Groundnut crop pest detection and classification using comprehensive deeplearning models

Pests pose a significant threat to crops, leading to substantial economic losses and decreased food production. Early detection and accurate classification of pests in crops are crucial for effective pest management strategies. In this study, we propose a method for pest detection and classification in groundnut crops using deep learning models. In this research, we compare the performance of three deep learning models, namely Custom CNN [proposed], LeNet-5, and VGG-16, for groundnut pest detection and classification. A comprehensive dataset containing images of diverse groundnut crop pests, including thrips, aphids, armyworms, and wireworms, from the IP102 dataset was utilized for model evaluation. The performance is evaluated using reliability metrics such as accuracy and loss. These findings demonstrate the utility of deep learning models for reliable pest classification of groundnut crops. The Custom CNN [proposed] model demonstrates high training accuracy but potential overfitting, while the VGG-16 model performs well on both training and test data, showcasing its ability to generalize. The models' accuracy in predicting pest species underscores their capability to capture and utilize visual patterns for precise classification. These findings underscore the potential of deep learning models, particularly the VGG-16 model, for pest detection and classification in groundnut crops. The knowledge gained from this study can contribute to the development of practical pest management strategies and aid in maintaining crop health and productivity. Further analysis and comparisons with other models are recommended to comprehensively evaluate the competitiveness and suitability of deep learning models in real -world applications of pest detection and classification in agricultural settings.