AI-driven Web Application for Early Detection of Sudden Death Syndrome (SDS) in Soybean Leaves Using Hyperspectral Images and Genetic Algorithm

• URL: http://arxiv.org/abs/2507.03198v1
• Date: Thu, 03 Jul 2025 22:20:47 GMT
• Title: AI-driven Web Application for Early Detection of Sudden Death Syndrome (SDS) in Soybean Leaves Using Hyperspectral Images and Genetic Algorithm
• Authors: Pappu Kumar Yadav, Rishik Aggarwal, Supriya Paudel, Amee Parmar, Hasan Mirzakhaninafchi, Zain Ul Abideen Usmani, Dhe Yeong Tchalla, Shyam Solanki, Ravi Mural, Sachin Sharma, Thomas F. Burks, Jianwei Qin, Moon S. Kim,
• Abstract summary: Sudden Death Syndrome (SDS), caused by Fusarium virguliforme, poses a significant threat to soybean production.<n>This study presents an AI-driven web application for early detection of SDS on soybean leaves using hyperspectral imaging.
• Score: 0.6871062584292211
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sudden Death Syndrome (SDS), caused by Fusarium virguliforme, poses a significant threat to soybean production. This study presents an AI-driven web application for early detection of SDS on soybean leaves using hyperspectral imaging, enabling diagnosis prior to visible symptom onset. Leaf samples from healthy and inoculated plants were scanned using a portable hyperspectral imaging system (398-1011 nm), and a Genetic Algorithm was employed to select five informative wavelengths (505.4, 563.7, 712.2, 812.9, and 908.4 nm) critical for discriminating infection status. These selected bands were fed into a lightweight Convolutional Neural Network (CNN) to extract spatial-spectral features, which were subsequently classified using ten classical machine learning models. Ensemble classifiers (Random Forest, AdaBoost), Linear SVM, and Neural Net achieved the highest accuracy (>98%) and minimal error across all folds, as confirmed by confusion matrices and cross-validation metrics. Poor performance by Gaussian Process and QDA highlighted their unsuitability for this dataset. The trained models were deployed within a web application that enables users to upload hyperspectral leaf images, visualize spectral profiles, and receive real-time classification results. This system supports rapid and accessible plant disease diagnostics, contributing to precision agriculture practices. Future work will expand the training dataset to encompass diverse genotypes, field conditions, and disease stages, and will extend the system for multiclass disease classification and broader crop applicability.

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