A Novel Hyperspectral Imaging based Framework for Tomato Leaf Disease Detection

Multiclass Citrus Leaf Disease Inspection with Region Classification and Hyperspectral Imagery

Highlights Pixels in citrus leaf images with symptoms of zinc deficiency and citrus greening were distinguished from control. Using 15 hyperspectral bands instead of 100 only lowered performance by one percentage point. Symptoms can be detected without the need for segmentation-labeled training data. A multispectral camera with filters can provide comparable performance instead of a hyperspectral system. ABSTRACT. Foliar diseases of citrus pose a significant threat to citrus production, impacting both yield and fruit quality. Accurate and efficient detection of diseased regions with machine vision is essential for effective disease management. A Convolutional Neural Network (CNN) model was designed and tested for pixel-level disease segmentation among three classes: asymptomatic/control, huanglongbing (HLB), and zinc deficiency. The first CNN was tested with various spectral band configurations. The model using 100 hyperspectral (HSI) bands achieved the highest accuracies, with 98% for asymptomatic regions, 88% for HLB, and 92% for zinc deficient regions. However, models with uniformly spaced 10 bands and PCA-selected 10- and 15-band combinations performed comparably. The model with PCA-selected 15 bands exhibited increased misclassification for asymptomatic samples, with 2% labeled as HLB and 2% as zinc deficient, compared to 1% using all HSI bands. These findings can contribute to the development of a cost-effective multispectral disease inspection system with selected bands. This approach could enable automated instance segmentation of disease symptoms on citrus leaves and thus assist citrus growers in early intervention. Keywords: Citrus, Citrus greening, Convolutional neural network, Disease inspection, Hyperspectral band selection, Hyperspectral imagery, Multiclass disease detection, Principal component analysis, Region classification.

Transformer-Based Hyperspectral Feature Learning Model for Early Detection of Wheat Yellow Rust

Wheat yellow rust is a major threat to global food security, causing yield losses of up to 70% if not detected early. Hyperspectral imaging enables pre-symptomatic detection, but extracting discriminative spectral–spatial features is challenging due to high dimensionality, redundancy, limited labelled data, and subtle disease signatures. This study proposes a unified transformer-based framework that integrates self-supervised masked autoencoder (MAE) pretraining with a SpectralFormer classifier. The MAE learns spectral–spatial representations by reconstructing masked spectral tokens, thereby providing effective initialisation for downstream supervised learning. The pretrained encoder is subsequently fine-tuned for disease classification, and the fully integrated model is evaluated on a real-world UAV-acquired hyperspectral dataset. Results show that the proposed MAE-SpectralFormer achieves 98.6% accuracy, 98.4% F1-score, and 0.957 Receiver Operating Characteristic - Area Under the Curve (ROC-AUC), outperforming the strongest Convolutional Neural Network (CNN) baseline (Inception-ResNet) by 5.4 percentage points in overall accuracy and 6.0 points in Rust-class F1-score, and exceeding the supervised SpectralFormer by 2.6 and 2.4 points, respectively. These findings demonstrate that self-supervised spectral–spatial learning improves early disease detection and offers a scalable, data-efficient solution for hyperspectral crop monitoring.

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

A SCALABLE IMAGE-BASED FRAMEWORK FOR DETECTING AND MONITORING RICE LEAF DISEASES

Rice is one of the world's most important staple crops, feeding more than half of the global population. However, rice production is significantly affected by leaf diseases such as bacterial leaf blight, blast, brown spot, and tungro, resulting in substantial yield losses. Traditional disease diagnosis relies on manual field inspection, which is time-consuming, subjective, and unsuitable for large-scale monitoring. This paper proposes a scalable image-based framework for automatic rice leaf disease detection and monitoring using deep learning and cloud-enabled analytics. The proposed framework integrates image preprocessing, data augmentation, lightweight convolutional neural networks (CNNs), transfer learning, and IoT-enabled monitoring to provide accurate disease identification in realtime. A MobileNetV3-EfficientNet hybrid architecture optimized using Bayesian hyperparameter tuning is employed to classify healthy and diseased rice leaves. Extensive experiments demonstrate that the proposed framework achieves an overall accuracy of 98.94%, precision of 98.71%, recall of 98.66%, F1-score of 98.68%, and AUC of 99.31%, outperforming existing deep learning models. The proposed scalable architecture enables deployment on smartphones, drones, and edge devices for smart agriculture applications.

Deep Learning for Early Detection of Crop Pathogens: A Multimodal Fusion Framework Leveraging Hyperspectral Imaging and Climate Data in Precision Agriculture

This research introduces a multimodal deep learning framework for early detection of plant pathogens to capture pre-symptomatic biochemical changes in plants while simultaneously modeling the environmental drivers of disease development. A hybrid fusion architecture combines 3D convolutional neural networks for spatial-spectral feature extraction from HSI cubes with transformer-primarily based temporal modeling of climate sequences. Cross-modal attention mechanisms dynamically weight discriminative features, which includes chlorophyll degradation bands and humidity thresholds, to permit joint representation learning. The framework achieved 94.5% accuracy in pathogen detection, outperforming unimodal HSI (84.1%) and climate- only (76.5%) baselines by 10-18 percentage points. Moreover, it detected fungal infections 5-7 days before visual symptom onset and had a 12.3% higher F1-rating compared to the current methods. Field simulations showed that precision application resulted in 41% reduction in fungicide use. By connecting proximal sensing with climatic analytics, this research contributes to precision agriculture by providing timely and eco-friendly pest control of diseases. The multimodal fusion framework is introduced to overcome the limitations of unimodal approaches. It integrates the most appropriate data sources, thus allowing the earliest and most accurate detection of plant pathogens.

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.