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

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

grasserie; histogram oriented gradient; machine learning; sericulture; silkworm

One of the major challenges associated with the sericulture industry is silkworm diseases, as they are very difficult to detect in the early stages. 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 are used for this study. The dataset is analyzed with machine learning techniques for image analysis, features are extracted from the pre-processed images using combining Histogram of Oriented Gradients (HOG) and the higher dimensional features are reduced with Kernel Principal Component Analysis (KPCA), and classification using supervised 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

Sania Thomas1, Binson V A2*, Sini Rahuman3, Sivakumar K S4

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6705/1166

Department of Computer Science and Engineering, Saintgits College of Engineering, Kerala, India

October 27, 2025

FAJAR BAGUS W

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