Search Results (305)

Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms. Full article

Accurate estimation aboveground biomass (AGB) in winter wheat is crucial for yield assessment but remains challenging to achieve non-destructively. Unmanned aerial vehicle (UAV)-based remote sensing offers a promising solution at the plot level. Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. This study proposes a novel method using Fractional Vegetation Coverage (FVC) to adjust field-sampled AGB to per-plant biomass, enhancing the accuracy of AGB estimation using UAV imagery. Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. The aim was to evaluate the performance of multimodal data in estimating winter wheat leaves, spikes, stems, and total AGB. Results demonstrated that (1) FVC-adjusted per-plant biomass significantly improved correlations with most indicators, particularly during the filling stage, when the correlation between leaf biomass and NDVI increased by 56.1%; (2) RF and NN models outperformed SVM, with the optimal accuracies being R² = 0.709, RMSE = 0.114 g for RF, R² = 0.66, RMSE = 0.08 g for NN, and R² = 0.557, RMSE = 0.117 g for SVM. Notably, the RF model achieved the highest prediction accuracy for leaf biomass during the flowering stage (R² = 0.709, RMSE = 0.114); (3) among different water treatments, the R² values of water and drought treatments were higher 0.723 and 0.742, respectively, indicating strong adaptability. This study provides an economically effective method for monitoring winter wheat growth in the field, contributing to improved agricultural productivity and fertilization management. Full article

Crop height is a key indicator of plant development and growth dynamics, offering valuable insights for temporal crop monitoring. However, modeling its variation across phenological stages remains challenging due to canopy structural changes. This study aimed to predict wheat height throughout the growth cycle by integrating radar remote sensing data with a phenology-informed clustering approach. The research was conducted in three wheat fields in Umbria, Italy, from 30 January to 10 June 2024, using in-field height measurements, phenological observations, and Sentinel-1 acquisitions. Backscatter variables (VH, VV, and CR) were processed using two speckle filters (Lee 7 × 7 and Refined Lee), alongside additional radar-derived parameters (entropy, anisotropy, alpha, and RVI). Fuzzy C-means clustering enabled the classification of observations into two phenological groups, supporting the development of stage-specific linear regression models. Results demonstrated high accuracy during early growth stages (tillering to stem elongation), with R² values of 0.76 (RMSE = 6.88) for Lee 7 × 7 and 0.79 (RMSE = 6.35) for Refined Lee. In later stages (booting to maturity), model performance declined, with Lee 7 × 7 outperforming Refined Lee (R² = 0.51 vs. 0.33). These findings underscore the potential of phenology-based modeling approaches to enhance crop height estimation and improve radar-driven crop monitoring. Full article

The aim of this study is to develop a rapid method for monitoring leaf nitrogen content (LNC) in winter wheat, which is essential for precise field management and accurate crop growth assessment. This study used a natural winter wheat population at Shanxi Agricultural University’s experimental base as the subject. UAV-mounted multispectral sensors collected images at jointing, heading, pre-grouting, and late grouting stages. Canopy spectral reflectance was extracted using image segmentation, and vegetation indices were calculated. Correlation analysis identified highly relevant indices with LNC. Support Vector Regression (SVR), Random Forest (RF), Ridge Regression (RR), K-Nearest Neighbors (K-NN), and ensemble learning algorithms (Voting and Stacking) were employed to model the relationship between selected vegetation indices and LNC. Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). Results showed that the Voting-based ensemble learning model outperformed other models. At the pre-grouting stage, this model achieved an R² of 0.85 and an RMSE of 1.57 for the training set, and an R² of 0.82 and an RMSE of 1.64 for the testing set. This study provides a theoretical basis and technical reference for monitoring LNC in winter wheat at key growth stages using low-altitude multispectral sensors, supporting precision agriculture and variety evaluation. Full article

The rapid and accurate prediction of crop yield and the construction of optimal yield prediction models are important for guiding field-scale agronomic management practices in precision agriculture. This study selected the leaf area index (LAI) of winter wheat (Triticum aestivum L.) at four different stages, and collected canopy spectral information and extracted vegetation indexes through unmanned aerial vehicle (UAV) multi-spectral sensors to establish the yield prediction model under the condition of slow-release nitrogen fertilizer and proposed optimal fertilization strategies for sustainable yield increase in wheat. The prediction results were evaluated using random forest (RF), support vector machine (SVM) and back propagation neural network (BPNN) methods to select the optimal spectral index and establish yield prediction models. The results showed that LAI has a significantly positive correlation with yield across four growth stages of winter wheat, and the correlation coefficient at the anthesis stage reached 0.96 in 2018–2019 and 0.83 in 2019–2020. Therefore, yield prediction for winter wheat could be achieved through a remote sensing estimation of LAI at the anthesis stage. Six vegetation indexes calculated from UAV-derived reflectance data were modeled against LAI, demonstrating that the red-edge vegetation index (CI_{red edge}) achieved superior accuracy in estimating LAI for winter wheat yield prediction. RF, SVM and BPNN models were used to evaluate the accuracy and precision of CI_{red edge} in predicting yield, respectively. It was found that RF outperformed both SVM and BPNN in predicting yield accuracy. The CI_{red edge} of the anthesis stage was the best vegetation index and stage for estimating yield of winter wheat based on UAV remote sensing. Under different N application rates, both predicted and measured yields exhibited a consistent trend that followed the order of SRF (slow-release N fertilizer) > SRFU1 (mixed TU and SRF at a ratio of 2:8) > SRFU2 (mixed TU and SRF at a ratio of 3:7) > TU (traditional urea). The optimum N fertilizer rate and N fertilizer type for winter wheat in this study were 220 kg ha⁻¹ and SRF, respectively. The results of this study will provide significant technical support for regional crop growth monitoring and yield prediction. Full article

Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha⁻¹), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R² increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R² improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems. Full article

Hyperspectral remote sensing using Unmanned Aerial Vehicles (UAVs) provides accurate, near real-time, and large-scale spatial estimation of the leaf area index (LAI), a significant crop variable for monitoring crop growth. In the present study, the LAI of wheat crops was estimated using high-resolution UAV-borne hyperspectral data. The PLS (Partial Least Squares) regression combined with the VIP (Variable Importance in the Projection) was used for selecting the optimum indices as feature vectors to the Extreme Gradient Boosting (Xgboost) model for predicting LAI. Twelve of twenty-seven vegetation indices were selected to develop the prediction model. On validation against the in situ measured LAI values, the prediction model shows good accuracy with an R² of 0.71. The model was used to generate a spatial map showing the variability of the LAI. Accurate mapping of LAI from high-resolution hyperspectral UAV data using machine learning models facilitates near-real-time monitoring of crop health. Full article

Plant modeling uses mathematical and computational methods to simulate plant structures, physiological processes, and interactions with various environments. In precision agriculture, it enables the digital monitoring and prediction of crop growth, supporting better management and efficient resource use. Wheat, as a major global staple, is vital for food security. However, wheat stripe rust, a widespread and destructive disease, threatens yield stability. The paper proposes wheat cultivation suitability evaluation with stripe rust disease using an agriculture group consensus framework (WCSE-AGC) to tackle this issue. Assessing stripe rust severity in regions relies on wheat pathologists’ judgments based on multiple criteria, creating a multi-attribute, multi-decision-maker consensus problem. Limited regional coverage and inconsistent evaluations among wheat pathologists complicate consensus-reaching. To support wheat pathologist participation, this study employs artificial-intelligence-generated content (AIGC) techniques by using Claude 3.7 to simulate wheat pathologists’ scoring through role-playing and chain-of-thought prompting. WCSE-AGC comprises three main stages. First, a graph neural network (GNN) models trust propagation within wheat pathologists’ social networks, completing missing trust links and providing a solid foundation for weighting and clustering. This ensures reliable expert influence estimations. Second, integrating secretary bird optimization (SBO), K-means, and three-way clustering detects overlapping wheat pathologist subgroups, reducing opinion divergence and improving consensus inclusiveness and convergence. Third, a two-stage optimization balances group fairness and adjustment cost, enhancing consensus practicality and acceptance. The paper conducts experiments using publicly available real wheat stripe rust datasets from four different locations, Ethiopia, India, Turkey, and China, and validates the effectiveness and robustness of the framework through comparative and sensitivity analyses. Full article

Mycotoxins are a large family of secondary metabolites produced by filamentous fungi species that may be present in food following fungal growth. Mycotoxins are found in a variety of crops, including wheat, millet, maize, sorghum, peanut, soybean, and their by-products. In recent years, the consumption of ready-to-eat food (RTE) has increased exponentially. An increasing number of consumers have elected to purchase and consume ready-made meals, a choice that allows for a more expedient and convenient dining experience. The aim of this review was to investigate recent literature to find a link between the consumption of mycotoxin-contaminated RTEs, modern detection methods (artificial intelligence), and potential health risks to consumers. The regular exchange of information between the Member States and the European Community (EU) concerning the monitoring of contaminants and undesirable chemical substances, and the subsequent communication of the findings to the EFSA, provides the foundation for the evolution of the legislative framework with the objective of enhancing food safety and reducing the risks associated with the consumption of food. It is imperative that governments, the food industry, and the scientific community collaborate to reduce this risk and ensure consumer safety. Full article

Soil organic matter (SOM) is an essential factor affecting the growth and development of crops, so the establishment of an efficient and rapid method for detecting SOM content is of great significance for crop cultivation and management. The spatial distribution map of SOM content in the study area was obtained by using the optimal model, and a distribution map of aboveground wheat biomass under different fertilization conditions was drawn. The results of this study showed that the fertilization treatments significantly increased the SOM content, and its spatial distribution showed obvious heterogeneity. By plotting the spatial distribution of SOM content and wheat growth under different fertilization conditions, it was found that the wheat biomass of fertilized fields was significantly higher than that of non-fertilized fields. Further analysis showed that there was a significant positive correlation between SOM content and wheat biomass, and a quantitative model between the two was established. This study provides scientific evidence and technical support for soil nutrient management and crop productivity enhancement in precision agriculture, as well as a reference for the application of hyperspectral imagery in agroecosystem monitoring. Full article

The accurate and timely determination of crop leaf area indices (LAIs) assists in making agricultural decisions. The objective of this study was to estimate crop LAIs using C-band RADARSAT-2 synthetic aperture radar (SAR) datasets and a modified water cloud model (MWCM). The WCM was improved through two steps: (1) constructing a vegetation coverage ratio ($f_v$) using normalized difference vegetation indices calculated from Landsat-8 images and introducing it into the traditional WCM, and (2) incorporating field-collected crop height into the vegetation canopy described in the scattering model. The proposed MWCM parameters were calibrated using an iterative optimization algorithm named the Levenberg–Marquardt (LM) algorithm. The model’s performance before and after improvement was systematically calibrated and validated using field data collected from Yigen Farm (Hulunbuir City, Inner Mongolia Autonomous Region, China). The results show that the MWCM performed better than the original WCM in four polarization channels—HH, VV, HV, and VH—for both wheat and rape oilseed LAI inversion. HH polarization showed the best performance using both the MWCM and WCM for wheat, with R² values of 0.4626 and 0.3327, respectively; meanwhile, for oilseed rape, the R² values were 0.4912 and 0.3128, respectively. The RMSEs of the wheat inversion results were reduced from 1.5227 m²m⁻² to 1.4898 m²m⁻², and those for oilseed rape were reduced from 1.0411 m²m⁻² to 0.7968 m²m⁻². This study proved the feasibility and superiority of the MWCM, which provides new technical support for accurate crop growth monitoring. Full article

Drought stress negatively affects plant metabolism and growth, triggering complex defence mechanisms to limit damage. This study evaluated the effectiveness of a single foliar application of 1 mM L-proline (Pro) in winter wheat (Triticum aestivum L., cv. Bohemie) in two separate experiments differing in the time interval between application and drought—7 days (experiment 1) and 35 days (experiment 2). Net photosynthetic rate (A), transpiration rate (E), stomatal conductance (g_s), leaf water potential (Ψw), intrinsic water use efficiency (WUE_i), endogenous proline content (Pro), malondialdehyde content (MDA), and maximum quantum yield of photosystem II (F_v/F_m) were measured. In experiment 1, drought markedly reduced net photosynthetic rate, transpiration rate, stomatal conductance, and leaf water potential in both drought-stressed treatments, namely, without priming plants (S) and with Pro priming plants (SPro). Pro and MDA content increased under stress. Higher E and g_s in the SPro treatment indicated more effective stomatal regulation and a distinct water use strategy. Pro content was significantly lower in SPro compared to S, whereas differences in MDA levels between these treatments were not statistically significant. The second drought period (D2) led to more pronounced limitations in gas exchange in both S and SPro. Enhanced osmoregulation was reflected by lower Ψw (S < SPro) and higher Pro accumulation in S (S > SPro). The effect of exogenous Pro persisted in the form of reduced endogenous Pro synthesis and improved photosystem II protection. Rehydration of stressed plants restored all monitored physiological parameters, and Pro-treated plants exhibited a more efficient recovery of gas exchange. Experiment 2 demonstrated a long-lasting priming effect that improved the preparedness of plants for future drought events. In the SPro treatment, this stress memory supported more efficient osmoregulation, reduced lipid peroxidation, improved protection of photosystem II integrity, and a more effective restart of gas exchange following rehydration. Our findings highlight the potential of exogenous proline as a practical tool for enhancing crop resilience to climate-induced drought stress. Full article

This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at various growth stages. The Modified Perpendicular Drought Index (MPDI) was calculated to quantify soil drought conditions. Simultaneously, soil samples were collected to measure the actual soil moisture content. These datasets were used to develop a Gradient Boosting Regression (GBR) model to estimate soil moisture across the entire field. The resulting AI-based model can guide decisions on the timing and scale of supplemental irrigation, ensuring water is applied only when needed during crop growth. Furthermore, MPDI values and wheat spike samples were used to construct another GBR model for yield prediction. When applying MPDI values from multispectral imagery collected at a similar stage in the following year, the model achieved a prediction accuracy of over 90%. The proposed approach offers a reliable solution for enhancing the resilience and productivity of dryland crops under climate stress and demonstrates the potential of integrating remote sensing and machine learning in precision water management. Full article

Wheat canopy height is an important parameter for monitoring growth status. Accurately predicting the wheat canopy height can improve field management efficiency and optimize fertilization and irrigation. Changes in the growth characteristics of wheat at different growth stages affect the canopy structure, leading to changes in the quality of the LiDAR point cloud (e.g., lower density, more noise points). Multispectral data can capture these changes in the crop canopy and provide more information about the growth status of wheat. Therefore, a method is proposed that fuses LiDAR point cloud features and multispectral feature parameters to estimate the canopy height of winter wheat. Low-altitude unmanned aerial systems (UASs) equipped with LiDAR and multispectral cameras were used to collect point cloud and multispectral data from experimental winter wheat fields during three key growth stages: green-up (GUS), jointing (JS), and booting (BS). Analysis of variance, variance inflation factor, and Pearson correlation analysis were employed to extract point cloud features and multispectral feature parameters significantly correlated with the canopy height. Four wheat canopy height estimation models were constructed based on the Optuna-optimized RF (OP-RF), Elastic Net regression, Extreme Gradient Boosting, and Support Vector Regression models. The model training results showed that the OP-RF model provided the best performance across all three growth stages of wheat. The coefficient of determination values were 0.921, 0.936, and 0.842 at the GUS, JS, and BS, respectively. The root mean square error values were 0.009 m, 0.016 m, and 0.015 m. The mean absolute error values were 0.006 m, 0.011 m, and 0.011 m, respectively. At the same time, it was obtained that the estimation results of fusing point cloud features and multispectral feature parameters were better than the estimation results of a single type of feature parameters. The results meet the requirements for canopy height prediction. These results demonstrate that the fusion of point cloud features and multispectral parameters can improve the accuracy of crop canopy height monitoring. The method provides a valuable method for the remote sensing monitoring of phenotypic information of low and densely planted crops and also provides important data support for crop growth assessment and field management. Full article

Integrated aquaculture–agriculture systems (IAASs) offer a sustainable approach to mitigating soil salinity by utilizing aquaculture effluents for irrigation. This study evaluates the growth performance of Nile tilapia (Oreochromis niloticus) and red tilapia (Oreochromis spp.) under varying salinity conditions and investigates their effluents on intercropped wheat and sugar beet. A field experiment was conducted using a randomized block design with seven treatments: control (chemical fertilizers dissolved in freshwater) and brackish water effluents from Nile tilapia and red tilapia at salinities of 5 ppt and 10 ppt as monocultures or mixed polycultures. Fish growth parameters were assessed, while wheat and sugar beet morphological and yield traits were monitored. Statistical analyses, including correlation and principal component analysis, were performed. Red tilapia outperformed Nile tilapia at 10 ppt salinity, achieving the highest final weight (174.52 ± 0.01 g/fish) and weight gain (165.78 ± 0.01 g/fish), while the mixed polyculture at 10 ppt exhibited optimal feed conversion (FCR: 1.32 ± 0.01). Wheat growth and yield traits (plant height, stalk diameter, and panicle weight) declined significantly under salinity stress, with 10 ppt treatments reducing plant height by ~57% compared to the control. Conversely, sugar beet demonstrated resilience, with total soluble solids (TSS) increasing by 20–30% under salinity. The mixed effluent partially mitigated salinity effects on wheat at 5 ppt but not at 10 ppt. This study highlights the potential of IAAS in saline environments, demonstrating red tilapia’s adaptability and sugar beet’s resilience to salinity stress. In contrast, wheat suffered significant reductions in growth and yield. Full article