ArticlePlant methods2019
Plant disease identification using explainable 3D deep learning on hyperspectral images.
Article in Plant methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed.
- Advances in genomics-driven genetic decoding and genomic design breeding in tomato.Horticulture research · 2026Article
- A low-cost "plant-scanner" platform for automated detection of Ustilago maydis infection in maize using deep learning.Scientific reports · 2026Article
- Three-Dimensional Crop Phenotyping for Crop Protection: Reconstruction Routes, Decision Pathways, and Digital-Twin Maturity.Plants (Basel, Switzerland) · 2026Review
- Beyond the Visual Spectrum: From RGB-Based Learning to Hyperspectral Intelligence for Plant Disease Detection-Challenges and Opportunities.Sensors (Basel, Switzerland) · 2026Review
- Latent Salinity Stress Detection inSensors (Basel, Switzerland) · 2026Article
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision.Journal of imaging · 2026Review
- A Review of High-Throughput Optical Sensors for Food Detection Based on Machine Learning.Foods (Basel, Switzerland) · 2026Review
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Design and Demonstration of Compact and Lightweight Imaging Spectrometer Based on Schwarzschild Reflector Systems Using Commercial Off-the-Shelf Optics.Sensors (Basel, Switzerland) · 2025Article
- Nondestructive Identification of Eggshell Cracks Using Hyperspectral Imaging Combined with Attention-Enhanced 3D-CNN.Foods (Basel, Switzerland) · 2025Article
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- Understanding plant phenotypes in crop breeding through explainable AI.Plant biotechnology journal · 2025Review
- Conventional and cutting-edge advances in plant virus detection: emerging trends and techniques.3 Biotech · 2025Review
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Identification of yellow vein clearing disease in lemons based on hyperspectral imaging and deep learning.Frontiers in plant science · 2025Article
- A review of deep learning architectures for plant disease detection.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging.aBIOTECH · 2024Article
- Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM.Scientific reports · 2024Article
- Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications.Plants (Basel, Switzerland) · 2024Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economically important disease, charcoal rot, which is a soil borne fungal disease that affects the yield of soybean crops worldwide.
resultsBased on hyperspectral imaging of inoculated and mock-inoculated stem images, our 3D DCNN has a classification accuracy of 95.73% and an infected class F1 score of 0.87. Using the concept of a saliency map, we visualize the most sensitive pixel locations, and show that the spatial regions with visible disease symptoms are overwhelmingly chosen by the model for classification. We also find that the most sensitive wavelengths used by the model for classification are in the near infrared region (NIR), which is also the commonly used spectral range for determining the vegetative health of a plant.
conclusionThe use of an explainable deep learning model not only provides high accuracy, but also provides physiological insight into model predictions, thus generating confidence in model predictions. These explained predictions lend themselves for eventual use in precision agriculture and research application using automated phenotyping platforms.
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