2026· Journal of the ASABE· Vol 69, pp. 559-570· 0 citations
TL;DR
This approach could enable automated instance segmentation of disease symptoms on citrus leaves and thus assist citrus growers in early intervention and contribute to the development of a cost-effective multispectral disease inspection system with selected bands.
Abstract
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.
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.
Poonam Chaudhary, Sneha Kandacharam· Indian Journal of Agricultur...· 0 citations
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.
Andrzej Wójtowicz, Jan Piekarczyk, Marek Wójtowicz et al.· Remote Sensing· 0 citations
Late blight in tomato caused by Phytophthora infestans can lead to severe economic losses. Early detection is essential for effective disease management. This study investigates the spectral characteristics of healthy and late blight-infected tomato leaves and plants using non-destructive hyperspectral imaging and proposes a cost-effective machine vision system for early disease detection. Hyperspectral images from seven batches of leaf sets and six batches of whole plant sets were taken hourly over a 96 h period under both controlled and artificially infected conditions. The hyperspectral data cubes were processed with an image analysis model that identified healthy vs. infected regions. Key wavelengths (77 from leaf datasets and 24 from plant datasets) were selected using recursive feature elimination and analysed with four machine learning classifiers: k-nearest neighbour, support vector machine, random forest, and artificial neural network. The models differentiated healthy and infected tissue with high accuracy (98–99%). The hyperspectral data were simplified into a multichannel image with most informative wavelengths, using a custom spectral index and binary decision rule. Experimental limitations were addressed, and a conceptual design of practical hardware was proposed: a monochrome camera combined with a multichannel light source and polariser mounted on mobile equipment. Although further trials will be needed, this proof-of-concept study and conceptual hardware design can be adapted in other crops facing similar disease challenges.
N. Setiawan, B. Labus, F. Tóth et al.· AgriEngineering· 0 citations
The FRCNN-FCMWeight model provides strong support for agricultural use cases and improves overall model generalization and enhances training robustness and identifies diseased regions effectively under highly variable field conditions.
Crop leaf diseases often differ by small lesion regions and similar color-texture patterns, which can limit the feature selectivity of compact CNN backbones when annotated images are insufficient. To address this problem, this study develops an improved ResNet18 for PlantVillage-based leaf disease recognition. The model combines three changes: an SE channel-recalibration unit for emphasizing lesion-related channels, Leaky ReLU for preserving gradient flow when activations are negative, and ImageNet-based transfer learning for faster adaptation to the target classes. Under the same training and evaluation protocol, the proposed network achieved 97.86% accuracy, with precision, recall, and F1-score of 97.63%, 97.41%, and 97.52%, respectively. These values exceeded the original ResNet18 and several commonly used CNN baselines. Ablation experiments showed stepwise gains from the three modifications, and Grad-CAM visualization indicated more concentrated responses over lesion regions. The results suggest that the modified ResNet18 is an effective compact baseline for crop leaf disease recognition, although validation under complex field conditions remains necessary.
A modified AlexNet architecture for classifying field-captured tomato leaf images into seven disease categories was developed and generally focused on symptom-bearing leaf regions, whereas target spot was the most difficult category to classify.
Debabrat Bharali, Kanak C. Bora, Rashel Sarkar et al.· Journal of Scientific Resear...· 0 citations