Multi Classification of Pest using Transfer Learning

The integration of machine learning and deep learning algorithms such as K-means, SVM, ResNet, and VGGNet signifies a significant advancement in agricultural development. Accurate and precise pest identification is crucial for farmers facing challenges in pest management. This paper adopts the MobileNet architecture to enhance pest classification accuracy. Leveraging a dataset containing 19 distinct pest classes enables the algorithm to train across diverse pest categories, facilitating efficient classification. By incorporating MobileNet, the model improves its ability to accurately identify pests, thereby providing farmers with a valuable tool for effective pest management and crop protection.