Design of Precision Agriculture System using Automating Pink Bollworm Detection in Cotton Crops: AI based Digital Approach for Sustainable Pest Management

Cotton, a vital staple crop globally, faces a persistent threat from the relentless pink bollworm. As a fundamental fiber source, particularly crucial in the Indian textile industry, cotton holds significant economic value for many nations. However, the widespread presence of the pink bollworm presents a considerable economic challenge worldwide. With their heavy reliance on cotton as their primary habitat, exploring alternative crops like legumes or grains becomes imperative to disrupt their life cycle and reduce their population naturally. This paper presents an innovative approach to tackle the prevalent threat of pink bollworm infestations in cotton crops. It introduces an AI based algorithm tailored to automate the detection of these pests, specifically focusing on cotton fields. through the utilization of digital advancements such as Computer Vision and Artificial Intelligence, particularly leveraging CNNs and transfer learning, the goal is to streamline the identification process and offer timely recommendations for pest management. This strategy holds promise for not only boosting crop yields but also addressing the economic challenges associated with pink bollworm infestations. Furthermore, by providing a valuable resource for researchers in this field, the paper aims to drive advancements in pest management strategies, thereby contributing to the long-term sustainability of cotton cultivation.