Hyperspectral Detection of Fusarium Wilt in Tomato Plants Using Machine Learning-Based Approaches

Crop health is vital in agricultural nations like India. One of India's most important crops, tomato, is farmed and consumed globally. Bacteria, viruses, and fungi may damage tomato plants and other food and vegetable crops. Fusarium wilt is a seed-borne fungal infection that causes severe damage in the growth and yield of tomato crop. The objective of this research is the assessment of spectral discriminability and classification of health and Fusarium wilt diseased-tomato plants using hyperspectral data. In-situ reflectance measurements of healthy and infected plants over a tomato growing region (Tumkur, Karnataka, India) have been collected, processed, and analyzed to spectrally differentiate diseased and healthy plants. Various statistical and machine-learning approaches have been applied for assessing the spectral discrimination of diseased plants with sensitivity to five different levels of disease severity. Results suggest the existence of stable spectral features which differentiate healthy and diseases plants at distinct levels of disease severity. The discrimination has substantial variance with the method used. Amongst the methods used, Linear Discriminant Analysis (LDA), Gradient Boosting, and XGBoost methods offer spectral discrimination of about 80% accuracy.