Early Detection of Grasserie Disease in Silkworms Using Computer Vision and Machine Learning

Silkworm diseases are a significant threat to the sericulture industry, with early detection remaining a major challenge due to limited resources. Timely identification of infected silkworms is essential to curb the spread of disease and reduce economic damage. This study focuses on diagnosing Grasserie disease, a highly contagious condition that can devastate silkworm populations, leading to substantial financial losses for farmers. To address the shortcomings of expert manual inspections, this study employed camera-captured images of silkworms for automated disease detection. A newly compiled dataset, consisting of 668 healthy silkworms and 574 infected with Grasserie disease, forms the basis of the investigation. The study applies machine learning techniques for image analysis, combining Histogram of Oriented Gradients (HOG) for feature extraction, Kernel Principal Component Analysis (KPCA) for dimensionality reduction, and supervised classification models. The results highlight the effectiveness of this approach in differentiating healthy silkworms from diseased ones. The machine learning model HOG integrated with KPCA and Decision Trees (DT) achieved strong performance, with accuracy, recall, and precision scores of 94.28%, 94.56%, and 92.48%, respectively. While these outcomes are encouraging, further research is needed to develop a practical IoT-based tool that enables sericulture farmers to quickly detect infections and take preventive measures, minimizing unexpected losses. This study marks a crucial advancement in silkworm disease detection, offering a pathway toward greater sustainability and economic stability in the sericulture sector.