Hyperspectral monitoring of wheat gluten index based on multi-model ensemble and adaptive learning.

Optimizing Leaf Nitrogen Estimation Using Hyperspectral Sensing and Machine Learning With Band Selection Techniques

Integrating hyperspectral sensing with machine learning (ML) enables efficient monitoring of crop health, improved fertilization management, and data-driven precision agriculture. This study proposes a framework for leaf-level nitrogen estimation using full-range visible, near-infrared, and short-wave infrared hyperspectral reflectance data combined with advanced ML models and band selection (BS) strategies. Seven regression models, including linear, kernel-based, and neural-network approaches, were benchmarked using both full-spectrum data and reduced bands subsets. BS was addressed through variable importance in projection (VIP), a constrained genetic algorithm (GA) specifically designed to enforce fixed subset cardinality, and by introducing a two-step hybrid VIP–GA (HVG) strategy, in which VIP was first applied as a coarse filtering step followed by GA-based refinement, thus combining computational efficiency with refined combinatorial optimization. Experiments were conducted on a large public leaf-level spectral dataset using both repeated random subsampling and leave-one-group-out (LOGO) validation to assess robustness to training set size and transferability under heterogeneous conditions. Results showed that regularized linear models achieved, on these data, performance comparable to or better than more complex nonlinear approaches, with ridge regression yielding the lowest $\text{NRMSE}{{\mathrm{P}}_{\text{IQR}}}$ values, ranging between 0.27 and 0.30 across different BS scenarios. LOGO experiments indicated moderate transferability across heterogeneous conditions, although performance remained dependent on group-specific characteristics. Overall, BS considerably reduced spectral dimensionality and computational cost while preserving predictive performance, with the HVG approach providing an effective tradeoff between accuracy, efficiency, and interpretability. These findings highlight the potential of combining ML and BS for efficient leaf-level nutrient monitoring and for providing a priori spectral insights for future hyperspectral sensing studies.

Multiscale Multispectral–Hyperspectral Data for Estimating Coffee Yield Using Machine Learning Algorithms

Abstract. This study evaluated the performance of multispectral (Mavic 3M) and hyperspectral (Blue Wave) data in estimating coffee crop productivity using linear regression, SVM, and neural networks. Forty plots with different varieties were analyzed. Multispectral data showed high correlation with productivity, especially the Red Edge (r = 0.704) and Green (r = 0.644) bands. For hyperspectral data, PRI (r = 0.535), GNDVI (r = -0.394), NDVI (r = -0.33), and CIRE (r = -0.328) were significant, highlighting the negative correlation pattern typically observed in perennial crops. Neural network models applied to hyperspectral data achieved the best performance (r = 0.92; RMSE = 6.6%), surpassing multispectral models (r = 0.84; RMSE = 9.4%).

Hyperspectral Prediction of Variety, SPAD Value, and Water Content of Oilseed Rape Leaves Using an Improved WGAN-GP

Rapid and non-destructive identification of oilseed rape varieties and prediction of leaf Soil Plant Analysis Development (SPAD) value and water content are important for variety evaluation and plant-status monitoring. However, hyperspectral prediction is constrained by small sample size, costly reference measurements, and acquisition-batch differences. Samples were collected over four consecutive days. Day 1 samples were used for training and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) generation. For Days 2–4, 20% of the samples from each day were combined to form a selection-validation set, while the remaining 80% were retained separately as Test sets 1–3. The improved WGAN-GP integrated principal component analysis–Gaussian mixture model (PCA–GMM)-based class assignment, a partial least squares regression (PLSR) consistency loss, physicochemical distribution control, and generated-sample selection. Under single-task modeling, the improved framework enhanced all three tasks across the test sets. On Test set 3, variety accuracy increased from 0.4672 to 0.6100, SPAD root mean square error of prediction (RMSEP) decreased from 6.2201 to 4.6303, and the water-content test-set correlation coefficient (rp) increased from 0.6803 to 0.7842. The improved multi-task model also enhanced all three tasks. These findings support the framework within the investigated three-variety, four-day leaf setting.

Combining polarized hyperspectral features with an improved deep learning model for wheat powdery mildew severity estimation

Machine Learning-Based Detection and Quantification of Septoria Leaf Blotch in Winter Wheat from Hyperspectral and UAV Multispectral Data

Septoria leaf blotch (SLB), caused by Zymoseptoria tritici, is one of the most destructive foliar diseases of wheat and requires accurate methods for early detection and disease severity assessment. This study evaluated the potential of hyperspectral ASD measurements and UAV multispectral imagery combined with machine learning for the detection and quantification of SLB in winter wheat. Six spectral datasets derived from hyperspectral reflectance, UAV multispectral imagery, and vegetation indices were analyzed using CatBoost, Random Forest, and XGBoost algorithms. Random Forest achieved the highest classification performance, reaching an accuracy of 0.9583 and a balanced accuracy of 0.9483. For disease severity prediction, the best performance was obtained using ASD-derived vegetation indices with Random Forest (R2 = 0.9199), while CatBoost consistently provided high regression accuracy across hyperspectral datasets. A reduced set of green, red, red-edge, and near-infrared bands produced classification results comparable to those obtained with the full hyperspectral spectrum, indicating that most diagnostic information is concentrated within these spectral regions. Although UAV multispectral data showed lower accuracy than hyperspectral measurements, particularly for disease severity prediction, they enabled effective field-scale disease monitoring. These findings demonstrate that hyperspectral sensing provides a valuable reference for developing accurate disease detection models, whereas UAV multispectral imagery represents a practical and scalable solution for operational precision agriculture.

Multistage Fourier Volumetric Attention Transformer for Hyperspectral Reconstruction and Physicochemical Quality Prediction of Brewing Sorghum