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Rice field pest detector based on deep learning and embedded system

Published: 20 September 2024 Publication History

Abstract

To improve the efficiency of rice field pest identification, reduce labor costs, and solve the problem of one-sided judgment in manual detection, a pest detection method based on deep learning and an embedded system was proposed and a portable instrument was designed. The detection model was first obtained by training 1500 rice field image datasets of pests through a YOLOv2-MobileNet network, which was then deployed into a device, and real-time detection of common rice paddy pests was realized. To reduce the impact of model deployment on the device performance, the detection accuracy and image capture frame rate were improved by adjusting the width of the MobileNet network. The results showed that the average detection accuracy of the model trained by the backbone network MobileNet-0.75 was 89.4%, 80.7%, 90.0%, 81.9%, and 83.6% for the locust, rice leaf roller, rice stem borer, rice green mirid nymph, and rice green adult, respectively, and the average image acquisition frame rate reached 35 frames per second. Real-time detection requirements were met. The design realizes real-time detection of five common rice field pests using embedded equipment and provides a reference value for the application of intelligent equipment for the detection of agricultural pests.
CCS CONCEPTS • Computing methodologies • Artificial intelligence • Computer vision • Computer vision problems • Object detection
Additional Keywords and Phrases: Pest detection, YOLOv2, Embedded system, K210

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FAIML '24: Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning
April 2024
379 pages
ISBN:9798400709777
DOI:10.1145/3653644
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 20 September 2024

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