AI-driven Web Application for Early Detection of Sudden Death Syndrome (SDS) in Soybean Leaves Using Hyperspectral Images and Genetic Algorithm
Journal:
arXiv
Published Date:
Jul 3, 2025
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.
