IoT - Based Multispectral Imaging System for Early Detection of Diseases, Pest Infestations in Kochchi (Dark Green Scotch Bonnet) Plants Using Deep Learning Algorithms

Abstract

Kochchi, also known as the Dark Green Scotch Bonnet, is a valuable chili variety that thrives in Sri Lanka. However, the yield and quality of this crop often suffer due to pests and plant diseases. Traditional detection methods rely on manual inspection, which is labor-intensive, subjective, and typically identifies symptoms only after visible damage occurs. This study proposes a multispectral imaging (MSI) framework combined with deep learning algorithms to achieve early and accurate disease detection under greenhouse conditions. A DJI Mavic 3 Multispectral drone was used to capture synchronized RGB and multispectral bands (Green, Red, Red-Edge, NIR). Preprocessing involved band alignment, vegetation index computation (NDVI, NDRE, GNDVI), and reduce noise, all aimed at spotting early signs of distress before they become severe. A convolutional neural network (CNN) was trained for binary classification of healthy versus infected plants, achieving high accuracy across cross-validation. For plants classified as infected, severity mapping was performed using advanced instance segmentation models, YOLOv11 and Mask R-CNN, followed by HSV color transformation and K-means clustering to quantify lesion area as a percentage of total leaf surface. Evaluation using Accuracy, Precision, Recall, F1-score, Intersection over Union (IoU), and Dice Coefficient confirmed the robustness of the system. The proposed approach demonstrates the potential of multispectral imaging combined with deep learning for early disease detection and severity quantification in Kochchi cultivation, reducing reliance on pesticides and supporting sustainable agriculture.