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Diseases of tomato leaves can seriously damage crop yield and financial rewards. The timely and accurate detection of tomato diseases is a major challenge in agriculture. Hence, the early and accurate diagnosis of tomato diseases is crucial. The emergence of deep learning has dramatically helped in plant disease detection. However, the accuracy of deep learning models largely depends on the quantity and quality of training data. To solve the inter-class imbalance problem and improve the generalization ability of the classification model, this paper proposes a cycle-consistent generative-adversarial-network-based Transformer model to generate diseased tomato leaf images for data augmentation. In addition, this paper uses a Transformer model and densely connected CNN architecture to extract multilevel local features. The Transformer module is utilized to capture global dependencies and contextual information accurately to expand the sensory field of the model. Experiments show that the proposed model achieved 99.45% accuracy on the PlantVillage dataset. The 2018 Artificial Intelligence Challenger dataset and the private dataset attained accuracies of 98.30% and 95.4%, and the proposed classification model achieved a higher accuracy and smaller model size compared to previous deep learning models. The classification model is generalizable and robust and can provide a stable theoretical framework for crop disease prevention and control. [ABSTRACT FROM AUTHOR]

The accessible literature covered in this paper commonly highlights psyllids as a significant group of insects affecting pear trees, posing a continual challenge for commercial orchards. With the development of modern pear cultivation systems, Cacopsylla pyri Linnaeus 1758 (Hemiptera: Psyllidae) has emerged as a major pest in pear orchards across many European countries, including those in the Western Balkans. For years, the agricultural sector has primarily relied on chemical insecticides to control pear psyllas, but these methods often fail to produce satisfactory results. This is largely due to C. pyri's rapid development of resistance to chemical treatments. Consequently, modern agriculture is increasingly shifting towards biological methods to manage C. pyri, involving the identification and conservation of its natural enemies. Although there is an abundance of research on the natural predators of C. pyri and their biocontrol applications across the globe, the Western Balkan region has conducted relatively few studies on the subject. Globally, various parasitoids, predators, and entomopathogenic fungi are often cited as effective against C. pyri. Specific species registered in the agroecological conditions of the Western Balkans include parasitic wasps such as Trechnites insidiosus Crawford, 1910 (Hymenoptera: Encyrtidae) and Prionomitus mitratus Dalman, 1820 (Hymenoptera: Encyrtidae), as well as the predatory bug Anthocoris nemoralis Fabricius, 1794 (Hemiptera: Anthocoridae). However, most Balkan countries have yet to fully utilise the potential of beneficial entomofauna or develop strategies for their commercial application at a national level. Considering that C. pyri is a major pest in pear cultivation and its natural enemies have not been thoroughly explored in most of the Western Balkans, this paper aims to review the literature data on available natural enemies of pear psyllas and to highlight and promote their undeniable potential in biological control. [ABSTRACT FROM AUTHOR]

The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model's learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model's ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities. [ABSTRACT FROM AUTHOR]

This review paper addresses the importance of increasing agrobiodiversity to cope with climate change and, at the same time, providing a sufficient amount of healthy food. This is in agreement with the messages from ecology and medicine indicating the advantages of biodiversity in general and agrobiodiversity in particular for the planet and for our health. Plant breeding is considered to be one of the causes of the decline in agrobiodiversity, and therefore, this paper illustrates alternatives to the commonly used approach based on centralized selection. The first alternative is decentralized participatory breeding, which adapts crops to both different agronomic environments and client preferences, representing an "option by context" model of research. The second alternative is evolutionary breeding, which is a more dynamic strategy than participatory plant breeding because it merges the advantages of decentralization with the ability of dynamic mixtures and evolutionary populations to cope with biotic and abiotic stresses and evolve, thus adapting to climate change and to the associated changes in the spectrum of pests. A crop capable of evolving as the environment around it evolves appears to be the most ideal way of responding to climate change and increasing agricultural biodiversity. [ABSTRACT FROM AUTHOR]

This study assesses the status of hydrothermal carbonization (HTC) technology and identifies barriers hindering its commercial viability. Conducting a global survey among HTC companies (with a total of 24 surveys sent), the research evaluates the current landscape, challenges, and future prospects of large-scale HTC operations. Furthermore, it presents a detailed global inventory of existing HTC facilities, illustrating geographical distribution and trends in application. Most of the companies are located in Europe, followed by Asia and North America. With substantial participation from HTC companies, exceeding 62% in the survey (15 companies), the study provides a comprehensive overview of diverse companies, their business models, regulatory challenges, and the overall state of HTC technology. The majority of companies in this study, approximately 80%, offer services in the field of waste management. This paper also explores the potential of HTC in transforming waste management practices, carbon sequestration methodologies, and the development of new materials. Employing a thorough SWOT analysis, the paper advocates for a broader adoption of HTC, emphasizing its transformative capacity in fostering sustainable management of urban, industrial, and agricultural residues, promoting circular economy principles, mitigating climate change, and offering a robust foundation for informed decision-making and sustainable development strategies. [ABSTRACT FROM AUTHOR]

Weeds compete with crops for water and nutrients and can adversely affect crop growth and yield, so it is important to research effective weed control methods. This paper provides an overview of the impact of weeds on crop yield and describes the current state of research on weed management in field herbaceous crops. Physical weed control mainly refers to thermal technologies represented by flame weed control and laser weed control, which can efficiently and accurately remove weeds. Mechanical weed control requires a combination of sensor technologies, machine vision technology, and high-precision navigation to improve weed control accuracy. Biological weed control relies heavily on plant extracts and pathogens to create herbicides, but it is costly, and some can be toxic to mammals. Chemical weed control is a common method, resulting in environmental pollution and weed resistance. To reduce the use of chemical herbicides, scholars have proposed integrated weed management strategies, which combine biological control, control of the seed bank, and improve crop competitiveness. Integrated weed management strategies are considered to be the future direction of weed management. In conclusion, physical, mechanical, biological, and chemical weed control methods are commonly used in weed management. Each method has its applicable scenarios, and the implementation of integrated weed management strategies can lead to better weed control, improving crop yield and quality. The main objective of this review is to organize the research progress on weed management methods for herbaceous crops in the field and to provide a reference for the agricultural sector to develop weed control strategies. Specifically, this paper categorizes weed management methods into four groups, discusses and presents the advantages and disadvantages of the aforementioned weed control methods, and discusses future research directions. [ABSTRACT FROM AUTHOR]

The precise estimation of sugarcane yield at the field scale is urgently required for harvest planning and policy-oriented management. Sugarcane yield estimation from satellite remote sensing is available, but satellite image acquisition is affected by adverse weather conditions, which limits the applicability at the field scale. Secondly, existing approaches from remote sensing data using vegetation parameters such as NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) have several limitations. In the case of sugarcane, crop yield is actually the weight of crop stalks in a unit of acreage. However, NDVI's over-saturation during the vigorous growth period of crops results in significant limitations for sugarcane yield estimation using NDVI. A new sugarcane yield estimation is explored in this paper, which employs allometric variables indicating stalk magnitude (especially stalk height and density) rather than vegetation parameters indicating the leaf quantity of the crop. In this paper, UAV images with RGB bands were processed to create mosaic images of sugarcane fields and estimate allometric variables. Allometric equations were established using field sampling data to estimate sugarcane stalk height, diameter, and weight. Additionally, a stalk density estimation model at the pixel scale of the plot was created using visible light vegetation indices from the UAV images and ground survey data. The optimal stalk density estimation model was applied to estimate the number of plants at the pixel scale of the plot in this study. Then, the retrieved height, diameter, and density of sugarcane in the fields were combined with stalk weight data to create a model for estimating the sugarcane yield per plot. A separate dataset was used to validate the accuracy of the yield estimation. It was found that the approach presented in this study provided very accurate estimates of sugarcane yield. The average yield in the field was 93.83 Mg ha−1, slightly higher than the sampling yield. The root mean square error of the estimation was 6.63 Mg ha−1, which was 5.18% higher than the actual sampling yield. This study offers an alternative approach for precise sugarcane yield estimation at the field scale. [ABSTRACT FROM AUTHOR]

To control heavy metal pollution effectively, prevention of heavy metal accumulations in agricultural soils should be the priority rather than remediation of heavy metal contamination. In this research, papers which contained input and output fluxes of Cd, As, Cr, Hg, and Pb in topsoil (the plough layer) of agricultural lands in Hunan, Zhejiang, the Yangtze River Delta, Hainan, and China as a whole were explored. Fluxes of heavy metal species were recalculated, and future trends of pollution were predicted. Also, cases regarding the application of technologies to control the input and output of heavy metals were analyzed. Results indicated that atmospheric decomposition was the dominant input source of heavy metals in all study sites except Hainan. The relative contributions of fertilizers, irrigation water, and straw returning fluctuated greatly among different sites. Cd pollution in all sites was the most serious, followed by Cr and Pb. In Hunan, Cd has already exceeded the maximum limit value and needs to be controlled urgently. The input of heavy metals from irrigation water, fertilizers, and straw returning could be controlled by proposing more policies to manage their quality and application amounts. The amount of heavy metals absorbed by plants could be increased by cultivating crops with hyperaccumulators and high-biomass plants. [ABSTRACT FROM AUTHOR]

As arid lands expand across the globe, scholars increasingly turn to the archaeological record for examples of sustainable farming in extreme environments. The arid north coast of Peru was the setting of early and intensive irrigation-based farming; it is also periodically impacted by sudden, heavy rainfall related to the El Niño Southern Oscillation (ENSO) phenomenon. While the sociopolitical effects, technologies, and engineering expertise of these irrigation systems have been thoroughly examined and theorized, little is known about how farmers managed periods of water stress. The aim of this study is to test whether arid zone farming was supported by hybrid, intermittent flood and perennial water source systems in the prehispanic past. An arroyo in the Chicama Valley was selected for preliminary data collection, and these data are presented here: (1) drone photography of the arroyo capturing the aftermath of a recent (2023) rain event; and (2) potassium (K) soil test kit results from samples collected near suspected prehispanic check dam features in the same area. The paper combines these data with comparative examples from the literature to suggest that the prehispanic features functioned as water-harvesting infrastructure. The paper concludes that sustainable, arid zone farming can be supported by hybrid, intermittent flood and perennial water source systems. [ABSTRACT FROM AUTHOR]

As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this "land gap" and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle. [ABSTRACT FROM AUTHOR]

To address the lack of specialized machinery for the mechanical weeding of SCSCP in the Huang Huai Hai region, this study designs a mechanized inter-row weeding machine for SCSCP. The machine features a reciprocating weeding shovel and an adaptive contouring mechanism for cultivation and soil loosening. This paper details the machine's principles by analyzing the geometric relationship and mechanical model between the corresponding profiling quantities, which determine the relevant parameters for adaptive contouring to ensure stable operation on undulating ground. Furthermore, by optimizing the design of the weeding shovel's reciprocating motion mechanism, combining EDEM simulation with the weeding shovel–soil interaction, it has been determined that, at various PTO shaft speeds, the optimal weeding efficacy is achieved with a blade-type weeding shovel structure when operating at a forward speed of 3.5 km/h. Field experiments were conducted with different PTO shaft speeds and weeding depths, using weeding and seedling injury rates as performance indicators. The results showed that, based on the optimal speed, the PTO shaft speed is 760 r/min, the operating depth is 3–5 cm, and the average row weeding rate is 90.4%. The average soybean and corn seedling injury rate is 3.4% and 4.2%, meeting the technical requirements for mechanical weeding. [ABSTRACT FROM AUTHOR]

At present, the potato's mechanized harvesting rate in hilly and mountainous areas is very low. The reasons for this are that in heavy soil, the separation of potato rhizomes from soil or vines is not sufficient, harvesting machinery is seriously damaged by the potato epidermis, and the harvested potato is easily buried in soil, resulting in a missed harvest. In this paper, a two-stage cleaning potato harvester with wave-type and roller-group-type separating mechanisms was designed, and its overall structure and working principle are introduced in detail. The new cleaning mechanism can increase the effective separating length and effective contact area of the potato–soil mixture so as to achieve the purpose of removing clay and heavy soil. The main separator uses a structure that combines offset waves with opposite waves and a staggered arrangement of large–small diameter straight bars. The secondary separator adopts a device combining left-hand and right-hand separating rollers. The discrete element model of the whole machine was established, and the results of the theoretical analysis were verified by simulation. The key factors affecting the harvest quality were analyzed by variance analysis and response surface analysis, and the field experiment was carried out with the rate of clean potatoes, damaged potatoes, and peeled potatoes harvested as the indexes. The field experiments showed that the machine achieved a rate of photos on or out of the earth of 98.87%, a damaged potato rate of 0.91%, and a peeled potato rate of 1.13%. The research results provide theoretical support and a technical reference for the design and optimization of potato harvesters, as well as the improvement of the potato–soil separating efficiency and harvest quality. [ABSTRACT FROM AUTHOR]

The improper treatment of crop straw not only leads to resource wastage but also adversely impacts the ecological environment. However, the application of microorganisms can accelerate the decomposition of crop straw and improve its utilization. In this study, cellulose-degrading microbial strains were isolated from naturally decayed corn straw and screened using Congo red staining, along with assessing variations in carboxymethyl cellulase (CMCase) activity, filter paper enzyme (FPase) activity and β-glucosidase (β-Gase) activity, as well as the degradation rate. The eight strains, namely Neurospora intermedia isolate 29 (A1), Streptomyces isolate FFJC33 (A2), Gibberella moniliformis isolate FKCB-009 (A3), Fusarium fujikuroi isolate EFS3(2) (A4), Fusarium Fujikuroi isolate FZ04 (A5), Lysine bacillus macroides strain LNHL43 (B1), Bacillus subtilis strain MPF30 (B2) and Paenibacilli lautus strain ALEB-P1 (C), were identified and selected for microbial strain consortium design based on their high activities of CMCase, FPase and β-Gase. The fungi, bacteria and actinomycete strains were combined without antagonistic effects on corn straw decomposition. The results showed the A2B2 combination had a significantly higher FPase at 55.44 U/mL and β-Gase at 25.73 U/mL than the other two strain combinations (p < 0.05). Additionally, the degradation rate of this combination was 40.33%, which was considerably higher than that of the other strains/consortia. The strain combination A4B2C also had superior enzyme activity, including CMCase with a value of 35.03 U/mL, FPase with a value of 63.59 U/mL and β-Gase with a value of 26.15 U/mL, which were significantly different to those of the other three strain combinations (p < 0.05). Furthermore, seven single microbial strains with high cellulase activities were selected to construct various microbial consortiums for in situ composting in order to evaluate their potential. Taken as a whole, the results of composting, including temperature, moisture content, pH, E4/E6 value and seed germination index, indicated that the microbial strain consortium consisting of Neurospora intermediate isolate 29, Fusarium fujikuroi isolate EFS3(2), Fusarium fujikuroi isolate FZ04, Lysinibacillus macrolides, Lysinibacillus sphaericus, Bacillus subtilis and Paenibacillus lautus was advantageous for corn straw decomposition and yielded high-quality compost. The screened flora was able to effectively degrade corn straw. This study provides a novel solution for the construction of a microbial consortium for the composting of corn straw. [ABSTRACT FROM AUTHOR]

The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient deficiency conditions—sulfur deficiency and phosphorus deficiency—were collected, totaling 11,364 images and 10 high-definition videos captured by a 4K drone. The data sets were augmented threefold using techniques such as flipping and gamma adjustment to construct a disease data set. Building upon the YOLOv8 framework, the EMA attention mechanism and Focal loss function were added to optimize the model, addressing the complex backgrounds and imbalanced positive and negative samples present in the sugarcane data set. Disease detection models EF-yolov8s, EF-yolov8m, EF-yolov8n, EF-yolov7, and EF-yolov5n were constructed and compared. Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. The experimental results demonstrate that our improved EF-yolov8s model outperforms other models, achieving mAP_0.5, precision, recall, and F1 scores of 89.70%, 88.70%, 86.00%, and 88.00%, respectively, highlighting the effectiveness of EF-yolov8s for sugarcane disease detection. Additionally, yolov8s-seg achieves an average precision of 80.30% with a smaller number of parameters, outperforming other models by 5.2%, 1.9%, 2.02%, and 0.92% in terms of mAP_0.5, respectively, effectively detecting nutrient deficiency symptoms and addressing the challenges of sugarcane growth monitoring and disease detection in complex environments using computer vision technology. [ABSTRACT FROM AUTHOR]

Soil salinity assessment methods based on remote sensing data are a common topic of scientific research. However, the developed methods, as a rule, estimate relatively small areas of the land surface at certain moments of the season, tied to the timing of ground surveys. Considerable variability of weather conditions and the state of the earth surface makes it difficult to assess the salinity level with the help of remote sensing data and to verify it within a year. At the same time, the assessment of salinity on the basis of multiyear data allows reducing the level of seasonal fluctuations to a considerable extent and revealing the statistically stable characteristics of cultivated areas of land surface. Such an approach allows, in our opinion, the processes of mapping the salinity of large areas of cultivated lands to be automated considerably. The authors propose an approach to assess the salinization of cultivated and non-cultivated soils of arid zones on the basis of long-term averaged values of vegetation indices and salinity indices. This approach allows revealing the consistent relationships between the characteristics of spectral indices and salinization parameters. Based on this approach, this paper presents a mapping method including the use of multiyear data and machine learning algorithms to classify soil salinity levels in one of the regions of South Kazakhstan. Verification of the method was carried out by comparing the obtained salinity assessment with the expert data and the results of laboratory tests of soil samples. The percentage of "gross" errors of the method, in other words, errors when the predicted salinity class differs by more than one position compared to the actual one, is 22–28% (accuracy is 0.78–0.72). The obtained results allow recommending the developed method for the assessment of long-term trends of secondary salinization of irrigated arable land in arid areas. [ABSTRACT FROM AUTHOR]

With the growth of the population and the development of modern industry and the economy, the problem of heavy metal pollution in cultivated soil has become increasingly prominent. Moreover, heavy metal poses a serious threat to plant growth due to its characteristics of difficult degradation, high mobility, easy enrichment, and potential toxicity and has become a social topic. Melatonin is a new type of plant hormone widely present in animals, plants, fungi, and bacteria, and its biological role has begun investigated in the last dozen years. Facing heavy metal stress, melatonin can play a pleiotropic role in the physiological processes of plants, such as stress resistance and growth regulation, mitigate the damage caused by stress on plants, and provide a new research idea for alleviating heavy metal stress in plants. From the aspects of the plant phenotype, physiology, element absorption, and molecular structure, this paper, therefore, mainly reviews the effects of melatonin on plants subjected to heavy metal stress and the mechanism of melatonin alleviating heavy metal stress and then puts forward future research directions. This information may be of great significance to the normal growth of crops under heavy metal stress and will provide an important theoretical basis for the genetic improvement of crop resistance in the future. [ABSTRACT FROM AUTHOR]

Soil texture is a significant attribute of soil properties. Obtaining insight into the soil texture is beneficial when making agricultural decisions during production. Nevertheless, assessing the soil texture in specific laboratory conditions entails substantial dedication, which is time-consuming and includes a high cost. In this paper, we propose a soil texture detection network by embedding the frequency channel attention network and a texture encoding network into the representation learning paradigm of the ResNet framework. Concretely, the former is reliable in exploiting the feature correlations among multi-frequency, while the latter focuses on encoding feature variables, jointly enhancing the ability of feature expression. Meanwhile, the clay, silt, and sand particles present in the soil are exported through a ResNet18 fully linked layer. Experimental results show that the correlation coefficient for predicting clay, silt, and sand content are 0.931, 0.936, and 0.957, respectively. For the root mean square error, the quantitative scores are 2.106%, 3.390%, and 3.602%, respectively. The proposed network also exhibits proposing generalization capability, yielding quite considerable results on different soil samples. Notably, the detection results are almost in agreement with the conventional laboratory measurements, and, at the same time, outperform other competitors, making it highly attractive for practical applications. [ABSTRACT FROM AUTHOR]

To address the real-time detection challenge of deploying deep learning-based tomato leaf disease detection algorithms on embedded devices, an improved tomato leaf disease detection algorithm based on YOLOv8n is proposed in this paper. It is able to achieve the efficient, real-time detection of tomato leaf diseases while maintaining model's lightweight requirements. The algorithm incorporated the LMSM (lightweight multi-scale module) and ALSA (Attention Lightweight Subsampling Module) to improve the ability to extract lightweight and multi-scale semantic information for the specific characteristics of tomato leaf disease, which include irregular spot size and lush tomato leaves. The head network was redesigned utilizing partial and group convolution along with a parameter-sharing method. Scalable auxiliary bounding box and loss function optimization strategies were introduced to further enhance performance. After undergoing the pruning technique, computation decreased by 61.7%, the model size decreased by 55.6%, and the FPS increased by 44.8%, all while a high level of accuracy was maintained. A detection speed of 19.70FPS on the Jetson Nano was obtained after undergoing TensorRT quantization, showing a 64.85% improvement compared to the initial detection speed. This method met the high real-time performance and small model size requirements for embedded tomato leaf disease detection systems, indirectly reducing the energy consumption of online detection. It provided an effective solution for the online detection of tomato leaf disease. [ABSTRACT FROM AUTHOR]

The spatiotemporal continuity characteristics of drought are the basis for analyzing its spatial migration and evolution, which is significant for mitigation and early warning of drought. The aim of this paper is to identify meteorological and vegetation drought events in China from 1982 to 2022, reveal the dynamic changes of typical drought events, and elucidate the propagation characteristics of meteorological and vegetation drought. The results showed that (1) based on a three-dimensional spatiotemporal clustering algorithm, China experienced 138 meteorological drought events and 76 vegetation drought events; (2) the severity of the meteorological drought event No. M138 (2022.03–2022.11) reached 667.58 × 104 km2·month, and the severity of the vegetation drought event No. V68 (2019.06–2020.04) reached 572.89 × 104 km2·month; (3) a total of 40 meteorological-vegetation drought event pairs had been identified, which was divided into three main types: "single", "simple", and "complexity"; and (4) in the typical drought event pair No. P-34, the area difference was 16.45 × 104 km2, and the severity difference was 3.89 × 104 km2. The research results can provide a new perspective for identifying the dynamic changes and propagation characteristics of drought events from a three-dimensional perspective, which is of great significance for predicting vegetation drought and protecting the ecological environment. [ABSTRACT FROM AUTHOR]

Deep vertical rotary tillage (DVRT) is an innovative soil tillage technology that has been widely adopted in China and shown significant potential in enhancing soil quality, optimizing water use efficiency, and increasing crop yields across diverse ecological and agronomic conditions. DVRT utilizes a vertical spiral drill bit for deep plowing, which preserves soil structure, reduces soil compaction, and improves water retention, making it particularly effective in regions facing climatic challenges such as drought. This review synthesizes the effects of DVRT on soil's physical and chemical properties, crop root systems, photosynthesis, and water use efficiency, demonstrating its advantages in promoting robust root development and improving nutrient utilization. Although the technology has been applied successfully across various crops and regions, its nationwide adoption remains limited. This paper emphasizes the need for further research to refine the theoretical framework of DVRT and develop tailored strategies for different local conditions. Additionally, integrating DVRT with other agronomic practices and advancing machinery design, supported by policy measures, is essential for maximizing its benefits. In conclusion, DVRT presents a promising approach for sustainable agriculture in China, contributing to improved soil quality, increased crop yields, and enhanced food security. [ABSTRACT FROM AUTHOR]

Fruit is a major source of vitamins, minerals, and dietary fiber in people's daily lives. Leaf diseases caused by climate change and other factors have significantly reduced fruit production. Deep learning methods for segmenting leaf diseases can effectively mitigate this issue. However, challenges such as leaf folding, jaggedness, and light shading make edge feature extraction difficult, affecting segmentation accuracy. To address these problems, this paper proposes a method based on EFS-Former. The expanded local detail (ELD) module extends the model's receptive field by expanding the convolution, better handling fine spots and effectively reducing information loss. H-attention reduces computational redundancy by superimposing multi-layer convolutions, significantly improving feature filtering. The parallel fusion architecture effectively utilizes the different feature extraction intervals of the convolutional neural network (CNN) and Transformer encoders, achieving comprehensive feature extraction and effectively fusing detailed and semantic information in the channel and spatial dimensions within the feature fusion module (FFM). Experiments show that, compared to DeepLabV3+, this method achieves 10.78%, 9.51%, 0.72%, and 8.00% higher scores for mean intersection over union (mIoU), mean pixel accuracy (mPA), accuracy (Acc), and F_score, respectively, while having 1.78 M fewer total parameters and 0.32 G lower floating point operations per second (FLOPS). Additionally, it effectively calculates the ratio of leaf area occupied by spots. This method is also effective in calculating the disease period by analyzing the ratio of leaf area occupied by diseased spots. The method's overall performance is evaluated using mIoU, mPA, Acc, and F_score metrics, achieving 88.60%, 93.49%, 98.60%, and 95.90%, respectively. In summary, this study offers an efficient and accurate method for fruit tree leaf spot segmentation, providing a solid foundation for the precise analysis of fruit tree leaves and spots, and supporting smart agriculture for precision pesticide spraying. [ABSTRACT FROM AUTHOR]

Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h−1), and spraying norm (250–400 L h−1). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm2 (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R2 = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R2 = 0.437–0.503). [ABSTRACT FROM AUTHOR]

Currently, tomato plant lowering is performed manually, which is both inefficient and costly. The manual process presents challenges in terms of efficiency and cost, creating a need for automated solutions in greenhouse environments. This paper addresses this issue by presenting the design and development of a tomato-plant-lowering robot utilizing machine vision and deep learning techniques. The study includes the design of an end effector optimized for plant-lowering operations based on the physical characteristics of tomato vines and roller hooks; precise positioning of roller hooks achieved through kinematic analysis and a custom dataset; integration of the RepC3 module from RT-DETR with YOLOv5s for enhanced object detection and positioning; and real-time camera feed display through an integrated application. Performance evaluation through experimental tests shows improvements in recognition accuracy, positioning precision, and operational efficiency, although the robot's success rate in leaf removal needs further enhancement. This research provides a solid foundation for future developments in plant-lowering robots and offers practical insights and technical guidance. [ABSTRACT FROM AUTHOR]

To reduce grain loss during pickup and prevent stalk entanglement in buckwheat harvesters, thereby improving the quality of mechanized harvesting, a three-stage pickup conveyor roll with a ground-level rotary knife-type pickup header was designed and tested. This paper, based on the growth characteristics of buckwheat, determined the three-stage pickup conveying process and the overall structure of the ground-level rotary knife-type pickup header. Kinematic analysis and parameter design of the rotary knife-type pickup roll were conducted. Finally, a physical prototype was fabricated, and field performance tests were carried out, using machine forward speed and pickup roll rotational speed as influencing factors and pickup loss rate as an evaluation metric. Results showed that the interaction between pickup roll rotational speed and forward speed had a significant effect on the pickup loss rate, with forward speed having a greater impact than pickup roll rotational speed. Under consideration of their interaction, when the pickup roll rotational speed was within the range of 396–457 r/min, and the forward speed was between 0.9–1.0 m/s, the pickup loss rate was minimized. Based on the regression equation model, the predicted optimal conditions were a forward speed of 1.0 m/s and a roll pickup speed of 396 rpm. Under these conditions, the test results showed a pickup loss rate of 5.235%, indicating good pickup performance. This research provides a reference for the design of pickup devices in grain combine harvesters. [ABSTRACT FROM AUTHOR]

Wheat is one of the most crucial food crops globally, with its yield directly impacting global food security. The accurate detection and counting of wheat spikes is essential for monitoring wheat growth, predicting yield, and managing fields. However, the current methods face challenges, such as spike size variation, shading, weed interference, and dense distribution. Conventional machine learning approaches have partially addressed these challenges, yet they are hampered by limited detection accuracy, complexities in feature extraction, and poor robustness under complex field conditions. In this paper, we propose an improved YOLOv10 algorithm that significantly enhances the model's feature extraction and detection capabilities. This is achieved by introducing a bidirectional feature pyramid network (BiFPN), a separated and enhancement attention module (SEAM), and a global context network (GCNet). BiFPN leverages both top-down and bottom-up bidirectional paths to achieve multi-scale feature fusion, improving performance in detecting targets of various scales. SEAM enhances feature representation quality and model performance in complex environments by separately augmenting the attention mechanism for channel and spatial features. GCNet captures long-range dependencies in the image through the global context block, enabling the model to process complex information more accurately. The experimental results demonstrate that our method achieved a precision of 93.69%, a recall of 91.70%, and a mean average precision (mAP) of 95.10% in wheat spike detection, outperforming the benchmark YOLOv10 model by 2.02% in precision, 2.92% in recall, and 1.56% in mAP. Additionally, the coefficient of determination (R2) between the detected and manually counted wheat spikes was 0.96, with a mean absolute error (MAE) of 3.57 and a root-mean-square error (RMSE) of 4.09, indicating strong correlation and high accuracy. The improved YOLOv10 algorithm effectively solves the difficult problem of wheat spike detection under complex field conditions, providing strong support for agricultural production and research. [ABSTRACT FROM AUTHOR]

There is a close relationship between stem and leaf biological characteristics of mature broccoli plants and defoliation technology. Morphological parameters such as the spread degree and diameter of cauliflower stem and leaf and the connection performance of cauliflower stem and leaf were studied. These experiments are helpful to the research of defoliation devices and methods for large-scale manual transportation lines. In this paper, according to the damage forms that may be suffered in the separation process of mature broccoli stems and leaves, "sunny" broccoli varieties were selected. Firstly, the mechanical properties of mature broccoli stems were obtained through different loading methods such as stretching, compression, shearing and bending. Secondly, the stress relaxation characteristics of broccoli were analyzed by static compression of broccoli flower balls. Finally, the separation method of broccoli stems and leaves was simulated by ANSYS. The simulation results showed that shear fracture was more suitable for stem and leaf separation of broccoli than tensile fracture. Then, aiming at the separation of stems and leaves of broccoli, an artificial broccoli assembly line was proposed to ensure the efficiency of leaf removal through man–machine cooperation. The dynamic characteristics of the belt of broccoli leaf removal line were studied to ensure the efficient and stable operation of the conveyor system of broccoli artificial leaf removal line. [ABSTRACT FROM AUTHOR]

Quick and accurate prediction of crop yields is beneficial for guiding crop field management and genetic breeding. This paper utilizes the fast and non-destructive advantages of an unmanned aerial vehicle equipped with a multispectral camera to acquire spatial characteristics of rice and conducts research on yield estimation in an open environment. The study proposes a yield estimation framework that hybrids synthetic minority oversampling technique (SMOTE) and deep neural network (DNN). Firstly, the framework used the Pearson correlation coefficient to select 10 key vegetation indices and determine the optimal feature combination. Secondly, it created a dataset for data augmentation through SMOTE, addressing the challenge of long data collection cycles and small sample sizes caused by long growth cycles. Then, based on this dataset, a yield estimation model was trained using DNN and compared with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). The experimental results indicate that the hybrid framework proposed in this study performs the best (R2 = 0.810, RMSE = 0.69 t/ha), significantly improving the accuracy of yield estimation compared to other methods, with an R2 improvement of at least 0.191. It demonstrates that the framework proposed in this study can be used for rice yield estimation. Additionally, it provides a new approach for future yield estimation with small sample sizes for other crops or for predicting numerical crop indicators. [ABSTRACT FROM AUTHOR]

The segmentation and extraction of oilseed rape crop rows are crucial steps in visual navigation line extraction. Agricultural autonomous navigation robots face challenges in path recognition in field environments due to factors such as complex crop backgrounds and varying light intensities, resulting in poor segmentation and slow detection of navigation lines in oilseed rape crops. Therefore, this paper proposes VC-UNet, a lightweight semantic segmentation model that enhances the U-Net model. Specifically, VGG16 replaces the original backbone feature extraction network of U-Net, Convolutional Block Attention Module (CBAM) are integrated at the upsampling stage to enhance focus on segmentation targets. Furthermore, channel pruning of network convolution layers is employed to optimize and accelerate the model. The crop row trapezoidal ROI regions are delineated using end-to-end vertical projection methods with serialized region thresholds. Then, the centerline of oilseed rape crop rows is fitted using the least squares method. Experimental results demonstrate an average accuracy of 94.11% for the model and an image processing speed of 24.47 fps/s. After transfer learning for soybean and maize crop rows, the average accuracy reaches 91.57%, indicating strong model robustness. The average yaw angle deviation of navigation line extraction is 3.76°, with a pixel average offset of 6.13 pixels. Single image transmission time is 0.009 s, ensuring real-time detection of navigation lines. This study provides upper-level technical support for the deployment of agricultural robots in field trials. [ABSTRACT FROM AUTHOR]

The priority problem in intensive viticulture is reducing pesticides, and fertilizers, and improving water-use efficiency. This is driven by global and EU regulatory efforts. This review, systematically examines 92 papers, focusing on progress in satellite solutions over time, and (pre)processing improvements of spatio-temporal and spectral resolution. The importance of the integration of satellites with ground truth data is highlighted. The results provide precise on-field adaptation strategies through the generation of prescription maps and variable rate application. This enhances sustainability and efficiency in vineyard management and reduces the environmental footprint of vineyard techniques. The effectiveness of different vegetation indices in capturing spatial and temporal variations in vine health, water content, chlorophyll levels, and overall vigor is discussed. The challenges in the use of satellite data in viticulture are addressed. Advanced satellite technologies provide detailed vineyard monitoring, offering insights into spatio-temporal variability, soil moisture, and vine health. These are crucial for optimizing water-use efficiency and targeted management practices. By integrating satellite data with ground-based measurements, viticulturists can enhance precision viticulture, reduce reliance on chemical interventions, and improve overall vineyard sustainability and productivity. [ABSTRACT FROM AUTHOR]

This review article focuses on agrivoltaic production systems (AV). The transition towards renewable energy sources, driven by the need to respond to climate change, competition for land use, and the scarcity of fossil fuels, has led to the consideration of new ways to optimise land use while producing clean energy. AV systems not only generate energy but also allow agricultural and livestock yields to be maintained or even increased under PV structures, offering a sustainable production strategy that may be more acceptable to local communities than traditional PV installations. This review assesses the technical feasibility of AV systems, the environmental, economic and social benefits, as well as the challenges faced and the legal framework regulating their implementation. It is highlighted that despite the advantages in land use efficiency and dual food and energy production, there are important challenges related to the initial investment required, the need for technological adaptation, social and regulatory obstacles, or the effects of shading on production. This paper underlines the importance of further research and development of these systems to overcome technical and economic constraints and maximise their potential benefits. It is concluded that although they present significant challenges, AV management offers promising opportunities to improve land efficiency and contribute to several sustainable development goals. [ABSTRACT FROM AUTHOR]

This paper proposes a method for localized damage detection in tomato, with the objective of enabling the detection of bruises prior to sorting. Bioimpedance spectroscopy technology is employed to assess the extent of localized damage in tomato. An equivalent circuit model is constructed, and the impedance spectroscopy data are obtained by developing a local damage measurement platform for tomatoes using a self-designed circular four-electrode BIS sensor. The electrical parameters are then extracted by fitting the constructed equivalent circuit model to the tomato data. Subsequently, we analyze the variation rules of the electrical parameters in different damage levels. To reduce the dimensionality of the features, including biological variables, fitted electrical parameters, and tomato ripeness, we employ Spearman feature selection. We then classify the reduced features by combining the advantages of the support vector machine and the artificial neural network. The results demonstrate that the designed circular four-electrode BIS sensor can non-destructively measure localized damage conditions in tomato. A localized damage measurement platform for tomatoes has been constructed using this sensor. A comparison of the impedance measurements obtained using the designed circular four-electrode BIS sensor with those obtained using a needle sensor proposed by previous scholars revealed that both sensors exhibited a decrease in impedance with increasing damage degree. This finding indicates that the designed circular four-electrode BIS sensor is an effective tool for characterizing damage conditions in tomatoes. The design of the tomato circular four-electrode BIS sensor is an effective means of characterizing tomato damage. The Spearman-SVM-ANN damage classification algorithm, based on the Spearman feature selection, effectively classified tomato damage with a 98.765% accuracy rate. The findings of this study provide a reference for the grading and transportation of tomatoes after harvest. [ABSTRACT FROM AUTHOR]

Sustainable management of potassium nutrition in alfalfa crop production is one of the major key factors for achieving optimum seed and biomass yields. An inappropriate supply of mineral potassium nutrition in alfalfa production could result in a decrease in biomass and grain yield production, leading to luxury consumption with cost implications. Alfalfa (Medicago sativa L.) is a perennial leguminous forage crop known for its high protein content, nutritive value, biomass yield production, soil-improving abilities, and livestock feed. Potassium nutrition plays a crucial role in alfalfa production by influencing several physiological processes essential for biomass yield, growth, development, photosynthesis, nutrient uptake, and stress tolerance. Although several studies have been conducted regarding the role of potassium nutrition in agriculture productivity, only limited research has focused on crop-specific impacts. Therefore, this paper reviews (i) the significant role potassium nutrition plays in alfalfa production along with its implications for quality, yield, growth, and resistance to abiotic stress; (ii) the factors affecting the availability, absorption, and transport of potassium; (iii) the source of potassium and the consequences of inadequate availability; and (iv) highlights some strategies for mitigating potassium nutrient deficiency to optimize alfalfa productivity and sustainability in agricultural systems. [ABSTRACT FROM AUTHOR]

Tomatoes are a critical economic crop. The realization of tomato harvesting automation is of great significance in solving the labor shortage and improving the efficiency of the current harvesting operation. Accurate recognition of fruits is the key to realizing automated harvesting. Harvesting fruit at optimum ripeness ensures the highest nutrient content, flavor and market value levels, thus maximizing economic benefits. Owing to foliage and non-target fruits obstructing target fruits, as well as the alteration in color due to light, there is currently a low recognition rate and missed detection. We take the greenhouse tomato as the object of research. This paper proposes a tomato recognition model based on the improved YOLOv8 architecture to adapt to detecting tomato fruits in complex situations. First, to improve the model's sensitivity to local features, we introduced an LSKA (Large Separable Kernel Attention) attention mechanism to aggregate feature information from different locations for better feature extraction. Secondly, to provide a higher quality upsampling effect, the ultra-lightweight and efficient dynamic upsampler Dysample (an ultra-lightweight and efficient dynamic upsampler) replaced the traditional nearest neighbor interpolation methods, which improves the overall performance of YOLOv8. Subsequently, the Inner-IoU function replaced the original CIoU loss function to hasten bounding box regression and raise model detection performance. Finally, the model test comparison was conducted on the self-built dataset, and the test results show that the mAP0.5 of the YOLOv8-Tomato model reached 99.4% and the recall rate reached 99.0%, which exceeds the original YOLOv8 model detection effect. Compared with faster R-CNN, SSD, YOLOv3-tiny, YOLOv5, and YOLOv8 models, the average accuracy is 7.5%, 11.6%, 8.6%, 3.3%, and 0.6% higher, respectively. This study demonstrates the model's capacity to efficiently and accurately recognize tomatoes in unstructured growing environments, providing a technical reference for automated tomato harvesting. [ABSTRACT FROM AUTHOR]

The extraction of navigation lines is a crucial aspect in the field autopilot system for intelligent agricultural equipment. Given that soybean seedlings are small, and straw can be found in certain Northeast China soybean fields, accurately obtaining feature points and extracting navigation lines during the soybean seedling stage poses numerous challenges. To solve the above problems, this paper proposes a method of extracting navigation lines based on the average coordinate feature points of pixel points in the bean seedling belt according to the calculation of the average coordinate. In this study, the soybean seedling was chosen as the research subject, and the Hue, Saturation, Value (HSV) colour model was employed in conjunction with the maximum interclass variance (OTSU) method for RGB image segmentation. To extract soybean seedling bands, a novel approach of framing binarised image contours by drawing external rectangles and calculating average coordinates of white pixel points as feature points was proposed. The feature points were normalised, and then the improved adaptive DBSCAN clustering method was used to cluster the feature points. The least squares method was used to fit the centre line of the crops and the navigation line, and the results showed that the average distance deviation and the average angle deviation of the proposed algorithm were 7.38 and 0.32. The fitted navigation line achieved an accuracy of 96.77%, meeting the requirements for extracting navigation lines in intelligent agricultural machinery equipment for soybean inter-row cultivation. This provides a theoretical foundation for realising automatic driving of intelligent agricultural machinery in the field. [ABSTRACT FROM AUTHOR]

To address the inefficient crushing of fertilizer during the mechanized spreading process caused by the caking of high-humidity organic fertilizer, a fertilizer spreader with a combined rotor and lining plate crushing mechanism was proposed in this paper. With the introduction of the basic structure and working principle of the spreader, a particle group model for an organic fertilizer consisting of both caked and bulk fertilizer was built, based on the Hertz–Mindlin model with bonding and the Hertz–Mindlin model with JKR contact, in EDEM to construct an organic fertilizer-crushing-and-spreading model. With the rotor speed, the axial distance of the hammer, and the number of circumferential hammer groups as the experimental factors and the maximum broken bond rate of the caked organic fertilizer and the minimum coefficient of variation of spreading uniformity as the experimental indices, the Box–Behnken test method was employed to establish regression equations for response surface analysis and multi-objective optimization of the test results. The results indicated that, when the rotor speed was 6.47 Hz, the axial distance of the hammer was 90.30 mm, the number of circumferential hammer groups was five, the broken bond rate reached 90.86%, and the coefficient of variation was 21.45%. Verification tests under these conditions showed a broken bond rate of 90.03% and a coefficient of variation of 22.12%, which were consistent with the optimization results. Therefore, our research provides a reference for the structural design of an organic fertilizer spreader and the optimization of its working parameters. [ABSTRACT FROM AUTHOR]

The Anthesis-Silking Interval (ASI) is a crucial indicator of the synchrony of reproductive development in maize, reflecting its sensitivity to adverse environmental conditions such as heat stress and drought. This paper presents an automated method for detecting the maize ASI index using a field high-throughput phenotyping platform. Initially, high temporal-resolution visible-light image sequences of maize plants from the tasseling to silking stage are collected using a field rail-based phenotyping platform. Then, the training results of different sizes of YOLOv8 models on this dataset are compared to select the most suitable base model for the task of detecting maize tassels and ear silks. The chosen model is enhanced by incorporating the SENetv2 and the dual-layer routing attention mechanism BiFormer, named SEBi-YOLOv8. The SEBi-YOLOv8 model, with these combined modules, shows improvements of 2.3% and 8.2% in mAP over the original model, reaching 0.989 and 0.886, respectively. Finally, SEBi-YOLOv8 is used for the dynamic detection of maize tassels and ear silks in maize populations. The experimental results demonstrate the method's high detection accuracy, with a correlation coefficient (R2) of 0.987 and an RMSE of 0.316. Based on these detection results, the ASI indices of different inbred lines are calculated and compared. [ABSTRACT FROM AUTHOR]

Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines the definition, evaluation methods, indicator selection, and relevant case studies. The concept of soil quality supplements soil science research by deepening our understanding of soils and aiding in the allocation of resources as agriculture intensifies to meet rising global demand. Soil quality provides a framework for educating stakeholders about the essential functions of soils and offers a tool for assessing and comparing different management techniques. Regular evaluation of soil quality is vital for maintaining high crop yields and addressing the gap between production and consumption. Nowadays, many researchers have explored machine learning (ML) and deep learning (DL) techniques and various algorithms to model and predict soil quality with satisfactory results. These chosen indicators can be influenced by chemical, biological, or physical features. This paper compares ML and DL with traditional methods, examining their features, limitations, different categories of machine learning, and their applications in soil quality assessment. Finally, we show that predicting soil quality has the potential to be extremely accurate and efficient with ML and DL. This distinguishes the application of DL and ML from other approaches since they can anticipate the soil quality index without the need for more intricate computations. Our suggestion for future studies is to evaluate soil quality over broader regions and predict it by using more accurate, modern, and faster methods, using a variety of activation functions and algorithms. [ABSTRACT FROM AUTHOR]

The aim of this research was to determine the impact of composted mushroom substrate and composted municipal waste on the quality and yield of Miscanthus sinensis Andersson biomass. The plant was grown on anthropogenic soil, cultured earth type and hortisol subtype, with a pH of 6.81. Before planting rhizomes, experimental plots were treated with composted mushroom substrate and composted municipal waste, applied separately or in combination, each dose introducing 170 N kg·ha−1 to the soil. During the experiment, observations of plant development and growth were carried out, and the yield was determined. Each growing season's measurements were taken of the grass height, the number and diameter of stems and the number of leaf blades and of nodes per stem. In order to determine the level of plant stress, relative chlorophyll content and chlorophyll fluorescence parameters were determined. The measurements were carried out in a non-invasive way, using the SPAD-502 chlorophyll meter and OS30p+ plant stress meter. For the research hypothesis, it was assumed that the one-time addition of composted mushroom substrate and composted municipal waste to the soil would increase yields. The experiment also aimed to assess the impact of both types of compost on the yield and morphological characteristics of Miscanthus sinensis. Its yields increased steadily, and, in the third year of cultivation, were higher by 52%. The highest average yields were noted on plots fertilized only with composted mushroom substrate (KPP100%), with 8.44 Mg·ha−1 DM, and with compost from municipal waste (KOM100%), with 7.91 Mg·ha−1 DM. The experience presents a solution to the problem of increasing amounts of organic waste and represents an improvement in cultivation techniques to increase crop yields, improve their quality and increase resistance to biotic and abiotic stress. This paper highlights the possibility of applying environmentally friendly organic waste materials to energy crops used as a sustainable energy source. [ABSTRACT FROM AUTHOR]

The interaction between the leaf and airflow directly influences droplet deposition on the leaf surface. This paper investigates the effect of this interaction on droplet deposition. A bidirectional fluid-structure coupling model was established using computational fluid dynamics (CFD) based on mechanical parameters and surface roughness of tomato leaves to simulate tomato leaf deposition under air-assisted spraying. Utilizing the model and considering air velocity, droplet size, and initial leaf inclination as experimental factors, a three-factor, three-level central composite design simulation and response surface analysis were conducted to examine the influence of each factor on the surface deposition amount of tomato leaves. The order of influence of each factor on the deposition amount is as follows: a quadratic regression model was established with the flow velocity having the greatest influence, followed by initial leaf inclination and then droplet size. The influence of each factor on the deposition distribution of the leaf surface was compared and studied separately. Airflow velocity significantly affected the deposition distribution of the leaf surface. Higher airflow velocities resulted in a lower proportion of deposition at the tip and a higher proportion at the base. The maximum relative errors of leaf deformation and deposition were 8.77% and 17.44%, respectively. The findings of this research can provide valuable insights for optimizing the working parameters of air-assisted atomizers. [ABSTRACT FROM AUTHOR]

Given the problems such as insufficient control on pests and diseases or pesticide damage on plants caused by uneven distribution of pesticide droplets during the current application process by UAVs, this paper designed novel uniform application equipment with nozzles swinging horizontally based on a UAV platform in order to improve the distribution uniformity of droplets volume. Nozzles swinging periodically are able to increase the overlap probability of spray fans generated from nozzles. It is helpful to further the spray deposition uniformity improvement. Through droplet motion analysis, CFD simulation, and spray tests, it was determined that the key factors affecting uniformity were the oscillating rod length, spray height, and nozzle angle. The best parameter combination was explored as the length of 175 mm, the height of 1.5 m, and the angle of 15°. Based on this combination, the prototype was produced and installed on the UAV platform. A field test was carried out to verify its performance. The results showed that the CV of the improved UAV was 26.41%, which was 6.43 percentage points lower than the traditional UAV, and the decrease was 19.58%, meaning that it is feasible to use this equipment to improve uniformity. [ABSTRACT FROM AUTHOR]

The escalating global population has led to an increased demand for both quantity and quality in food production. Throughout history, plant diseases have posed significant threats to agricultural output by causing substantial food losses annually while also compromising product quality. Accurate identification of pathogens, clarifying the pathogenic mechanism of pathogens, and understanding the interaction between pathogens and hosts are important for the control of plant diseases. This Special Issue, "Research Progress on Pathogenicity of Fungi in Crops", belongs to the section "Pest and Disease Management" of Agronomy. It contains research papers on the identification and phylogeny of fungal pathogens, the molecular genetics of plant fungal pathogens, the molecular mechanisms of fungal pathogenicity, and the molecular basis of the interaction between fungi and crops. These studies encapsulate efforts to understand disease systems within current genomics, transcriptomics, proteomics, and metabolomics studies, highlighting research findings that could be future targets for crop disease and pest control. The studies presented in this Special Issue promote the progress of fungal pathogenicity research in crops and provide a scientific basis for future disease control, which is of great significance for sustainable agricultural development and global food security. [ABSTRACT FROM AUTHOR]

The estimation of orchard blooming levels and the determination of peak blooming dates are very important because they determine the timing of orchard flower thinning and are essential for apple yield and quality. In this paper, we propose an orchard blooming level estimation method for global-level and block-level blooming level estimation of orchards. The method consists of a deep learning-based apple flower detector, a blooming level estimator, and a peak blooming day finding estimator. The YOLOv5s model is used as the apple flower detector, which is improved by adding a coordinate attention layer and a small object detection layer and by replacing the model neck with a bidirectional feature pyramid network (BiFPN) structure to improve the performance of the apple flower detector at different growth stages. The robustness of the apple flower detector under different light conditions and the generalization across years was tested using apple flower data collected in 2021–2022. The trained apple flower detector achieved a mean average precision of 77.5%. The blooming level estimator estimated the orchard blooming level based on the proportion of flowers detected at different growth stages. Statistical results show that the blooming level estimator follows the trend of orchard blooming levels. The peak blooming day finding estimator successfully positioned the peak blooming time and provided information for the flower thinning timing decision. The method described in this paper is able to provide orchardists with accurate information on apple flower growth status and is highly automated. [ABSTRACT FROM AUTHOR]

Smart agriculture relies on accurate yield maps as a crucial tool for decision-making. Many yield maps, however, suffer from spatial errors that can compromise the quality of their data, while several approaches have been proposed to address some of these errors, detecting voids or holes in the maps remains challenging. Additionally, the quality of yield datasets is typically evaluated based on root mean squared errors after interpolation. This evaluation method relies on weighbridge data, which can occasionally be inaccurate, impacting the quality of decisions made using the datasets. This paper introduces a novel algorithm designed to identify voids in yield maps. Furthermore, it maps three types of spatial errors (GPS errors, yield surges, and voids) to two standard data quality dimensions (accuracy and completeness). Doing so provides a quality score that can be utilized to assess the quality of yield datasets, eliminating the need for weighbridge data. The paper carries out three types of evaluations: (1) evaluating the algorithm's efficacy by applying it to a dataset containing fields with and without voids; (2) assessing the benefits of integrating void detection and other spatial error identification techniques into the yield data processing chain; and (3) examining the correlation between root mean squared error and the proposed quality score before and after filtering out spatial errors. The results of the evaluations demonstrate that the proposed algorithm achieves a 100% sensitivity, 91% specificity, and 82% accuracy in identifying yield maps with voids. Additionally, there is a decrease in the root mean squared error when various spatial errors, including voids after applying the proposed data pre-processing chain. The inverse correlation observed between the root mean squared error and the proposed quality score (−0.577 and −0.793, before and after filtering spatial errors, respectively) indicates that the quality score can effectively assess the quality of yield datasets. This assessment enables seamless integration into real-time big data quality assessment solutions based on various data quality dimensions. [ABSTRACT FROM AUTHOR]

In essence, the core of APV is to manage light while considering the unique conditions of crops, enabling farmers to create a more sustainable and productive cultivation environment and then use excess solar energy for PV electricity generation. According to the International Energy Agency (IEA) [[1]], deploying renewable energy technologies is crucial for achieving the net-zero emissions target by 2050. The crop cultivation performance of APV-utilizing cells such as amorphous silicon and cadmium telluride is suboptimal because the light transmission spectrum of thin-film solar cells is not aligned with the range required for crop photosynthesis. The second paper, written by Wagner M et al. [[21]], found that installing overhead APV on agricultural land has positive ecological outcomes; the study also demonstrated that, under certain conditions, APV can promote the expansion of renewable energy production resources without reducing food production resources. [Extracted from the article]

Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield prediction methods in solar greenhouses, based on experimental data for water and fertilizer consumption by greenhouse tomatoes in different regions over many years, this paper investigated the prediction models of greenhouse tomato yields under three different soil fertility conditions (low, medium, and high). Under these three different soil fertility conditions, greenhouse tomato yields were predicted using the neural network prediction model (NN), the neural network prediction model based on particle swarm optimization (PSO–NN), the neural network prediction model based on an adaptive inertia weight particle swarm optimization algorithm (AIWPSO–NN), and the neural network prediction model based on the improved particle swarm optimization algorithm (IPSO–NN). The experimental results demonstrate that the evaluation indexes (mean square error, mean absolute error, and R2) of the IPSO–NN prediction model proposed in this paper were superior to the other three prediction models (i.e., NN prediction model, AIWPSO–NN prediction model, and IPSO–NN prediction model) under three different soil fertility conditions. Among them, compared with the NN prediction model, the MSE of the other three prediction models under high soil fertility decreased to 0.0082, 0.0041, and 0.0036; MAE decreased to 0.0759, 0.0511, and 0.0489; R2 decreased to 0.8641, 0.9323, and 0.9408. These results indicated that the IPSO–NN prediction model had a higher predictive ability for greenhouse tomato yields under three different soil fertility conditions. In view of the important role of tomato yield prediction in greenhouses, this technology may be beneficial to agricultural management and decision support. [ABSTRACT FROM AUTHOR]

Kale has gained popularity as a healthy food choice due to its rich nutrient profile. However, kale production systems are also affected by various pests and diseases, especially plant pathogenic bacteria, such as Pectobacterium carotovorum. The purpose of this research was to investigate the biocontrol activity of the Pseudomonas fluorescens strain SP007s and develop a formulation that provides stability, long shelf life, and disease control, as well as promoting growth in kale; we expect this formulation to have further commercial applications in the cultivation of kale. The results indicated that a unique mixture of paper sludge, sugar sludge, and glycerol at 40% relative humidity promoted SP007 survival for 6 months at room temperature (30 °C) with measures of 5.92 × 106 CFU/g. This treatment maintained the control efficacy of P. carotovorum in infected soil within 28 days of soil drenching. We evaluated the SP007s formula for controlling soft rot disease in greenhouse conditions, and our results revealed that it can reduce the disease in kale by 65.2% and promotes growth in terms of canopy width, shoot height, number of leaves per plant, fresh weight, and dry weight, which were superior to the control. In addition, the formula can induce the accumulation of endogenous salicylic acid and phenolic compounds, as well as a systemic acquired resistance pathway in the plant defense system. Therefore, the formula of SP007s can be a promising novel biocontrol formula for use in kale production, as it promotes plant growth and acts against P. carotovorum causal soft rot disease. [ABSTRACT FROM AUTHOR]

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network's attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network's ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops. [ABSTRACT FROM AUTHOR]

The coupling coordination of agricultural technology innovation with food security is of great significance for high-quality agricultural development. By identifying the coupling coordination relationship between the two systems and the influencing factors, this paper aims to promote the virtuous cycle of coordinated development between regional agriculture and technology, as well as accelerate the realisation of high-quality development of Chinese agriculture. Therefore, this paper explores the spatial and temporal coupling characteristics of the two using the entropy value method, coupling coordination degree model, and exploratory spatial data analysis, and it screens for important influencing factors using the grey correlation model. The main results show that ① the coupling coordination relationship between agricultural technology innovation and food security in China is at a dissonant stage, but the value of the coupling coordination degree increases from 0.2076 to 0.3437 during the period of study, and the level of coordination gradually improves. ② The degree of coupling coordination in the provincial space exhibits a distribution pattern of "high in the east and low in the west". The areas of high value are primarily situated in the provinces of Shandong, Jiangsu, and other provinces along the southeastern coast of China, while the areas of low value are mainly located in the provinces of Qinghai, Ningxia, and other provinces in inland northwest China. ③ The Moran's index of provincial coupling coordination is greater than 0, showing a certain positive correlation, and there is a significant pattern of spatial aggregation. ④ The correlation coefficients between the influencing factors and the degree of coupling coordination are all greater than 0.35, indicating a moderate or high correlation, but the significance of technological support capacity and food distribution security increased over time. [ABSTRACT FROM AUTHOR]

The environmental temperature and humidity are crucial factors for the normal growth and development of arbor tree cuttings by altering their hormone levels and influencing their physiological activities. Developing a temperature and humidity environmental model for arbor tree cuttings serves as a key technique to improve the adjustment performance of environmental parameters in facility agriculture systems and enhance the rooting rate of cuttings. This paper provides a comprehensive summary of current research on the inherent characteristics of cuttings and the factors influencing environmental temperature and humidity. It explores the mechanisms of interaction between the inherent characteristics of cuttings and the factors influencing environmental temperature and humidity. This paper investigates the interactive relationships among the factors affecting environmental temperature and humidity. It analyzes methods to improve the efficiency of constructing temperature and humidity environmental models for arbor tree cuttings. To enhance the transferability of the environmental model, the necessary physiological activities under the influence of plant hormones are generalized as common physiological traits in the growth and development of cuttings. In addition, this paper explores the factors influencing the air and substrate temperature and the humidity in facility agriculture systems as well as two types of facilities for controlling environmental temperature and humidity. Furthermore, it reviews the research progress in environmental models from both mechanistic and data-driven perspectives. This paper provides a comparative analysis of the characteristics associated with these two model categories. Building upon this, the paper summarizes and discusses methods employed in constructing temperature and humidity environmental models for arbor tree cuttings. In addition, it also anticipates the application of deep learning techniques in the construction of temperature and humidity environmental models for arbor cuttings, including utilizing machine vision technology to monitor their growth status. Finally, it proposes suggestions for building physiological models of fruit tree-like arbor cuttings at different growth stages. To enhance the transferability of environmental models, the integration of physiological models of cuttings, environmental models, and control system performance are suggested to create an environmental identification model. This paper aims to achieve control of the common physiological activities of cuttings. [ABSTRACT FROM AUTHOR]

The non-judicious use of water at the farm level in traditional irrigation application methods is a present-day concern across the world that can be resolved by enhancing application efficiency through the adoption of advanced irrigation techniques. Sprinkler irrigation is a method that has high application efficiency, which can be further increased when coupled with automation toward precision irrigation. The objectives of this review are to summarize the main aspects of sprinkler and precision irrigation and their development, scope, and future prospects specifically in Asian countries. In this paper, a modified methodology, inspired by PRISMA guidelines, was used to explore the available literature to summarize the existing knowledge in the field. Regarding the technological aspects of the analyzed works, it became evident that sprinkler systems are an efficient method to not only irrigate crops (with 39% water saving) but also for the application of fertilizers with higher efficiency (>35%) and water productivity (>14.1%) compared with gravity irrigation systems. Moreover, this paper highlights the prominent features of precision irrigation for maximizing agricultural productivity. The use of sprinkler irrigation with precision applications using automation with a sensor-based mechanism for field data collection, data transformation, data analysis, and operation of IoT-based automatic solenoid valves can save 20–30% more irrigation water and increase crop yield by 20–27%. An analytical understanding and knowledge of the field were used to draw conclusions that are thought-provoking for scientists, researchers, and other stakeholders. [ABSTRACT FROM AUTHOR]