Plant disease identification using explainable 3D deep learning on hyperspectral images

By Koushik Nagasubramanian, Sarah Jones, Asheesh K. Singh, Soumik Sarkar, Arti Singh, Baskar Ganapathysubramanian. Plant Methods, 2019

336 citations recorded by the source corpus.

Hyperspectral 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. Based 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. The 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.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/aa1bb0201dcf409eafd5862a0d30c68c02fca4e5

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Citations in the corpus

Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, methodology): “S7 [41] 2019 4 3D-CNN 95,73% Customized 1.”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “In [112], the author developed a supervised 3D-CNN model to learn the spectral and spatial information of hyperspectral images for the classification of healthy and charcoal rot infected samples.”
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, background): “[144] Tried to detect charcoal rot in soybeans using close‐range hyperspectral imaging and identified wavelength ranges that are sensitive to this disease.”