Deep learning-based rice pest detection research

Peng Xiong, Cong Zhang, Linfeng He, Xiaoyun Zhan and Yuantao Han

PLOS ONE, 2024, vol. 19, issue 11, 1-20

Abstract: With the increasing pressure on global food security, the effective detection and management of rice pests have become crucial. Traditional pest detection methods are not only time-consuming and labor-intensive but also often fail to achieve real-time monitoring and rapid response. This study aims to address the issue of rice pest detection through deep learning techniques to enhance agricultural productivity and sustainability. The research utilizes the IP102 large-scale rice pest benchmark dataset, publicly released by CVPR in 2019, which includes 9,663 images of eight types of pests, with a training-to-testing ratio of 8:2. By optimizing the YOLOv8 model, incorporating the CBAM (Convolutional Block Attention Module) attention mechanism, and the BiFPN (Bidirectional Feature Pyramid Network) for feature fusion, the detection accuracy in complex agricultural environments was significantly improved. Experimental results show that the improved YOLOv8 model achieved mAP@0.5 and mAP@0.5:0.95 scores of 98.8% and 78.6%, respectively, representing increases of 2.8% and 2.35% over the original model. This study confirms the potential of deep learning technology in the field of pest detection, providing a new technological approach for future agricultural pest management.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0313387 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 13387&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0313387

DOI: 10.1371/journal.pone.0313387

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().