Agriculture is a cornerstone of India’s economy, supporting the livelihood of a substantial portion of the population and contributing significantly to the nation’s gross domestic product (GDP). Agriculture plays a pivotal role in India's economy, sustaining a large portion of the population and making a significant contribution to the country’s GDP. Nonetheless, plant pest infestations pose a severe threat to agricultural productivity and food security. This research explores the use of deep learning techniques for the detection and classification of plant pests, with the goal of enhancing pest management practices in Indian agriculture. We developed a convolutional neural network (CNN) model, trained on an extensive dataset of pest images, which includes various species and their developmental stages. The model demonstrated high levels of accuracy, precision, and recall, proving its effectiveness in identifying pests across different plant species and environmental conditions. Our findings suggest that deep learning can greatly improve pest identification processes, offering a swift and dependable diagnostic tool. This advancement has the potential of 98.22% accuracy to aid Indian farmers and agricultural experts in reducing crop losses and improving yield quality, thereby contributing to the sustainability and resilience of the agricultural sector.