Pest detection system using machine learning techniques

Abstract

Countries like Bangladesh yield a significant portion of their economy from their agricultural sector. Agricultural pests, on the other hand, have a significant impact on both agricultural production and crop storage. The pest category must be precisely identified, and specific management actions must be adopted as a prevention technique against these pests. As a result, a computer vision-based agricultural pest recognition system must be developed. The implications of certain prospective machine learning algorithms, like Support Vector Machine, Inceptionv3, and Xception, are discussed in this research to achieve insect detection with the complicated agriculture setting. In this study, the dataset used are images of mainly 5 common pests found in a paddy field in Bangladesh. The results achieved from the models were studied based on their accuracy and loss percentage to determine the better approach for such detection to take necessary actions. In this research, SVM outperformed both InceptionV3 and Xception with an accuracy of about 72.5%.