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

A Novel Hyperspectral Imaging based Framework for Tomato Leaf Disease Detection

A lightweight deep learning model, the Dual Multi-Layer Perceptron Feature Fusion Network (DMLPFFN) as a hyperspectral-based approach to the four tomato disease stages, which includes healthy, asymptomatic, early and late infection is demonstrated.

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

Wheat gluten index is an important indicator of food quality inspection. Although hyperspectral technology offers non-destructive and rapid detection potential, its application faces challenges including high-dimensional data redundancy, multicollinearity among adjacent wavelengths, and model overfitting risks in small-sample scenarios. This study proposes a rapid prediction method for wheat gluten index integrating adaptive spectral preprocessing with machine learning. Based on 89 wheat flour samples, adaptive wavelet denoising (average SNR improvement: 11.92 dB), successive projections algorithm (SPA) feature selection, and variance inflation factor (VIF) collinearity diagnosis reduced 2001 spectral features to 5 core variables (587 nm, 436nm_square, 1241 nm, 1080nm_square, 940nm_square). Ten algorithms including PLSR, SVR, XGBoost, and CatBoost were systematically compared under 70% training/30% test split with 10-fold cross-validation. CatBoost achieved optimal performance (R2 = 0.8552, RMSE = 7.8602), surpassing PLSR (R2 = 0.7090) and XGBoost (R2 = 0.7923) by 20.6% and 8.0% respectively. Notably, the well-tuned single CatBoost model outperformed stacking ensemble methods (R2 = 0.7803), providing empirical evidence for model selection in small-sample spectral analysis. SHAP interpretability analysis identified critical spectral bands corresponding to protein and starch absorption characteristics, offering guidance for portable equipment optimization and mechanistic understanding of spectral-quality relationships.

Explainable AI-based hybrid GNN-MLP model for strawberry fruit disease detection using hyperspectral imaging

An enhanced hybrid deep-learning method by combining graph neural networks (GNNs) and multi-layer perceptrons (MLPs) for effective strawberry disease detection in real environments of fields offers an accurate and explainable solution that has a computationally efficient commitment for real-time monitoring of disease in smart agriculture settings, particularly on low-cost hardware assets.

A Multi-Channel GASF-Based hyperspectral classification framework integrating feature enhancement and interpretability for green chili pepper quality assessment

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.

Enhanced tomato leaf disease detection using multi-modal deep-handcrafted feature fusion with optimization-driven learning techniques