Grasserie disease detection in silkworm using machine learning methods

Grasserie disease, caused by the Bombyx mori nuclear polyhedrosis virus (BmNPV), is one of the most severe diseases affecting silkworms, leading to significant economic losses in sericulture. This study leverages machine learning techniques to detect Grasserie disease in FC1 x FC2 double hybrid silkworms, using image processing and classification methods to automate detection. A dataset of 737 images, consisting of 322 infected and 415 healthy silkworms, was analyzed. Local Binary Patterns (LBP) were used for feature extraction, followed by Principal Component Analysis (PCA) for dimensionality reduction. A decision tree classifier achieved an accuracy of 92.1%, recall of 92.4%, precision of 90.2%, F1 score of 91.6%, and specificity of 90.7%. This paper outlines the methodology, discusses the results, and suggests future improvements for practical applications in sericulture.