Use of machine learning techniques to detect and classify selected fungal diseases in rice crop using hyperspectral imaging

Fungal diseases cause significant yield losses in rice, making early detection and accurate classification essential for effective disease management. In this study, hyperspectral imaging technique was used to acquire the spectral signatures of three major fungal diseases viz., brown spot, blast and sheath blight in rice. The acquired hyperspectral images were pre-processed using Standard Normal Variate (SNV) transformation and Savitzky-Golay filtering, followed by pixel-wise spectral data extraction. Principal Component Analysis (PCA) was used to investigate spectral variability among healthy and diseased leaf samples. Subsequently, machine learning models including artificial neural networks (ANN), support vector machines (SVM) and random forests (RF) were employed to classify these diseases based on the acquired and pre-processed spectral signature data. The results indicated that the ANN model outperform the others, achieving an accuracy of 98%, followed by SVM at 94%, and RF at 88%. Among the three models, the ANN exhibited the highest accuracy, precision and recall, making it the most effective model for disease detection and classification. Hyperspectral imaging, combined with machine learning, offers an affordable and efficient solution for large-scale detection and assessment of fungal diseases in rice crops.