorophyll, and fatty acids and observed cross-sectional root structure (aerenchyma) and suberin lamella deposition in the endodermis. All drought conditions resulted in an obvious development of aerenchyma and two- to fourfold greater accumulation of proline. The limited water supply (-1.0 and -1.5 MPa) significantly increased gene expression (ABA metabolism, suberization, and AQPs) and developed greater layer of suberin lamella in root endodermis. In addition, the ratio of the unsaturated to the saturated fatty acids was increased, which could be considered as an adjusted cell permeability. Interestingly, these metabolic adaptations were an exception with a severe drought condition (hygroscopic coefficient, -3.1 MPa). Accordingly, we concluded that the drought-tolerant mechanism in rice roots is sophisticatedly regulated until permanent wilting point (-1.5 MPa), and ABA metabolism, suberization, and AQPs activity might be independent and/or concurrent process as a survival strategy against drought. [ABSTRACT FROM AUTHOR]

An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differences in water status among cultivars were already accounted by the measurement of canopy temperature. [ABSTRACT FROM AUTHOR]

Plant phenotyping is a critical field in agriculture, aiming to understand crop growth under specific conditions. Recent research uses images to describe plant characteristics by detecting visual information within organs such as leaves, flowers, stems, and fruits. However, processing data in real field conditions, with challenges such as image blurring and occlusion, requires improvement. This paper proposes a deep learning-based approach for leaf instance segmentation with a local refinement mechanism to enhance performance in cluttered backgrounds. The refinement mechanism employs Gaussian low-pass and High-boost filters to enhance target instances and can be applied to the training or testing dataset. An instance segmentation architecture generates segmented masks and detected areas, facilitating the derivation of phenotypic information, such as leaf count and size. Experimental results on a tomato leaf dataset demonstrate the system's accuracy in segmenting target leaves despite complex backgrounds. The investigation of the refinement mechanism with different kernel sizes reveals that larger kernel sizes benefit the system's ability to generate more leaf instances when using a High-boost filter, while prediction performance decays with larger Gaussian low-pass filter kernel sizes. This research addresses challenges in real greenhouse scenarios and enables automatic recognition of phenotypic data for smart agriculture. The proposed approach has the potential to enhance agricultural practices, ultimately leading to improved crop yields and productivity. [ABSTRACT FROM AUTHOR]

Turmeric (Curcuma longa L.), a significant commercial crop of the Indian subcontinent is widely used as a condiment, natural dye, and as a cure for different ailments. Various bioactive compounds such as turmerones and curcuminoids have been isolated from C. longa that have shown remarkable medicinal activity against various ailments. However, reduced soil fertility, climatic variations, rapid urbanization, and enhanced food demand, pose a multifaceted challenge to the current agricultural practices of C. longa. Plant growth-promoting microbes play a vital role in plant growth and development by regulating primary and secondary metabolite production. Rhizospheric associations are complex species-specific interconnections of different microbiota with a plant that sustain soil health and promote plant growth through nutrient acquisition, nitrogen fixation, phosphate availability, phytohormone production, and antimicrobial activities. An elaborative study of microbiota associated with the roots of C. longa is essential for rhizospheric engineering as there is a huge potential to develop novel products based on microbial consortium formulations and elicitors to improve plant health, stress tolerance, and the production of secondary metabolites such as curcumin. Primarily, the purpose of this review is to implicate the rhizospheric microbial flora as probiotics influencing overall C. longa health, development, and survival for an increase in biomass, enhanced yield of secondary metabolites, and sustainable crop production. [ABSTRACT FROM AUTHOR]

Changes in weather patterns with emerging drought risks and rising global temperature are widespread and negatively affect crop growth and productivity. In nature, plants are simultaneously exposed to multiple biotic and abiotic stresses, but most studies focus on individual stress conditions. However, the simultaneous occurrence of different stresses impacts plant growth and development differently than a single stress. Plants sense the different stress combinations in the same or in different tissues, which could induce specific systemic signalling and acclimation responses; impacting different stress-responsive transcripts, protein abundance and modifications, and metabolites. This mini-review focuses on the combination of drought and heat, two abiotic stress conditions that often occur together. Recent omics studies indicate common or independent regulators involved in heat or drought stress responses. Here, we summarize the current research results, highlight gaps in our knowledge, and flag potential future focus areas. [ABSTRACT FROM AUTHOR]

A phenotyping pipeline utilising DeepLab was developed for precisely estimating the height, volume, coverage and vegetation indices of European and Japanese varieties. Using this pipeline, the effect of varying UAV height on the precise estimation of potato crop growth properties was evaluated. A UAV fitted with a multispectral camera was flown at a height of 15 m and 30 m in an experimental field where various varieties of potatoes were grown. The properties of plant height, volume and NDVI were evaluated and compared with the manually obtained parameters. Strong linear correlations with R2 of 0.803 and 0.745 were obtained between the UAV obtained plant heights and manually estimated plant height when the UAV was flown at 15 m and 30 m respectively. Furthermore, high linear correlations with an R² of 0.839 and 0.754 were obtained between the UAV-estimated volume and manually estimated volume when the UAV was flown at 15 m and 30 m respectively. For the vegetation indices, there were no observable differences in the NDVI values obtained from the UAV flown at the two heights. Furthermore, high linear correlations with R² of 0.930 and 0.931 were obtained between UAV-estimated and manually measured NDVI at 15 m and 30 m respectively. It was found that UAV flown at the lower height had a higher ground sampling distance thus increased resolution leading to more precise estimation of both the height and volume of crops. For vegetation indices, flying the UAV at a higher height had no effect on the precision of NDVI estimates. [ABSTRACT FROM AUTHOR]

Recent developments in deep learning-based automatic weeding systems have shown promise for unmannedweed eradication. However, accurately distinguishing between crops and weeds in varying field conditions remains a challenge for these systems, as performance deteriorates when applied to new or different fields due to insignificant changes in low-level statistics and a significant gap between training and test data distributions. In this study, we propose an approach based on unsupervised domain adaptation to improve crop-weed recognition in new, unseen fields. Our system addresses this issue by learning to ignore insignificant changes in low-level statistics that cause a decline in performance when applied to new data. The proposed network includes a segmentation module that produces segmentation maps using labeled (training field) data while also minimizing entropy using unlabeled (test field) data simultaneously, and a discriminator module that maximizes the confusion between extracted features from the training and test farm samples. This module uses adversarial optimization to make the segmentation network invariant to changes in the field environment. We evaluated the proposed approach on four different unseen (test) fields and found consistent improvements in performance. These results suggest that the proposed approach can effectively handle changes in new field environments during real field inference. [ABSTRACT FROM AUTHOR]

Cotton crop is known to be poorly adapted to waterlogging, especially during the early growth stages. Developing functional relationships between crop growth and development parameters and the duration of waterlogging is essential to develop or improve existing cotton crop models for simulating the impact of waterlogging. However, there are only limited experimental studies conducted on cotton specifically aimed at developing the necessary functional relationships required for waterlogging modeling. Further research is needed to understand the effects of waterlogging on cotton crops and improve modeling capabilities in this area. The current study aimed to conduct waterlogging experiments and develop functional relationships between waterlogging and cotton growth and physiology. The experiments were conducted in pots, and the waterlogging was initiated by plugging the drain hole at the bottom of the pot using a wooden peg. In the experiments, eight waterlogging treatments, including the control treatment, were imposed at the vegetative growth stage (15 days after sowing). Control treatment had zero days of water-logged condition; other treatments had 2, 4, 6, 8, 10, 12, and 14 days of waterlogging. It took five days to reach zero oxygen levels and one to two days to return to control after the treatment. After a total treatment duration of 14 days (30 days after sowing), the growth, physiological, reproductive, and nutrient analysis was conducted. All physiological parameters decreased with the number of days of waterlogging. Flavonoid and anthocyanin index increased with increased duration of waterlogging. Photosynthesis and whole plant dry weight in continuously waterlogged conditions were 75% and 78% less compared to 0, and 2-day water-logged plants. Plant height, stem diameter, number of main stem leaves, leaf area, and leaf length also decreased with waterlogging duration. When waterlogging duration increased, leaf, stem, and root macronutrients decreased, while micronutrients showed mixed trends. Based on the experimental study, functional relationships (linear, quadratic, and exponential decay) and waterlogging stress response indices are developed between growth and development parameters and the duration of waterlogging. This can serve as a base for developing or improving process-based cotton models to simulate the impact of waterlogging. [ABSTRACT FROM AUTHOR]

Timely and accurate prediction of crop yield is essential for increasing crop production, estimating planting insurance, and improving trade benefits. Potato (Solanum tuberosum L.) is a staple food in many parts of the world and improving its yield is necessary to ensure food security and promote related industries. We conducted a comprehensive literature survey to demonstrate methodological evolution of predicting potato yield. Publications on predicting potato yield based on methods of remote sensing (RS), crop growth model (CGM), and yield limiting factor (LF) were reviewed. RS, especially satellite-based RS, is crucial in potato yield prediction and decision support over large farm areas. In contrast, CGM are often utilized to optimize management measures and address climate change. Currently, combined with the advantages of low cost and easy operation, unmanned aerial vehicle (UAV) RS combined with artificial intelligence (AI) show superior potential for predicting potato yield in precision management of large-scale farms. However, studies on potato yield prediction are still limited in the number of varieties and field sample size. In the future, it is critical to employ time-series data from multiple sources for a wider range of varieties and large field sample sizes. This study aims to provide a comprehensive review of the progress in potato yield prediction studies and to provide a theoretical reference for related research on potato. [ABSTRACT FROM AUTHOR]

Salt stress affects large cultivated areas worldwide, thus causing remarkable reductions in plant growth and yield. To reduce the negative effects of salt stress on plant growth and yield, plant hormones, nutrient absorption, and utilization, as well as developing salt-tolerant varieties and enhancing their morphophysiological activities, are some integrative approaches to coping with the increasing incidence of salt stress. Numerous studies have been conducted to investigate the critical impacts of these integrative approaches on plant growth and yield. However, a comprehensive review of these integrative approaches, that regulate plant growth and yield under salt stress, is still in its early stages. The review focused on the major issues of nutrient absorption and utilization by plants, as well as the development of salt tolerance varieties under salt stress. In addition, we explained the effects of these integrative approaches on the crop's growth and yield, illustrated the roles that phytohormones play in improving morpho-physiological activities, and identified some relevant genes involve in these integrative approaches when the plant is subjected to salt stress. The current review demonstrated that HA with K enhance plant morphophysiological activities and soil properties. In addition, NRT and NPF genes family enhance nutrients uptake, NHX1, SOS1, TaNHX, AtNHX1, KDML, RD6, and SKC1, maintain ion homeostasis and membrane integrity to cope with the adverse effects of salt stress, and sd1/Rht1, AtNHX1, BnaMAX1s, ipal-1D, and sft improve the plant growth and yield in different plants. The primary purpose of this investigation is to provide a comprehensive review of the performance of various strategies under salt stress, which might assist in further interpreting the mechanisms that plants use to regulate plant growth and yield under salt stress. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the main causes of land degradation in arid and semiarid areas. Timely and accurate monitoring of soil salinity in different areas is a prerequisite for amelioration. Hyperspectral technology has been widely used in soil salinity monitoring due to its high efficiency and rapidity. However, vegetation cover is an inevitable interference in the direct acquisition of soil spectra during crop growth period, which greatly limits the monitoring of soil salinity by remote sensing. Due to high soil salinity could lead to difficulty in plants' water absorption, and inhibit plant dry matter accumulation, a method for monitoring root zone soil salinity by combining vegetation canopy spectral information and crop aboveground growth parameters was proposed in this study. The canopy spectral information was acquired by a spectroradiometer, and then variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and random frog algorithm (RFA) were used to extract the salinity spectral features in cotton canopy spectrum. The extracted features were then used to estimate root zone soil salinity in cotton field by combining with cotton plant height, aboveground biomass, and shoot water content. The results showed that there was a negative correlation between plant height/aboveground biomass/shoot water content and soil salinity in 0-20, 0-40, and 0-60 cm soil layers at different growth stages of cotton. Spectral feature selection by the three methods all improved the prediction accuracy of soil salinity, especially CARS. The prediction accuracy based on the combination of spectral features and cotton growth parameters was significantly higher than that based on only spectral features, with R² increasing by 10.01%, 18.35%, and 29.90% for the 0-20, 0-40, and 0-60 cm soil layer, respectively. The model constructed based on the first derivative spectral preprocessing, spectral feature selection by CARS, cotton plant height, and shoot water content had the highest accuracy for each soil layer, with R² of 0.715,0.769, and 0.742 for the 0-20, 0-40,0-60 cm soil layer, respectively. Therefore, the method by combining cotton canopy hyperspectral data and plant growth parameters could significantly improve the prediction accuracy of root zone soil salinity under vegetation cover conditions. This is of great significance for the amelioration of saline soil in salinized farmlands arid areas. [ABSTRACT FROM AUTHOR]

Introduction: Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies. Methods: Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values. Results: We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation. Discussion: A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation. [ABSTRACT FROM AUTHOR]

Remotely estimating leaf phosphorus concentration (LPC) is crucial for fertilization management, crop growth monitoring, and the development of precision agricultural strategy. This study aimed to explore the best prediction model for the LPC of rice (Oryza sativa L.) using machine learning algorithms fed with full-band (OR), spectral indices (SIs), and wavelet features. To obtain the LPC and leaf spectra reflectance, the pot experiments with four phosphorus (P) treatments and two rice cultivars were carried out in a greenhouse in 2020-2021. The results indicated that P deficiency increased leaf reflectance in the visible region (350-750 nm) and decreased the reflectance in the near-infrared (NIR, 750-1350 nm) regions compared to the P-sufficient treatment. Difference spectral index (DSI) composed of 1080 nm and 1070 nm showed the best performance for LPC estimation in calibration (R² = 0.54) and validation (R² = 0.55). To filter and denoise spectral data effectively, continuous wavelet transform (CWT) of the original spectrum was used to improve the accuracy of prediction. The model based on Mexican Hat (Mexh) wavelet function (1680 nm, Scale 6) demonstrated the best performance with the calibration R² of 0.58, validation R² of 0.56 and RMSE of 0.61 mg g-1. In machine learning, random forest (RF) had the best model accuracy in OR, SIs, CWT, and SIs + CWT compared with other four algorithms. The SIs and CWT coupling with the RF algorithm had the best results of model validation, the R² was 0.73 and the RMSE was 0.50 mg g-1, followed by CWT (R² = 0.71, RMSE = 0.51 mg g-1), OR (R² = 0.66, RMSE = 0.60 mg g-1), and SIs (R² = 0.57, RMSE = 0.64 mg g-1). Compared with the best performing SIs based on the linear regression models, the RF algorithm combining SIs and CWT improved the prediction of LPC with R² increased by 32%. Our results provide a valuable reference for spectral monitoring of rice LPC under different soil P-supplying levels in a large scale. [ABSTRACT FROM AUTHOR]

Drought stress is a major environmental threat that limits plant growth and crop productivity. Therefore, it is necessary to uncover the molecular mechanisms behind drought tolerance in crops. Here, OsWRKY76 positively regulated drought stress in rice. OsWRKY76 expression was induced by PEG treatment, dehydration stress, and exogenous MeJA rather than by no treatment. Notably, OsWRKY76 knockout weakened drought tolerance at the seedling stage and decreased MeJA sensitivity. OsJAZ12 was significantly induced by drought stress, and its expression was significantly higher in OsWRKY76-knockout mutants than in wild-type ZH11 under drought stress. Yeast two-hybrid and bimolecular fluorescence complementation assays showed that OsWRKY76 interacted with OsJAZ12. OsWRKY76 weakened the interaction between OsbHLH148 and OsJAZ12 in yeast cells. The OsJAZ12 protein repressed the transactivation activity of OsbHLH148, and this repression was partly restored by OsWRKY76 in rice protoplasts. Moreover, OsDREB1E expression was lower in OsWRKY76- knockout mutants than in wild-type ZH11 under drought stress, but it was upregulated under normal growth conditions. Yeast one-hybrid, electrophoretic mobility shift, and dual-luciferase assays showed that OsWRKY76 and OsbHLH148 bound directly to the OsDREB1E promoter and activated OsDREB1E expression in response to drought stress. These results suggest that OsWRKY76 confers drought tolerance through OsbHLH148- mediated jasmonate signaling in rice, offering a new clue to uncover the mechanisms behind drought tolerance. [ABSTRACT FROM AUTHOR]

Roots are sensors evolved to simultaneously respond to manifold signals, which allow the plant to survive. Root growth responses, including the modulation of directional root growth, were shown to be differently regulated when the root is exposed to a combination of exogenous stimuli compared to an individual stress trigger. Several studies pointed especially to the impact of the negative phototropic response of roots, which interferes with the adaptation of directional root growth upon additional gravitropic, halotropic or mechanical triggers. This review will provide a general overview of known cellular, molecular and signalling mechanisms involved in directional root growth regulation upon exogenous stimuli. Furthermore, we summarise recent experimental approaches to dissect which root growth responses are regulated upon which individual trigger. Finally, we provide a general overview of how to implement the knowledge gained to improve plant breeding. [ABSTRACT FROM AUTHOR]

Accurate and timely nitrogen (N) scheduling requires knowledge of in-season crop N deficit. Therefore, understanding the association between crop growth and crop N demand during its growth period is imperative for fine-tuning N scheduling decisions to actual crop N demand and to enhance N use efficiency. The concept of the critical N dilution curve has been employed to assess and quantify the intensity and time of crop N deficit. However, research regarding the association between crop N deficit and N use efficiency in wheat is limited. The present study was carried out to determine whether there are relationships between the accumulated nitrogen deficit (Nand) and agronomic N use efficiency (AEN) as well as with its components (N fertilizer recovery efficiency (REN) and N fertilizer physiological efficiency (PEN)) of winter wheat and to explore the potential capacity of Nand for predicting AEN and its components. Data acquired from five variable N rates (0, 75, 150, 225, and 300 kg ha-1) field experiments using six winter wheat cultivars were used to establish and validate the relationships between Nand and AEN, REN, and PEN. The results indicated that plant N concentration in winter wheat was significantly affected by N application rates. Nand varied from -65.73 to 104.37 kg ha-1 after Feekes stage 6 under diffeRENt N application rates. The AEN and its components were also affected by cultivars, N levels, seasons, and growth stages. A positive correlation was observed between Nand, AEN, and its components. Validation using an indePENdent data set showed the robustness of the newly developed empirical models to accurately predict AEN, REN, and PEN with an RMSE of 3.43 kg kg-1, 4.22%, and 3.67 kg kg-1 and RRMSE of 17.53%, 12.46%, and 13.17%, respectively. This indicates that Nand has the potential to predict AEN and its components during the growth period of winter wheat. The findings will assist in improving inseason N use efficiency by fine-tuning N scheduling decisions in winter wheat cultivation. [ABSTRACT FROM AUTHOR]

Accurate and efficient crop classification using remotely sensed data can provide fundamental and important information for crop yield estimation. Existing crop classification approaches are usually designed to be strong in some specific scenarios but not for multi-scenario crop classification. In this study, we proposed a new deep learning approach for multi-scenario crop classification, named Cropformer. Cropformer can extract global features and local features, to solve the problem that current crop classification methods extract a single feature. Specifically, Cropformer is a two-step classification approach, where the first step is self-supervised pre-training to accumulate knowledge of crop growth, and the second step is a fine-tuned supervised classification based on the weights from the first step. The unlabeled time series and the labeled time series are used as input for the first and second steps respectively. Multi-scenario crop classification experiments including full-season crop classification, inseason crop classification, few-sample crop classification, and transfer of classification models were conducted in five study areas with complex crop types and compared with several existing competitive approaches. Experimental results showed that Cropformer can not only obtain a very significant accuracy advantage in crop classification, but also can obtain higher accuracy with fewer samples. Compared to other approaches, the classification performance of Cropformer during model transfer and the efficiency of the classification were outstanding. The results showed that Cropformer could build up a priori knowledge using unlabeled data and learn generalized features using labeled data, making it applicable to crop classification in multiple scenarios. [ABSTRACT FROM AUTHOR]

The resource allocation of different component organs of crops under drought stress is a strategy for the coordinated growth of crops, which also reflects the adaptability of crops to drought condition. In this study, maize variety namely 'Denghai 618', under the ventilation shed, two treatment groups of light drought (LD) and moderate drought (MD), and the same rehydration after drought are set, as well as the normal water supply for control in shed (CS). The drought experiment was conducted in the jointing-tasseling stage in 2021. The effects of different drought stress on the water content and biomass allocation of each component organ were analyzed. The results showed that (1) during the drought period, the water content of each component organ of summer maize decreased in general, but the Water content distribution ratio (WCDR) of the root increased by 1.83%-2.35%. The WCDR of stem increased by 0.52%- 1.40%. (2) Under different drought treatments, the root biomass (RB) increased 33.94% ~ 46.09%, and fruit biomass (FB) increased 1.46% ~ 2.49%, the leaf biomass (LB) decreased by 8.2% and 1.46% respectively under LD and MD. (3) The allometric growth model constructed under sufficient water is not suitable for drought stress; the allometric exponent α under drought stress is lower than that of the CS: CS (α=1.175) > MD (α = 1.136) > LD (α = 1.048), which also indicates that the impact of existing climate change on grain yield may be underestimated. This study is helpful to understand the adaptive strategies of the coordinated growth of maize component organs under drought stress and provide a reference for the prediction of grain yield under climate change. [ABSTRACT FROM AUTHOR]

Lentil, an important cool season food legume, is a rich source of easily digestible protein, folic acid, bio-available iron, and zinc nutrients. Lentil grows mainly as a sole crop in the winter after harvesting rice in South Asia. However, the annual productivity is low due to its slow growth during the early phase, competitive weed infestation, and disease outbreaks during the crop growth period. Disease resistance breeding has been practiced for a long time to enhance resistance to various diseases. Often the sources of resistance are available inwild crop relatives. Thus, wide hybridization and the ovule rescue technique have helped to introgress the resistance trait into cultivated lentils. Besides hybridization, induced mutagenesis contributed immensely in creating variability for disease tolerance, and several disease-resistant mutant lines have been developed. However, to overcome the limitations of traditional breeding approaches, advancement in molecular marker technologies, and genomics has helped to develop diseaseresistant and climate-resilient lentil varieties with more precision and efficiency. This review describes types of diseases, disease screening methods, the role of conventional and new breeding technologies in alleviating disease-incurred damage and progress toward making lentil varieties more resilient to disease outbreaks under the shadow of climate change. [ABSTRACT FROM AUTHOR]

The microbial diseases cause significant damage in agriculture, resulting in major yield and quality losses. To control microbiological damage and promote plant growth, a number of chemical control agents such as pesticides, herbicides, and insecticides are available. However, the rising prevalence of chemical control agents has led to unintended consequences for agricultural quality, environmental devastation, and human health. Chemical agents are not naturally broken down by microbes and can be found in the soil and environment long after natural decomposition has occurred. As an alternative to chemical agents, biocontrol agents are employed to manage phytopathogens. Interest in lactic acid bacteria (LAB) research as another class of potentially useful bacteria against phytopathogens has increased in recent years. Due to the high level of biosafety, they possess and the processes they employ to stimulate plant growth, LAB is increasingly being recognized as a viable option. This paper will review the available information on the antagonistic and plant-promoting capabilities of LAB and its mechanisms of action as well as its limitation as BCA. This review aimed at underlining the benefits and inputs from LAB as potential alternatives to chemical usage in sustaining crop productivity. [ABSTRACT FROM AUTHOR]

Saline stress is a significant factor that caused crop growth inhibition and yield decline. SHORT INTERNODES/STYLISH (SHI/STY) and SHI-RELATED SEQUENCE (SRS) transcription factors are specific to plants and share a conserved RING-like zinc-finger domain (CX2CX7CX4CX2C2X6C). However, the functions of SHI/STY and SRS genes in cotton responses to salt stress remain unclear. In this study, 26 GhSRSs were identified in Gossypium hirsutum, which further divided into three subgroups. Phylogenetic analysis of 88 SRSs from8 plant species revealed independent evolutionary pattern in some of SRSs derived from monocots. Conserved domain and subcellular location predication of GhSRSs suggested all of them only contained the conserved RING-like zinc-finger domain (DUF702) domain and belonged to nucleus-localized transcription factors except for the GhSRS22. Furthermore, synteny analysis showed structural variation on chromosomes during the process of cotton polyploidization. Subsequently, expression patterns of GhSRS family members in response to salt and drought stress were analyzed in G. hirsutum and identified a salt stressinducible gene GhSRS21. The GhSRS21 was proved to localize in the nuclear and silencing it in G. hirsutum increased the cotton resistance to salt using the virusinduced gene silencing (VIGS) system. Finally, our transcriptomic data revealed that GhSRS21 negatively controlled cotton salt tolerance by regulating the balance between ROS production and scavenging. These results will increase our understanding of the SRS gene family in cotton and provide the candidate resistant gene for cotton breeding. [ABSTRACT FROM AUTHOR]