Application of Deep Learning in Image Recognition of Citrus Pests

Author:

Jia Xinyu12ORCID,Jiang Xueqin12ORCID,Li Zhiyong12ORCID,Mu Jiong12,Wang Yuchao3,Niu Yupeng12

Affiliation:

1. College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China

2. Ya’an Digital Agricultural Engineering Technology Research Center, Ya’an 625000, China

3. College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China

Abstract

The occurrence of pests at high frequencies has been identified as a major cause of reduced citrus yields, and early detection and prevention are of great significance to pest control. At present, studies related to citrus pest identification using deep learning suffer from unbalanced sample sizes between data set classes, which may cause slow convergence of network models and low identification accuracy. To address the above problems, this study built a dataset including 5182 pest images in 14 categories. Firstly, we expanded the dataset to 21,000 images by using the Attentive Recurrent Generative Adversarial Network (AR-GAN) data augmentation technique, then we built Visual Geometry Group Network (VGG), Residual Neural Network (ResNet) and MobileNet citrus pest recognition models by using transfer learning, and finally, we introduced an appropriate attention mechanism according to the model characteristics to enhance the ability of the three models to operate effectively in complex, real environments with greater emphasis placed on incorporating the deep features of the pests themselves. The results showed that the average recognition accuracy of the three models reached 93.65%, the average precision reached 93.82%, the average recall reached 93.65%, and the average F1-score reached 93.62%. The integrated application of data augmentation, transfer learning and attention mechanisms in the research can significantly enhance the model’s ability to classify citrus pests while saving training cost and time, which can be a reference for researchers in the industry or other fields.

Funder

Sichuan Provincial Department of Science and Technology

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

Reference45 articles.

1. An Exploration of Factors Affecting Development of Citrus Industry in Tanzania: Empirical Evidence From Muheza District, Tanga Region;Makorere;Int. J. Food Agric. Econ.,2014

2. Effect of Huanglongbing or Greening Disease on Orange Juice Quality, a Review;Plotto;Front. Plant Sci.,2019

3. (2023, March 09). UC IPM Publication: Integrated Pest Management for Citrus. Available online: https://ipm.ucanr.edu/IPMPROJECT/ADS/manual_citrus.html.

4. (2023, March 09). (PDF) Survey on the Situation of Citrus Pest Management in Mediterranean Countries. Available online: https://www.researchgate.net/publication/263965064_Survey_on_the_situation_of_citrus_pest_management_in_Mediterranean_countries.

5. (2023, March 09). Introduction to Integrated Pest Management|US EPA, Available online: https://www.epa.gov/ipm/introduction-integrated-pest-management.

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3