Abstract
Early detection of tomato bacterial spot is critical for effective disease management in greenhouse environments. This study developed an automated hyperspectral data collection system to capture spectral information from inoculated tomatoes in a walk-in growth chamber, enabling noninvasive monitoring of disease progression. A plant physiological-informed machine learning model, incorporating a genetic algorithm for feature selection, was used to optimize the classification of healthy and diseased samples. To reduce multicollinearity and enhance the machine learning model performance, three key vegetation indices (VIs) were selected from a pool of 18 candidate VIs using a genetic algorithm. The model achieved a 92% recall rate using the three VIs that are associated with chlorophyll content and photosynthetic efficiency. The result of this study suggests a promising methodology for plant disease detection beyond that of the tomato bacterial spot.