A Framework for Insect Detection and Classification in Paddy Images

India, known for its heavy reliance on agriculture with 70% of its population engaged in farming, places great importance on crops like paddy. However, agricultural productivity faces challenges due to uncontrollable factors such as rainfall patterns, temperature fluctuations, and overall climate conditions. Among these challenges, insect infestations pose a significant threat to crop yields. To address this issue, there is a growing need for innovative solutions such as automated agricultural systems that can accurately identify and manage plant pests through image analysis techniques. In this context, the integration of advanced features like BAG and SURF, along with classifiers such as SVM and k-NN, is pivotal. The performance evaluation of the IR2PI system reveals that employing the k-NN classifier leads to a remarkable 95% accuracy, showcasing its potential for enhancing agricultural productivity and sustainability.