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...

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 i...

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

The results showed that ResNet-SVM outperformed the standalone ResNet, demonstrating the synergistic advantage of deep feature extraction and SVM-based discrimination, and the three-channel strategy further improved classification performance.

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

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 is proposed.

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