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In this paper, artificial intelligence (ai) is used for the integration of machine learning (ml), deep learning (dl) and remote sensing technologies in order to increase precision pest prediction and control. The use of ai in agriculture can improve crop management techniques, disease and pest detection, and monitoring through drones and sensors, leading to increased output and product quality (naveed et al The benefits improved yields, resource efficiency, and faster response are highlighted, along with current challenges such as technical and regulatory hurdles.

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To assess the performance of the models employed in this study, a variety of evaluation metrics was utilized, each selected to provide insights into different aspects of model accuracy. This article explores the latest advances in pest monitoring using iot and ai technologies, highlighting case studies, benefits, and challenges associated with their implementation. The authors build practical pest monitoring systems based on deep learning detectors and evaluate their performance using agripest

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This dataset and associated code will be publicly available, facilitating further research in pest detection and precision agriculture.

The study was funded by scientific research fund project of zhengzhou university of economics and trade young in 2022 (grant no. Qk2228), and henan province higher education institution college student innovation training program project in 2023 (grant no.