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Accurate and timely prediction of crop yields is crucial for ensuring food security and promoting sustainable agricultural practices. This study developed a winter wheat yield prediction model using machine learning techniques, incorporating remote sensing data and statistical yield records from Henan Province, China. The core of the model is an ensemble voting regressor, which integrates ridge regression, gradient boosting, and random forest algorithms. This study optimized the hyperparameters of the ensemble voting regressor and conducted an in-depth comparison of its yield prediction performance with that of other mainstream machine learning models, assessing the impact of key hyperparameters on model accuracy. This study also explored the potential of yield prediction at different growth stages and its application in yield spatialization. The results demonstrate that the ensemble voting regressor performed exceptionally well throughout the entire growth period, with an R2 of 0.90, an RMSE of 439.21 kg/ha, and an MAE of 351.28 kg/ha. Notably, during the heading stage, the model's prediction performance was particularly impressive, with an R2 of 0.81, an RMSE of 590.04 kg/ha, and an MAE of 478.38 kg/ha, surpassing models developed for other growth stages. Additionally, by establishing a yield spatialization model, this study mapped county-level yield predictions to the pixel level, visually illustrating the spatial differences in land productivity. These findings provide reliable technical support for winter wheat yield prediction and valuable references for crop yield estimation in precision agriculture. [ABSTRACT FROM AUTHOR]

Vegetation indices are widely used to assess vegetation dynamics. The Normalized Vegetation Index (NDVI) is the most widely used metric in agriculture, frequently as a proxy for different physiological and agronomical aspects, such as crop yield or biomass, crop density, or drought stress. Much effort has therefore been directed to NDVI forecasting, which is usually correlated with precipitation. However, in Mediterranean and arid climates, the relationship is more complex due to prolonged dry periods and sparse precipitation events. In this study, we forecast the NDVI 7 and 30 days ahead for Mediterranean permanent grasslands using a machine learning Random Forest (RF) model for the period from 2015 to 2022. The model compares two soil moisture products as predictors: simulated soil moisture values based on in situ soil moisture sensor observations and remote sensing-derived observations of Soil Water Index (SWI) values. We further analyzed the anomalies of the predicted NDVI using the z-score. The results show that both products can be used as reliable predictors for permanent grasslands in Mediterranean areas. Predictions at 7 days are more accurate and better forecast the negative effect of drought on vegetation dynamics than those at 30 days. This study shows the potential of using a simple methodology and readily available data to predict the grassland growth dynamics in the Mediterranean area. [ABSTRACT FROM AUTHOR]

Crop models are instrumental in simulating resource utilization in agriculture, yet their complexity necessitates extensive calibration, which can impact the accuracy of yield predictions. Machine learning shows promise for enhancing yield estimations but relies on vast amounts of training data. This study aims to improve the pakchoi yield prediction accuracy of simulation models. We developed a stacking ensemble learning model that integrates three base models—EU-Rotate_N, Random Forest Regression and Support Vector Regression—with a Multi-layer Perceptron as the meta-model for the pakchoi dry matter yield prediction. To enhance the training dataset and bolster machine learning performance, we employed the EU-Rotate_N model to simulate daily dry matter yields for unsampled data. The test results revealed that the stacking model outperformed each base model. The stacking model achieved an R² value of 0.834, which was approximately 0.1 higher than that of the EU-Rotate_N model. The RMSE and MAE were 0.283 t/ha and 0.196 t/ha, respectively, both approximately 0.6 t/ha lower than those of the EU-Rotate_N model. The performance of the stacking model, developed with the expanded dataset, showed a significant improvement over the model based on the original dataset. [ABSTRACT FROM AUTHOR]

Maize plays a crucial role in China's grain production, with a cultivation area reaching 44.22 million hectares and an annual yield of 289 million tons in 2023. However, the challenge remains on how to further increase maize yield and water use efficiency (WUE) without adding to the environmental burden. To systematically evaluate the impact of straw incorporation under varying production conditions on maize yield and WUE, this study collected experimental data from multiple locations across China. A meta-analysis was conducted to compare the effects of straw incorporation versus no incorporation, and the main influencing factors were identified using correlation analysis and a random forest model. The results indicate that straw incorporation significantly enhances both maize yield and WUE, with the most pronounced improvements observed under conditions of an average growing season temperature of 19–23 °C, soil pH of 6.5–7.5, low initial soil organic matter content, and deep plowing for straw incorporation. Additionally, moderate nitrogen application rates and straw incorporation amounts (9000–15,000 kg·ha−2) also significantly boost maize yield and WUE. Field management practices and meteorological conditions are identified as the primary factors affecting maize yield and WUE under straw incorporation conditions. Therefore, straw incorporation stands out as an effective agricultural practice for achieving high maize yields and efficient resource utilization. The findings of this study provide valuable insights for global food security and the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Timely and accurate predictions of winter wheat yields are key to ensuring food security. In this research, winter wheat yield prediction models for six provinces were established using a random forest (RF) model. Two methods were employed to analyze feature variables. RF partial dependence plots were generated to demonstrate the nonlinear relationships between the feature variables and yield, and bivariate Moran's I was considered to identify the spatial associations between variables. Results showed that when environmental data from key growth periods were used for prediction model establishment, the root mean square error (RMSE) varied between 200 and 700 kg/ha, and the coefficient of determination (R2) exceeded 0.5. Feature variable analysis results indicated that the longitude, latitude, topography and normalized difference vegetation index (NDVI) were important variables. Below the threshold, the yield gradually increased with increasing NDVI. Bivariate Moran's I results showed that there was zonal distribution of meteorological elements. Within a large spatial range, the change in environmental variables due to the latitude and longitude should be accounted for in modeling, but the influence of collinearity between the feature variables should be eliminated via variable importance analysis. [ABSTRACT FROM AUTHOR]

Straw returning may stimulate soil microbial activity, thereby influencing microbial-mediated soil nitrification, which can lead to nitrate leaching and nitrogen (N) loss. However, its effects under long-term nitrogen fertilization remain unclear. At an experimental station with 34 years of fertilizer application (0, 135, and 270 kg ha−1 N), we investigated how nitrogen fertilization and straw returning affected the soil potential nitrification rate (PNR) and ammonia-oxidizing microorganisms (AOM). Our results suggest that N fertilization concurrently inhibits soil PNR, but this inhibition can be alleviated by straw returning, particularly with low nitrogen fertilization (p < 0.05). Long-term N fertilization significantly decreased the abundance of ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB), and complete ammonia-oxidizing bacteria cladeB (CAOB-cladeB). Straw returning increased AOA abundance and diversity, especially with low or no fertilization (p < 0.05). Furthermore, the partial least squares path model demonstrated that AOA abundance affected soil PNR by altering the AOA community. According to random forest analysis, soil pH and AOA beta diversity were the primary factors affecting soil PNR (explaining 10.76% and 10.03% of the variation, respectively). Overall, our findings highlight the importance of straw returning and AOA in soil nitrification under long-term nitrogen fertilization, emphasizing the need to consider these interactions for sustainable agriculture. [ABSTRACT FROM AUTHOR]

In grassland ecosystems, aboveground biomass (AGB) is critical for energy flow, biodiversity maintenance, carbon storage, climate regulation, and livestock husbandry. Particularly on the climate-sensitive Tibetan Plateau, accurate AGB monitoring is crucial for assessing large-scale grassland livestock capacity. Previous studies focused on predicting AGB mainly at the plant community level and from the perspective of dry weight (AGBd). This study aims to predict grassland AGB in Xizang at both the plant taxonomic group (sedge, graminoid, forb) and community levels, from both an AGBd and a fresh weight (AGBf) perspective. Three to four independent variables (growing mean temperature, total precipitation, total radiation and NDVImax, maximum normalized difference vegetation index) were used for AGB prediction using nine models in Xizang grasslands. The random forest model (RFM) showed the greatest potential in simulating AGB (training R2 ≥ 0.62, validation R2 ≥ 0.87). This could be due to the nonlinear relationships between AGB, meteorological factors, and NDVImax. The RFM exhibited robustness against outliers and zero values resulting from taxonomic groups that were absent from the quadrats. The accuracies of the RFM were different between fresh and dry weight, and among the three taxonomic groups. The RFM's use of fewer variables can reduce complexity and costs compared to previous studies. Therefore, the RFM emerged as the optimal model among the nine models, offering potential for large-scale investigations into grassland AGB, especially for analyzing spatiotemporal patterns of plant taxonomic groups. [ABSTRACT FROM AUTHOR]

Net photosynthetic rate (Pn) is a common indicator used to measure the efficiency of photosynthesis and growth conditions of plants. In this study, soybeans under different moisture gradients were selected as the research objects. Fourteen vegetation indices (VIS) and five canopy structure characteristics (CSC) (plant height (PH), volume (V), canopy cover (CC), canopy length (L), and canopy width (W)) were obtained using an unmanned aerial vehicle (UAV) equipped with three different sensors (visible, multispectral, and LiDAR) at five growth stages of soybeans. Soybean Pn was simultaneously measured manually in the field. The variability of soybean Pn under different conditions and the trend change of CSC under different moisture gradients were analysed. VIS, CSC, and their combinations were used as input features, and four machine learning algorithms (multiple linear regression, random forest, Extreme gradient-boosting tree regression, and ridge regression) were used to perform soybean Pn inversion. The results showed that, compared with the inversion model using VIS or CSC as features alone, the inversion model using the combination of VIS and CSC features showed a significant improvement in the inversion accuracy at all five stages. The highest accuracy (R2 = 0.86, RMSE = 1.73 µmol m−2 s−1, RPD = 2.63) was achieved 63 days after sowing (DAS63). [ABSTRACT FROM AUTHOR]

In a study aimed at developing a rapid and nondestructive method for testing the quality of strawberries, spectral data from four strawberry varieties at different ripening stages were collected using a geophysical spectrometer, primarily focusing on the 350–1800 nm band. The spectra were preprocessed using Savitzky–Golay (SG) filtering, and characteristic bands were extracted using Pearson correlation coefficient (PCC) analysis. Models for predicting strawberry quality were built using random forest (RF), support vector machine (SVM), partial least squares (PLS), and Gaussian regression (GPR). The results indicated that the SVM model exhibited relatively high accuracy in predicting anthocyanin, hardness, and soluble solids content in strawberries. For the test set, the SVM model achieved R2 and RMSE values of 0.81, 0.87, and 0.89, and 0.04 mg/g, 0.33 kg/cm2, and 0.72%, respectively. Additionally, the PLS model demonstrated relatively high accuracy in predicting the titratable acid content of strawberries, achieving R2 and RMSE values of 0.85 and 0.03%, respectively, for the test set. These findings provided a solid foundation for strawberry quality modeling and a veritable guide for non-destructive assessment of strawberry quality. [ABSTRACT FROM AUTHOR]

Land degradation severely affects soil functions, thereby weakening crop productivity. However, the quantitative effects of the soil organic matter (SOM) composition and soil microbial properties on maize yield remain unclear under different levels of land degradation. Here, a gradient of land degradation was selected for sampling in the Horqin Sandy Land in northeast China. The results show that the relative abundances of aliphatic and aromatic groups decreased by 21.51% and 86.01% with increasing land degradation, respectively, and the considerable increase in polysaccharide groups led to a decrease in stability and resistance to SOM decomposition. Microbial properties, such as microbial biomass carbon, basic respiration, qCO2, and enzyme activities, decreased as a result of land degradation. The phospholipid fatty acid content and the ratio between bacteria and fungi markedly decreased with an increasing level of land degradation, and the ratio of G+ to G− markedly increased. Correlation analysis confirmed that the microbial properties were significantly related to the SOM composition, and the random forest model indicated that fungi were key factors affecting maize yield (7.15%, p < 0.05). Moreover, the results of the structure equation modelling revealed that land degradation reduced the soil physiochemical properties, resulting in a decrease in microorganisms, causing variations in the SOM composition and directly leading to a decrease in crop yield. The effect of microorganisms (β = 0.84 ***) on maize yield was greater than that of the SOM composition (β = 0.53 **). Our investigation can provide a theoretical basis for the conservation of the cropland in Horqin Sandy Land. [ABSTRACT FROM AUTHOR]

In order to efficiently detect total nitrogen content in Korla fragrant pear leaves, near-infrared spectroscopy technology was utilized to develop a detection model. The collected spectra underwent various preprocessing techniques including first-order derivative, second-order derivative, Savitzky–Golay + second-order derivative, multivariate scattering correction, multivariate scattering correction + first-order derivative, and standard normal variable transformation + second-order derivative. A competitive adaptive reweighted sampling algorithm was employed to extract characteristic wavelengths, and a prediction model for the total nitrogen content of fragrant pear leaves was established by combining the random forest algorithm, genetic algorithm-based random forest algorithm, radial basis neural network algorithm, and extreme learning machine algorithm. The study found that spectral preprocessing of SNV + SD along with the radial basis neural network algorithm yielded better predictions for total nitrogen content of fragrant pear leaves. The validation set results showed an R2 of 0.8547, RMSE of 0.291%, and RPD of 2.699. Therefore, the SNV + SD + CARS + RBF algorithm combination model proved to offer optimal comprehensive performance in predicting the total nitrogen content of fragrant pear leaves. [ABSTRACT FROM AUTHOR]

Non-destructive, fast, and accurate prediction of soil organic matter content in farmland is of great significance for soil fertility assessment and rational fertilization. In the process of soil organic matter prediction, it is important to give full play to the advantages of different prediction models and to integrate different prediction models to innovatively construct a combined prediction model of soil organic matter content so as to improve the prediction accuracy and generalization ability of the model. In this study, the soil organic matter content of agricultural soils was taken as the research object, and the visible near-infrared hyperspectral curves of soils were measured by the Starter Kit indoor mobile scanning platform (Headwall Photonics, Bolton, MA, USA), and the original spectral curves were firstly de-noised by Savitzky–Golay (S-G) smoothing. Secondly, the smoothed and denoised spectral data were subjected to a first-order differential transform, and the features were selected based on the first-order differential transformed spectral data using the L1-paradigm algorithm features. Then, secondly, eight algorithms based on the selected feature bands, such as LASSO Regression (LASSO) (Model 1), Multilayer Perceptron (MLP) (Model 2), Random Forest (RF) (Model 3), Gaussian Kernel Regression (GKR) (Model 4), Ridge Regression (Model 5), Long Short-Term Memory (LSTM) (Model 6), Convolutional Neural Networks (CNN) (Model 7), and Support Vector Regression (SVR) (Model 8), were applied to construct a single-prediction model of soil organic matter content. Finally, a superior linear combination-prediction model was proposed by the eight single-prediction models constructed, and the standard deviation-based prediction validity was added to test the model. The results showed the following: (1) the weights of the eight single-prediction models in the combined prediction model were ω 1 * = 0.099 , ω 2 * = 0.202 , ω 3 * = 0.000 , ω 4 * = 0.357 , ω 5 * = 0.088 , ω 6 * = 0.089 , ω 7 * = 0.000 , ω 8 * = 0.165 , respectively; (2) The average precision E of the predicted values of soil organic matter content constructed based on the eight single-prediction models was 0.856; the average standard deviation σ was 0.181, and the average prediction validity M was 0.702; (3) The accuracy E of the predicted value of soil organic matter content of the combined model was 0.893, which was 4.322% higher than the average accuracy of the single model; the standard deviation of the combined model was 0.129, which was 28.333% lower than the average standard deviation of the single model, and the prediction validity M of the combined model was 0.778, which was 10.826% higher than the average prediction validity of the single model. The combined model can be used for the effective estimation of soil organic matter content in farmland with visible near-infrared spectral data, which can provide a basis and reference for the rapid detection of soil organic matter content in farmland. [ABSTRACT FROM AUTHOR]

In the cultivation of maize, the leaf area index (LAI) serves as an important metric to determine the development of the plant. Unmanned aerial vehicles (UAVs) that capture RGB images, along with random forest regression (RFR), can be used to indirectly measure LAI through vegetative indices. Research using these techniques is at an early stage, especially in the context of maize for silage. Therefore, this study aimed to evaluate which vegetative indices have the strongest correlations with maize LAI and to compare two regression methods. RFR, ridge regression (RR), support vector machine (SVM), and multiple linear regression (MLR) regressions were performed in Python for comparison using images obtained in an area cultivated with maize for silage. The results showed that the RGB spectral indices showed saturation when the LAI reached 3 m2 m−2, with the VEG (vegetable index), COM (combination), ExGR (red–green excess), and TGI (triangular greenness index) indices selected for modeling. In terms of regression, RFR showed superior performance with an R2 value of 0.981 and a root mean square error (RMSE) of 0.138 m2 m−2. Therefore, it can be concluded that RFR using RGB indices is a good way to indirectly obtain the LAI. [ABSTRACT FROM AUTHOR]

Rice–duckweed coculturing as an advanced technique has proven effective for weed control. However, the complex environmental interactions underlying its effectiveness remain unclear. In this study, a controlled pot experiment was conducted to isolate the shading effect of duckweeds (Landoltia punctata and Spirodela polyrhiza) from their intricate environmental interactions with paddy weeds (Cyperus difformis, Eclipta prostrata, and Ammannia auriculata). The results revealed that as the duckweeds grew, there were notable decreases in total weed density (52.41–77.01%) and biomass (42.33–99.23%) while rice biomass remained unaffected. Duckweed covering allowed less than 8.64% of light to pass through and led to decreases in environmental temperature, pH, and dissolved oxygen concentration in the floodwater, but had little impact on inorganic nitrogen levels. Through screening with random forest analysis and structural equation modelling, it was found that weed density and biomass were predominantly influenced by the reduced light transmittance (54.1% and 77.2%, p ≤ 0.001) resulting from duckweed covering while the decline in dissolved oxygen content partly influenced weed density (18%). In conclusion, this study's results bring a different perspective, confirming the sunlight-shading effect of duckweed as a critical mechanism significantly inhibiting the germination and growth of weeds. These findings provide a scientific basis for the improved application of ecological weed control methods in paddy fields. [ABSTRACT FROM AUTHOR]

The traditional approach of prescribing phosphate fertilizer solely based on soil test P (STP) has faced criticism from scientists and agriculturists pushing farmers to seek phosphate fertilization models that incorporate additional factors. By embracing integrated approaches, farmers can receive more precise recommendations that align with their specific conditions and fertilization techniques. This study aimed to utilize artificial intelligence prediction to replicate soybean response curves to fertilizer by integrating edaphic and climatic factors. Literature data on soybean response to P fertilization were collected, and the Random Forest (RF) algorithm was applied to predict response curves. The predictions utilized seven predictors: P dose, STP, soil pH, texture, % OM, precipitation, and P application methods. These predictions were compared to the traditional STP-based approach. The STP-based P prescription models exhibited extremely low robustness values (R2) of 1.53% and 0.88% for the PBray-1 and POlsen diagnostic systems, respectively. In contrast, implementing the RF algorithm allowed for more accurate prediction of yield gains at various P doses, achieving robustness values of 87.4% for the training set and 60.9% for the testing set. The prediction errors remained below 10% throughout the analysis. Implementing artificial intelligence modeling enabled the study to achieve precise predictions of the optimal P dose and customized fertilization recommendations tailored to farmers' specific soil conditions, climate, and individual fertilization practices. [ABSTRACT FROM AUTHOR]

Soil moisture is a key environmental variable. There is a lack of software to facilitate non-specialists in estimating and analyzing soil moisture at the field scale. This study presents a new open-sourced R package mlhrsm, which can be used to generate Machine Learning-based high-resolution (30 to 500 m, daily to monthly) soil moisture maps and uncertainty estimates at selected sites across the contiguous USA at 0–5 cm and 0–1 m. The model is based on the quantile random forest algorithm, integrating in situ soil sensors, satellite-derived land surface parameters (vegetation, terrain, and soil), and satellite-based models of surface and rootzone soil moisture. It also provides functions for spatial and temporal analysis of the produced soil moisture maps. A case study is provided to demonstrate the functionality to generate 30 m daily to weekly soil moisture maps across a 70-ha crop field, followed by a spatial–temporal analysis. [ABSTRACT FROM AUTHOR]

Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R2 = 0.835, RMSE = 14.357; for LAI, R2 = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training. [ABSTRACT FROM AUTHOR]

Climate change research identifies risks to agriculture that will impact agricultural land suitability. To mitigate these impacts, agricultural growing regions will need to adapt, diversify, or shift in location. Various machine learning algorithms have successfully modelled agricultural land suitability globally, predominantly using climate and soil features. Topography controls many of the environmental processes that impact agriculture, including soils, hydrology, and nutrient availability. This research evaluated the relationship between specialty crops and topography using land-surface parameters extracted from a 30 m DEM, soil features, and specialty crop presence/absence data derived from eight years of previous land classifications in southern Ontario, Canada. Using random forest, a model was developed for each specialty crop where feature permutation importance, Matthew's correlation coefficient, and the area under the precision-recall curve was calculated. Elevation relative to watershed minimum and maximum, direct radiation on Day 172, and spherical standard deviation of normals were identified as the mean most important topographic features across all models and beet crops were found to have the highest association with topographic features. These results identify locations of agricultural expansion opportunities if climate becomes more favourable. The importance of topography in addition to climate and soils when identifying suitable areas for specialty crops is also highlighted. [ABSTRACT FROM AUTHOR]

Changes occurring because of human activity in protected natural places require constant monitoring of land use (LU) structures. Therefore, Korgalzhyn District, which occupies part of the Korgalzhyn State Natural Reserve territory, is of considerable interest. The aim of these studies was to analyze changes in the composition of the land use/land cover (LULC) of Korgalzhyn District from 2010 to 2021 and predict LU transformation by 2030 and 2050. Landsat image classification was performed using Random Forest on the Google Earth Engine. The combined CA-ANN model was used to predict LULC changes by 2030 and 2050, and studies were carried out using the MOLUSCE plugin. The results of these studies showed that from 2010 to 2021, there was a steady increase in the share of ploughable land and an adequate reduction in grassland. It is established that, in 2030 and 2050, this trend will continue. At the same time, there will be no drastic changes in the composition of other land classes. The obtained results can be helpful for the development of land management plans and development policies for the Korgalzhyn District. [ABSTRACT FROM AUTHOR]

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy's original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model's accuracy follows the order: 1.5 order > 2.0 order > 1.0 order > 0.5 order > original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF > SVM > BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models. [ABSTRACT FROM AUTHOR]

The prompt and precise identification of corn and soybeans are essential for making informed decisions in agricultural production and ensuring food security. Nonetheless, conventional crop identification practices often occur after the completion of crop growth, lacking the timeliness required for effective agricultural management. To achieve in-season crop identification, a case study focused on corn and soybeans in the U.S. Corn Belt was conducted using a crop growth curve matching methodology. Initially, six vegetation indices datasets were derived from the publicly available HLS product, and then these datasets were integrated with known crop-type maps to extract the growth curves for both crops. Furthermore, crop-type information was acquired by assessing the similarity between time-series data and the respective growth curves. A total of 18 scenarios with varying input image numbers were arranged at approximately 10-day intervals to perform identical similarity recognition. The objective was to identify the scene that achieves an 80% recognition accuracy earliest, thereby establishing the optimal time for early crop identification. The results indicated the following: (1) The six vegetation index datasets demonstrate varying capabilities in identifying corn and soybean. Among those, the EVI index and two red-edge indices exhibit the best performance, all surpassing 90% accuracy when the entire time-series data are used as input. (2) EVI, NDPI, and REVI2 indices can achieve early identification, with an accuracy exceeding 80% around July 20, more than two months prior to the end of the crops' growth periods. (3) Utilizing the same limited sample size, the early crop identification method based on crop growth curve matching outperforms the method based on random forest by approximately 20 days. These findings highlight the considerable potential and value of the crop growth curve matching method for early identification of corn and soybeans, especially when working with limited samples. [ABSTRACT FROM AUTHOR]

The accurate assessment of nitrogen (N) status is important for N management and yield improvement. The N status in plants is affected by plant densities and N application rates, while the methods for assessing the N status in drip-irrigated cotton under reduced nitrogen treatment and different plant densities are lacking. Therefore, this study was conducted with four different N treatments (195.5, 299, 402.5, and 506 kg N ha−1) and three sowing densities (6.9 × 104, 13.8 × 104, and 24 × 104 plants ha−1) by using a low-cost Unmanned Aerial Vehicle (UAV) system to acquire RGB imagery at a 10 m flight altitude at cotton main growth stages. We evaluated the performance of different ground resolutions (1.3, 2.6, 5.2, 10.4, 20.8, 41.6, 83.2, and 166.4 cm) for image textures, vegetation indices (VIs), and their combination for leaf N concentration (LNC) estimation using four regression methods (stepwise multiple linear regression, SMLR; support vector regression, SVR; extreme learning machine, ELM; random forest, RF). The results showed that combining VIs (ExGR, GRVI, GBRI, GRRI, MGRVI, RGBVI) and textures (VAR, HOM, CON, DIS) yielded higher estimation accuracy than using either alone. Specifically, the RF regression models had a higher accuracy and stability than SMLR and the other two machine learning algorithms. The best accuracy (R2 = 0.87, RMSE = 3.14 g kg−1, rRMSE = 7.00%) was obtained when RF was applied in combination with VIs and texture. Thus, the combination of VIs and textures from UAV images using RF could improve the estimation accuracy of drip-irrigated cotton LNC and may have a potential contribution in the rapid and non-destructive nutrition monitoring and diagnosis of other crops or other growth parameters. [ABSTRACT FROM AUTHOR]

Determining the amount of nitrogen (N) and phosphorus (P) discharged into lakes and assessing the effectiveness of reduction measures in a basin that involve the interaction of multiple factors are still daunting challenges. In this study, the random forest (RF) model was employed to simulate the impact of controlling measures on the amount of N and P discharged in 2025 under seven specific reduction scenarios. Discharged N and P decreased in the basin by 23.38% and 31.69% from 2011 to 2020, respectively. The N and P nutrient discharge intensities were significantly higher in the western part of the basin (13.31 kg·ha−1·a−1 and 1.34 kg·ha−1·a−1) than those in the eastern region (10.24 kg·ha−1·a−1 and 0.74 kg·ha−1·a−1). Fertilizer runoff (N: 34.72%, 5934.49 t; P: 13.60%, 199.76 t), domestic sewage (N: 29.14%, 4009.27 t; P: 34.84%, 496.59 t), and livestock farming (N: 15.11%, 2657.50 t; P: 40.05%, 616.05 t) were the key sources of N and P. The RF model shows that (R2 > 0.994, p < 0.01) the multi-factor reduction effect is the best, and under this discharge reduction effect, the amount of N and P discharged in 2025 are expected to decrease by 13.79% and 19.42%, respectively, compared with those in 2020. In addition, different key sources in sub-basins might lead to regional differences in the discharge reduction effects of various measures. Ultimately, we recommend that the synergistic treatment of point and non-point sources, using treatments with multiple measures, should be implemented in different regions to reduce the amount of N and P discharged in the Chaohu Lake Basin. [ABSTRACT FROM AUTHOR]

The Leaf Area Index (LAI) is a crucial indicator of crop photosynthetic potential, which is of great significance in farmland monitoring and precision management. This study aimed to predict potato plant LAI for potato plant growth monitoring, integrating spectral, textural, and morphological data through UAV images and machine learning. A new texture index named VITs was established by fusing multi-channel information. Vegetation growth features (Vis and plant height Hdsm) and texture features (TIs and VITs) were obtained from drone digital images. Various feature combinations (VIs, VIs + TIs, VIs + VITs, VIs + VITs + Hdsm) in three growth stages were adopted to monitor potato plant LAI using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), random forest (RF), and eXtreme gradient boosting (XGBoost), so as to find the best feature combinations and machine learning method. The performance of the newly built VITs was tested. Compared with traditional TIs, the estimation accuracy was obviously improved for all the growth stages and methods, especially in the tuber-growth stage using the RF method with 13.6% of R2 increase. The performance of Hdsm was verified by including it either as one input feature or not. Results showed that Hdsm could raise LAI estimation accuracy in every growth stage, whichever method is used. The most significant improvement appeared in the tuber-formation stage using SVR, with an 11.3% increase of R2. Considering both the feature combinations and the monitoring methods, the combination of VIs + VITs + Hdsm achieved the best results for all the growth stages and simulation methods. The best fitting of LAI in tuber-formation, tuber-growth, and starch-accumulation stages had an R2 of 0.92, 0.83, and 0.93, respectively, using the XGBoost method. This study showed that the combination of different features enhanced the simulation of LAI for multiple growth stages of potato plants by improving the monitoring accuracy. The method presented in this study can provide important references for potato plant growth monitoring. [ABSTRACT FROM AUTHOR]

Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method to address this issue, using multi-source data and object features to achieve multi-crop classification in mountainous areas. Firstly, a deep learning method was employed to extract cropland field parcels in mountainous areas. Subsequently, the fusion of multi-source data was carried out based on cropland field parcels, while object features tailored for mountainous crops were designed for crop classification. Comparative analysis indicates that the proposed classification method demonstrates exceptional performance, enabling accurate mapping of various crops in mountainous regions. The F1 score and overall accuracy (OA) of the proposed method are 0.8449 and 0.8502, representing a 10% improvement over the pixel-based random forest classification results. Furthermore, qualitative analysis reveals that the proposed method exhibits higher classification accuracy for smaller plots and more precise delineation of crop boundaries. Finally, meticulous crop mapping of corn, sorghum, rice, and other crops in Xishui County, Guizhou Province, demonstrates the significant potential of the proposed method in crop classification within mountainous scenarios. [ABSTRACT FROM AUTHOR]

The high salt content in saline–alkaline land leads to insufficient nutrients, thereby reducing agricultural productivity. This has sparked widespread interest in improving saline–alkaline soil. In this investigation, 16S rRNA gene high-throughput sequencing was employed to examine the impacts of three cropping systems (monoculture, rotation, and mixture) on soil bacterial communities. It was found that cropping rotations and mixtures significantly increased soil bacterial α-diversity. Random forest analysis showed a significant linear relationship between AK and EC and bacterial α-diversity. In addition, principal coordinates analysis (PCoA) further confirmed the significant differences in β-diversity between different soil layers. Through co-occurrence network analysis, it was found that cropping rotations and mixtures increased the stability and complexity of co-occurrence networks. By calculating NST to analyze the assembly process of soil bacterial communities in different cropping systems, it was found that the assembly process of soil bacterial communities was dominated by a stochastic process. Functional prediction results showed that a large number of C, N, and S cycling microbes appeared in soil bacterial communities. Our study aims to establish a fresh perspective on the improvement and recovery of saline–alkaline soil. [ABSTRACT FROM AUTHOR]

Cultivated land quality is an essential measure of cultivated land production capability. Establishing a cultivated land quality inversion model based on high-resolution remote sensing data provides a scientific basis for regional cultivated land resource management and sustainable utilization. Utilizing field survey data, cultivated land quality evaluation data, and high-resolution remote sensing data, a spectral index-cultivated land quality model was constructed and optimized with the machine learning method, and cultivated land quality inversion and verification in Chuzhou City in 2021 were carried out. The results showed that the distribution of cultivated land quality in the study area depicted with the remote sensing inversion model based on random forest was consistent with the actual cultivated land quality. Although the accuracy of the SVT-CLQ inversion model established using four spectral indices is slightly lower than that of the MSVT-CLQ group established using 15 indices, it can still accurately reflect the distribution of cultivated land quality in the study area. Compared with the two models of the MSVT-CLQ and SVT-CLQ groups, the field survey data of sampling points is reduced, the time and energy of field sampling and analysis are correspondingly saved, the efficiency of cultivated land quality evaluation is improved, and the dynamic monitoring and rapid evaluation of cultivated land quality are realized. [ABSTRACT FROM AUTHOR]

Wheat is one of the most important food crops in the world, and its high and stable yield is of great significance for ensuring food security. Timely, non-destructive, and accurate monitoring of wheat growth information is of great significance for optimizing cultivation management, improving fertilizer utilization efficiency, and improving wheat yield and quality. Different color indices and vegetation indices were calculated based on the reflectance of the wheat canopy obtained by a UAV remote sensing platform equipped with a digital camera and a hyperspectral camera. Three variable-screening algorithms, namely competitive adaptive re-weighted sampling (CARS), iteratively retains informative variables (IRIVs), and the random forest (RF) algorithm, were used to screen the acquired indices, and then three regression algorithms, namely gradient boosting decision tree (GBDT), multiple linear regression (MLR), and random forest regression (RFR), were used to construct the monitoring models of wheat aboveground biomass (AGB) and leaf nitrogen content (LNC), respectively. The results showed that the three variable-screening algorithms demonstrated different performances for different growth indicators, with the optimal variable-screening algorithm for AGB being RF and the optimal variable-screening algorithm for LNC being CARS. In addition, using different variable-screening algorithms results in more vegetation indices being selected than color indices, and it can effectively avoid autocorrelation between variables input into the model. This study indicates that constructing a model through variable-screening algorithms can reduce redundant information input into the model and achieve a better estimation of growth parameters. A suitable combination of variable-screening algorithms and regression algorithms needs to be considered when constructing models for estimating crop growth parameters in the future. [ABSTRACT FROM AUTHOR]

As global warming continues, heat stress events are expected to increase and negatively affect rice production. Spatiotemporal changes in single-season rice exposure to heat stress were explored along the middle and lower reaches of the Yangtze River (MLRYR) in China during 1971–2020 based on created heat thresholds in different phenological stages, derived from comparison of historical heat records for single-season rice and agro-meteorological data. The feature importance (IF) provided by the Random Forest model was used to modulate the relationship between threshold accumulated temperature and yield reduction rate caused by heat stress. In addition, critical temperature thresholds at different phenological stages were determined by combining Overall Accuracy and the Receiver Operating Characteristic (ROC) curve. According to historical disaster records, the heat stress occurred before the reproductive phase (i.e., the tillering–jointing stage) and ended in the filling stage. Critical temperature thresholds of Tmax at tillering–jointing, booting, flowering and filling were quantified as 36, 35, 35 and 38 °C, with higher IF values of 13.14, 10.93, 17.15 and 13.15, respectively. The respective values of Overall Accuracy and the areas under the ROC curve were greater than 0.85 and 0.930, implying that each threshold performed excellently in identifying heat occurrence. Based on the determined critical thresholds, accumulated harmful temperature (Tcum), number of heat days (HD), first heat date (FHD) and last heat date (LHD) were presented to characterize heat exposure. It was clear that Tcum and HD exhibited a north-to-south increasing trend from 1971 to 2020, with the obvious increasing occurrence in most parts of the study region through the period of 2010 to 2020. FHD occurred earlier in most stations except the northeast parts, while LHD ended later in southern MLRYR. Exploring heat critical thresholds at different phenological stages highlighted in this study can help decision-makers monitor and evaluate heat exposure to single-season rice in MLRYR and further develop mitigation strategies to ensure rice production security. [ABSTRACT FROM AUTHOR]

This study employed an ensemble machine learning approach to evaluate the effect of bioclimatic covariates on the prediction accuracy of soil total carbon (TC) in the Pannonian biogeoregion. The analysis involved two main segments: (1) evaluation of base environmental covariates, including surface reflectance, phenology, and derived covariates, compared to the addition of bioclimatic covariates; and (2) assessment of three individual machine learning methods, including random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM), as well as their ensemble for soil TC prediction. Among the evaluated machine learning methods, the ensemble approach resulted in the highest prediction accuracy overall, outperforming the individual models. The ensemble method with bioclimatic covariates achieved an R2 of 0.580 and an RMSE of 10.392, demonstrating its effectiveness in capturing complex relationships among environmental covariates. The results of this study suggest that the ensemble model consistently outperforms individual machine learning methods (RF, XGB, and SVM), and adding bioclimatic covariates improves the predictive performance of all methods. The study highlights the importance of integrating bioclimatic covariates when modeling environmental covariates and demonstrates the benefits of ensemble machine learning for the geospatial prediction of soil TC. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is one important agrometeorological parameter for hydrological studies and climate risk zoning. ET0 calculation by the FAO Penman–Monteith method requires several input data. However, the availability of climate data has been a problem in many places around the world, so the study of scenarios with different combinations of climate data has become essential. The aim of this study was to evaluate the performance of artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR) approaches to estimate monthly mean ET0 with different input data combinations and scenarios. Three scenarios were evaluated: at the state level, where all climatological stations were used (Scenario I–SI), and at the regional level, where the Minas Gerais state was divided according to the climatic classifications of Thornthwaite (Scenario II–SII) and Köppen (Scenario III–SIII). ANN and RF performed better in ET0 estimation among the models evaluated in the SI, SII, and SIII scenarios with the following data combinations: (i) latitude, longitude, altitude, month, mean, maximum and minimum temperature, and relative humidity and (ii) latitude, longitude, altitude, month, mean temperature, and relative humidity. SVM and MLR models are recommended for all scenarios in situations with limited climatic data where only air temperature and relative humidity data are available. The results and information presented in this study are important for the agricultural chain and water resources in Minas Gerais state. [ABSTRACT FROM AUTHOR]

The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R2 values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R2 and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band's range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium. [ABSTRACT FROM AUTHOR]

Fruit maturity is a crucial index for determining the optimal harvesting period of open-field loofah. Given the plant's continuous flowering and fruiting patterns, fruits often reach maturity at different times, making precise maturity detection essential for high-quality and high-yield loofah production. Despite its importance, little research has been conducted in China on open-field young fruits and vegetables and a dearth of standards and techniques for accurate and non-destructive monitoring of loofah fruit maturity exists. This study introduces a real-time detection and maturity classification method for loofah, comprising two components: LuffaInst, a one-stage instance segmentation model, and a machine learning-based maturity classification model. LuffaInst employs a lightweight EdgeNeXt as the backbone and an enhanced pyramid attention-based feature pyramid network (PAFPN). To cater to the unique characteristics of elongated loofah fruits and the challenge of small target detection, we incorporated a novel attention module, the efficient strip attention module (ESA), which utilizes long and narrow convolutional kernels for strip pooling, a strategy more suitable for loofah fruit detection than traditional spatial pooling. Experimental results on the loofah dataset reveal that these improvements equip our LuffaInst with lower parameter weights and higher accuracy than other prevalent instance segmentation models. The mean average precision (mAP) on the loofah image dataset improved by at least 3.2% and the FPS increased by at least 10.13 f/s compared with Mask R-CNN, Mask Scoring R-CNN, YOLACT++, and SOLOv2, thereby satisfying the real-time detection requirement. Additionally, a random forest model, relying on color and texture features, was developed for three maturity classifications of loofah fruit instances (M1: fruit setting stage, M2: fruit enlargement stage, M3: fruit maturation stage). The application of a pruning strategy helped attain the random forest model with the highest accuracy (91.47% for M1, 90.13% for M2, and 92.96% for M3), culminating in an overall accuracy of 91.12%. This study offers promising results for loofah fruit maturity detection, providing technical support for the automated intelligent harvesting of loofah. [ABSTRACT FROM AUTHOR]

Effective in-season crop nitrogen (N) status diagnosis is important for precision crop N management, and remote sensing using an unmanned aerial vehicle (UAV) is one efficient means of conducting crop N nutrient diagnosis. Here, field experiments were conducted with six N levels and six maize hybrids to determine the nitrogen nutrition index (NNI) and yield, and to diagnose the N status of the hybrids combined with multi-spectral data. The NNI threshold values varied with hybrids and years, ranging from 0.99 to 1.17 in 2018 and 0.60 to 0.71 in 2019. A proper agronomic optimal N rate (AONR) was constructed and confirmed based on the measured NNI and yield. The NNI (R2 = 0.64–0.79) and grain yield (R2 = 0.70–0.73) were predicted well across hybrids using a random forest model with spectral, structural, and textural data (UAV). The AONRs calculated using the predicted NNI and yield were significantly correlated with the measured NNI (R2 = 0.70 and 0.71 in 2018 and 2019, respectively) and yield (R2 = 0.68 and 0.54 in 2018 and 2019, respectively). It is concluded that data fusion can improve in-season N status diagnosis for different maize hybrids compared to using only spectral data. [ABSTRACT FROM AUTHOR]

To solve the problem that the parameters of the multiple-input multiple-output (MIMO) irrigation system are difficult to control accurately, an on-demand irrigation control experimental device was developed. The main input parameters of the device are the opening degree of the main pipe valve, the opening frequency of and the pump station, the opening degree of the branch pipeline valve with the different combinations of different opening degrees. Based on these input parameters, four types of experimental methods were designed, and a total of 1695 groups of experiments were designed. The results show that the different opening degree combinations of the branch electric valve cannot significantly affect the flow of the branch pipeline but also significantly affect the pressure of the main pipeline. The prediction error of the operating frequency of the pump station and the opening degree of the branch valve were regarded as the objective function. Six intelligent prediction models were constructed, which are Back Propagation (BP), support vector regression (SVR) Linear, SVR-RBF, SVR-Poly, random forest (RF) and eXtreme Gradient Boosting (XGBoost), respectively. The results show that the XGBoost is the best model among the six models. For the opening degree of three branch valves, the mean absolute error (MAE) between the predicted value and actual value is less than 3.3%, the mean square deviation (RMSE) between the predicted values and actual values is less than 4.5%, and the R2 of between the predicted values and actual values is greater than 0.990. The control models and system can meet the needs of an on-demand irrigation system. [ABSTRACT FROM AUTHOR]

In-season crop-type maps are required for a variety of agricultural monitoring and decision-making applications. The earlier the crop type maps of the current growing season are obtained, the more beneficial it is for agricultural decision-making and management. With the availability of a large amount of high spatiotemporal resolution remote sensing data, different data sources are expected to increase the frequency of data acquisition, which can provide more information in the early season. To explore the potential of integrating different data sources, a Dual-1DCNN algorithm was built based on the CNN model in this study. Moreover, an incremental training method was used to attain the network on each data acquisition date and obtain the best detection date for each crop type in the early season. A case study for Hengshui City in China was conducted using time series of Sentinel-1A (S1A) and Sentinel-2 (S2) attained in 2019. To verify this method, the classical methods support vector machine (SVM), random forest (RF), and Mono-1DCNN were implemented. The input for SVM and RF was S1A and S2 data, and the input for Mono-1DCNN was S2 data. The results demonstrated the following: (1) Dual-1DCNN achieved an overall accuracy above 85% at the earliest time.; (2) all four types of models achieved high accuracy (F1s were greater than 90%) on summer maize after sowing one month later; (3) for cotton and common yam rhizomes, Dual-1DCNN performed best, with its F1 reaching 85% within 2 months after cotton sowing, 15 days, 20 days, and 45 days ahead of Mono-1DCNN, SVM, and RF, respectively, and its extraction of the common yam rhizome was achieved 1–2 months earlier than other methods within the acceptable accuracy. These results confirmed that Dual-1DCNN offered significant potential in the in-season detection of crop types. [ABSTRACT FROM AUTHOR]

Farmland is the basis for human survival and development. The phenomenon of cropland abandonment has seriously affected national agricultural production and food security. In this study, remote sensing monitoring of abandoned cropland is carried out based on multisource time series remote sensing data using the Google Earth Engine (GEE) cloud platform. Landsat and Sentinel-2 time series data from 2010–2021 were used to obtain monthly synthetic cloud-free image sets in combination with cropland plot data. The time series farmland probability dataset was generated using the random forest classification method. The LandTrendr algorithm was used to extract and analyse the time series cropland probability dataset. Finally, this study also explored the drivers of change in abandoned cropland in Fujian Province. The results show that (1) the LandTrendr algorithm can effectively extract abandoned farmland and avoid the impact of pseudovariation resulting from non-farmland categories. A total of 87.02% of the abandoned farmland was extracted in 2018; 87.50% of the abandoned farmland was extracted in 2020. (2) The abandoned area in Fujian Province fluctuated after a significant increase in 2012, with the abandoned area exceeding 30 thousand hectares. Since 2017, the abandoned area has decreased to slightly below 30 thousand hectares. (3) The regression results of the factors affecting abandoned cropland in Fujian Province show that the increase in the number of agricultural workers and the improvement in soil organic matter content will significantly reduce the area of abandoned cropland in Fujian Province, while the increase in the rate of urbanization, poor road accessibility, and insufficient irrigation conditions will increase the area of abandoned cropland. The results of this study are useful for conducting surveys of cropland abandonment and obtaining timely and accurate data on cropland abandonment. The results of this study are of great significance for the development of effective measures to stop the abandonment of cropland, and ensure the implementation of food security strategies. [ABSTRACT FROM AUTHOR]

The "Dangshan" pear woolliness response is a physiological disease that mostly occurs in the pear growth process. The appearance of the disease is not obvious, and it is difficult to detect with the naked eye. Therefore, finding a way to quickly and nondestructively identify "Dangshan" pear woolliness disease is of great significance. In this paper, the near-infrared spectral (NIR) data of "Dangshan" pear samples were collected at 900–1700 nm reflectance spectra using a handheld miniature NIR spectrometer, and the data were modelled and analysed using random forest (RF), support vector machine (SVM) and boosting algorithms under the processing of 24 pretreatment methods. Considering the variations between different pretreatment methods, this work determined the relative optimality index of different pretreatment methods by evaluating their effects on model accuracy and Kappa and selected the best-performing first derivative with standard normal variate and Savitzky–Golay and first derivative with multiplicative scatter correction and Savitzky–Golay as the best pretreatment methods. With the best pretreatment method, all five models in the three categories showed good accuracy and stability after parameter debugging, with accuracy and F1 greater than 0.8 and Kappa floating at approximately 0.7, reflecting the good classification ability of the models and proving that near-infrared spectroscopy (NIRS) in the rapid identification of "Dangshan" pear woolliness response disease was feasible. By comparing the performance differences of the models before and after the pretreatment methods, it was found that the ensemble-learning models such as RF and boosting were more stringent on pretreatment methods in identifying "Dangshan" pear woolliness response disease than support vector machines, and the performance of the ensemble learning models was significantly improved under appropriate pretreatment methods. This experiment provided a relatively stable detection method for "Dangshan" pear woolliness response disease under nonideal detection conditions by analysing the impact of pretreatment methods and models on the prediction result. [ABSTRACT FROM AUTHOR]

In order to explore the drag reduction mechanism of pneumatic subsoiling and study the influence of pneumatic subsoiling on the soil, this study used machine learning models to predict the working resistance of a pneumatic subsoiler and adopted random forest (RF), error back-propagation (BP), eXtreme gradient boosting (XGBoost) and support vector regression (SVR) to analyze and compare the predictions of these four models. Field experiments were carried out in two fields with different bulk densities and moisture content. The effects of these parameters on the resistance of pneumatic subsoiling were studied by changing the working air pressure, depth and forward speed. In the RF, SVR, XGBoost and BP models, five parameters (working air pressure, working depth, forward speed, bulk density and moisture content) were inputted as independent variables, and the operating resistance of pneumatic subsoiling was used as the predicted value. After training the four models, the results showed that the R2 value of the RF model was the highest and the error was the smallest, which made it better than the SVR, XGBoost and BP models. The values of MAPE, R2 and RMSE for the RF model's test set were 0.01, 0.99, and 3.61 N, respectively, indicating that the RF model could predict the resistance value of subsoiling well. When the RF model was used to analyze the five input parameters, the experimental results showed that the contribution of working air pressure to reducing the resistance of subsoiling reached 29%, indicating that pneumatic subsoiling can reduce the resistance, drag and consumption. [ABSTRACT FROM AUTHOR]

Selenium (Se) is a beneficial microelement for humans, and its varying abundances and shortages have attracted widespread concern. The accumulation process of soil Se is quite complicated, being controlled by multiple factors. However, the influence mechanism of soil properties, climate, and topographic conditions on Se distribution is still unclear in Se-deficient areas. For this study, we collected 2804 samples from cropland soil to assess the levels of Se and the factors that influence those levels in Se-deficient areas of southwestern China. The Se content in this area (0.17 mg/kg) was less than the mean value of China as a whole (0.29 mg/kg). Moran's I index and a random forest (RF) model showed that higher Se levels were mostly observed in the southern and northern sections of the area we studied. The RF model had excellent performance in predicting soil Se content, with an accuracy of 64%. The use of Shapley additive explanations indicated that soil organic matter (SOM) and mean annual precipitation (MAP) were the critical factors determining Se distribution. The areas with high SOM and MAP showed high Se levels. The information obtained from this work can provide guidance for agricultural planning in Se-deficient areas. [ABSTRACT FROM AUTHOR]

The emitter is one of the most critical components in drip irrigation. The flow path geometry parameters have a significant effect on the emitter's hydraulic performance and have a direct impact on the emitter's irrigation uniformity and lifetime. The hydraulic characteristics of the emitter are the key indicators of its performance. However, obtaining the hydraulic characteristics of the emitter is complex. Typically, only a small number of calibrations are performed for specific equipment models, making it difficult to obtain the parameter. Therefore, limited data corresponding to the morphological parameters and the flow rate were simulated using the FLUENT software, and the influence of the characteristics was analyzeanalyzed, based on which a flow rate prediction model was constructed using the ensemble learning (CatBoost) model. The extended data set was generated by stochastic simulation and parameter fitting. The flow index and flow coefficient prediction model were built and evaluated using the CatBoost model again with the augmented data set as a benchmark. The results show that the significant correlation between the geometric structure and the flow index and flow coefficient provides the basis for the correlation model. CatBoost can fit the complex nonlinear relationships between the parameters well, achieving excellent simulation accuracy for the flow rate (R2 = 0.9987), flow index (R2 = 0.9961), and flow coefficient (R2 = 0.9946), where the path width has the highest importance score in the model construction for the flow index (score = 55.97) and flow coefficient (score = 45.2). Furthermore, the CatBoost models used in this study achieved the best prediction results compared to seven typical models (XGBoost, Bagging, Random Forest, Tree, Adaboost, and KNN). [ABSTRACT FROM AUTHOR]

While populations of the Asian chestnut gall wasp (Dryocosmus kuriphilus Yasumatsu), an invasive pest affecting the European chestnut (Castanea sativa Miller), have started to be controlled biologically, this pest still conditions chestnut tree development. With the aim of assessing plant health status as a means of monitoring gall wasp infestation, we used a field spectroradiometer to collect data from leaves taken from 83 trees in two chestnut orchards. We calculated characteristic spectral signatures for pest infestation, and after training and validation, developed classifiers to distinguish between different infestation levels. Several partial least square discriminant analysis (PLS-DA) and random forest (RF) models were fitted with reflectance and transformed values to obtain characteristic curves reflecting infestation. Four wavelengths (560 nm, 680 nm, 1400 nm, and 1935 nm) were identified as showing the greatest differences between curves. The best overall accuracy (69.23%) was achieved by an RF model fitted with reflectance transformed values. Lower overall accuracy (26.92%) was achieved in distinguishing between infestation levels. In conclusion, while more specific differences in infestation levels were not detectable, our method successfully discriminated between gall absence and presence. [ABSTRACT FROM AUTHOR]

The differentiation between the main crop and weeds is an important step for selective spraying systems to avoid agrochemical waste and reduce economic and environmental impacts. In this sense, this study aims to classify and map the area occupied by weeds, determine the percentage of area occupied, and indicate treatment control strategies to be adopted in the field. This study was conducted by using a yellow Bourbon cultivar (IAC J10) with 1 year of implementation on a commercial coffee plantation located at Minas Gerais, Brazil. The aerial images were obtained by a remotely piloted aircraft (RPA) with an embedded multispectral sensor. Image processing was performed using PIX4D, and data analysis was performed using R and QGIS. The random forest (RF) and support vector machine (SVM) algorithms were used for the classification of the regions of interest: coffee, weed, brachiaria, and exposed soil. The differentiation between the study classes was possible due to the spectral differences between the targets, with better classification performance using the RF algorithm. The savings gained by only treating areas with the presence of weeds compared with treating the total study area are approximately 92.68%. [ABSTRACT FROM AUTHOR]

Canopy chlorophyll content (CCC) is closely related to crop nitrogen status, crop growth and productivity, detection of diseases and pests, and final yield. Thus, accurate monitoring of chlorophyll content in crops is of great significance for decision support in precision agriculture. In this study, winter wheat in the Guanzhong Plain area of the Shaanxi Province, China, was selected as the research subject to explore the feasibility of canopy spectral transformation (CST) combined with a machine learning method to estimate CCC. A hyperspectral canopy ground dataset in situ was measured to construct CCC prediction models for winter wheat over three growth seasons from 2014 to 2017. Sensitive-band reflectance (SR) and narrow-band spectral index (NSI) were established based on the original spectrum (OS) and CSTs, including the first derivative spectrum (FDS) and continuum removal spectrum (CRS). Winter wheat CCC estimation models were constructed using univariate regression, partial least squares (PLS) regression, and random forest (RF) regression based on SR and NSI. The results demonstrated the reliability of CST combined with the machine learning method to estimate winter wheat CCC. First, compared with OS-SR (683 nm), FDS-SR (630 nm) and CRS-SR (699 nm) had a larger correlation coefficient between canopy reflectance and CCC; secondly, among the parametric regression methods, the univariate regression method with CRS-NDSI as the independent variable achieved satisfactory results in estimating the CCC of winter wheat; thirdly, as a machine learning regression method, RF regression combined with multiple independent variables had the best winter wheat CCC estimation accuracy (the determination coefficient of the validation set (Rv2) was 0.88, the RMSE of the validation set (RMSEv) was 3.35 and relative prediction deviation (RPD) was 2.88). Thus, this modeling method could be used as a basic method to predict the CCC of winter wheat in the Guanzhong Plain area. [ABSTRACT FROM AUTHOR]

Wheat can be cultivated by hole sowing, but its border effect has not yet been studied. Therefore, we carried out a field experiment from 2021 to 2022 at the Doukou Crop Experimental Demonstration Station (108°52′ E, 34°37′ N) of Northwest A&F University in Jingyang County, Xianyang City, Shaanxi Province, China. The response of dry matter, photosynthetic characteristics, and yield components of wheat to the border effects under the hole sowing method was studied. The results showed specific border effects on each index of five wheat varieties (XN136, XN175, XN527, XN536, and XN765), among which the border effects of XN175 and XN765 were the most significant, with the highest yield. Subsequent correlation analysis revealed that only grain per spike and intercellular carbon dioxide concentration responded negatively to the border effects, and the rest were positively correlated. Finally, we conducted a random forest model analysis of different indicators of wheat varieties with significant border effects. We found that net photosynthetic rate and aboveground dry matter per plant had the most significant impact and contribution to the border effects. In contrast, grain per spike had the most negligible impact on the border effects. Our results fill a gap in the study of the border effects of wheat under hole sowing cultivation for future researchers. [ABSTRACT FROM AUTHOR]

Soil cation exchange property (SCEP) is important in soil development and environmental buffering. However, the variations in SCEP and its correlation with soil-forming factors in subsurface soil are not fully understood. In this study, we quantified the surface and subsurface SCEP variations as a function of parent material, vegetation, hillslope position, and soil type. Fifty upland soil profiles from mid-subtropical China were selected. The cation exchange capacity (CEC) and effective CEC (ECEC) of subsurface soil were significantly higher in soils derived from slate, Quaternary red clay (QRC), and limestone than in soils derived from granite and sandstone. The subsurface soils derived from limestone had the highest base saturation (BS) and the sum of base cations, and the lowest aluminum (Al) saturation. The SCEP in surface soil significantly varied with vegetation and hillslope position. The surface soil CEC was the highest in mixed-forest vegetation, whereas the ECEC and exchangeable acidity (EA) were the highest in arable vegetation. Exchangeable potassium (K+) was lowest and the EA was highest in soil orders at the strong development phase. Exchangeable calcium (Ca2+), magnesium (Mg2+), CEC, and BS were the highest in soil orders at the intermediate development phase. The prediction accuracy of soil CEC using the random forest model was higher than that obtained using multiple stepwise regression, with the best results (R2 = 0.92) obtained in the surface soil. Our study indicated that the SCEP in surface and subsurface soils was controlled by different soil-forming factors and could be effectively predicted by soil properties in subtropical China. [ABSTRACT FROM AUTHOR]

Crop growth is often uneven within an agricultural parcel, even if it has been managed evenly. Aerial images are often used to determine the presence of vegetation and its spatial variability in field parcels. However, the reasons for this uneven growth have been less studied, and they might be connected to variations in topography, as well as soil properties and quality. In this study, we evaluated the relationship between drone image data and field and soil quality indicators. In total, 27 multispectral and RGB drone image datasets were collected from four real farm fields in 2016–2020. We analyzed 13 basic soil quality indicators, including penetrometer resistance in top- and subsoil, soil texture (clay, silt, fine sand, and sand content), soil organic carbon (SOC) content, clay/SOC ratio, and soil quality assessment parameters (topsoil biological indicators, subsoil macroporosity, compacted layers in the soil profile, topsoil structure, and subsoil structure). Furthermore, a topography variable describing water flow was used as an indicator. Firstly, we evaluated single pixel-wise linear correlations between the drone datasets and soil/field-related parameters. Correlations varied between datasets and, in the best case, were 0.8. Next, we trained and tested multiparameter non-linear models (random forest algorithm) using all 14 soil-related parameters as features to explain the multispectral (NIR band) and RGB (green band) reflectance values of each drone dataset. The results showed that the soil/field indicators could effectively explain the spatial variability in the drone images in most cases (R2 > 0.5), especially for annual crops, and in the best case, the R2 value was 0.95. The most important field/soil features for explaining the variability in drone images varied between fields and imaging times. However, it was found that basic soil quality indicators and topography variables could explain the variability observed in the drone orthomosaics in certain conditions. This knowledge about soil quality indicators causing within-field variation could be utilized when planning cultivation operations or evaluating the value of a field parcel. [ABSTRACT FROM AUTHOR]

In oat production, the over-application of nitrogen (N) fertilizer in fields due to low N fertilizer use efficiency not only increases production costs but also causes environmental pollution. Currently, mining low N-tolerant oat varieties is an important way to promote sustainable agriculture. In this study, 30 oat varieties were grown in a seedling culture with two treatments of normal N (10 mM NH4NO3) and low N (1.25 mM NH4NO3), and the correlations between agronomic traits and plant N content and low N tolerance coefficients and indices were determined, which can be used as indicators for the evaluation of low N-tolerant oat varieties. Coefficient of variation, correlation analysis, principal component analysis, partial least-squares discrimination analysis, random forest analysis, least absolute shrinkage and selection operator regression and model evaluation, and membership function analysis were used for in-depth analysis of these indicators. Plant N content, root–crown ratio, and dry weight of aboveground plant parts were found to be important indicators of low N tolerance in oats. According to the membership function ranking of the 30 selected oat varieties, Jiayan 2, Qingyongjiu 035, and Qingyin 2 had strong tolerance to low N stress and Qingyongjiu 003, Qingyongjiu 021, and Qingyongjiu 016 had poor tolerance to low N stress. Thus, this study provides a reliable and comprehensive method for evaluating the low N tolerance of oat varieties as well as a reference for screening other low N-tolerant plants. [ABSTRACT FROM AUTHOR]

Evapotranspiration by phreatophytes in riparian zones makes up a large component of the water balance. However, our understanding of the relative importance of controlling factors such as climatic conditions, species type, depth to groundwater and distance to surface water in riparian zones remains a significant knowledge gap. A field experiment was conducted in an irrigated catchment in North Queensland, Australia, to investigate the factors controlling evapotranspiration by groundwater dependent trees. The sap flow of four tree species was measured, along with soil moisture, groundwater levels and local climatic conditions. The relative influence of species, hydrologic and climate factors, and measured variables were investigated with two non-parametric methods: random forest and Principal Component Analysis (PCA). Field monitoring data revealed differences in sap flow rates and diurnal sap flow trends between species. Distance from surface water explained the most variance in sap flow rates, followed by depth to groundwater and species, based on random forest modeling. The sap flow rates for some of the Eucalyptus tessellaris trees at this site reduced as groundwater levels declined. Overall, results demonstrate the value that can be gained from applying non-parametric methods, such as random forest and PCA, to investigate the relative importance of the factors influencing evapotranspiration. [ABSTRACT FROM AUTHOR]

Timely harvest can effectively guarantee the yield and quality of rapeseed. In order to change the artificial experience model in the monitoring of rapeseed harvest period, an intelligent prediction method of harvest period based on deep learning network was proposed. Three varieties of field rapeseed in the harvest period were divided into 15 plots, and mobile phones were used to capture images of silique and stalk and manually measure the yield. The daily yield was divided into three grades of more than 90%, 70–90%, and less than 70%, according to the proportion of the maximum yield of varieties. The high-dimensional features of rapeseed canopy images were extracted using CNN networks in the HSV space that were significantly related to the maturity of the rapeseed, and the seven color features of rapeseed stalks were screened using random forests in the three color-spaces of RGB/HSV/YCbCr to form a canopy-stalk joint feature as input to the subsequent classifier. Considering that the rapeseed ripening process is a continuous time series, the LSTM network was used to establish the rapeseed yield classification prediction model. The experimental results showed that Inception v3 of the five CNN networks has the highest prediction accuracy. The recognition rate was 91% when only canopy image features were used, and the recognition rate using canopy-stalk combined features reached 96%. This method can accurately predict the yield level of rapeseed in the mature stage by only using a mobile phone to take a color image, and it is expected to become an intelligent tool for rapeseed production. [ABSTRACT FROM AUTHOR]