Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests
Joint Authors
He, Yong
Zeng, Hong
Fan, Yangyang
Ji, Shuaisheng
Wu, Jianjian
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-10
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Telecommunications Engineering
Abstract EN
In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model.
We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling.
We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model.
Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer.
The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management.
This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).
American Psychological Association (APA)
He, Yong& Zeng, Hong& Fan, Yangyang& Ji, Shuaisheng& Wu, Jianjian. 2019. Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests. Mobile Information Systems،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1193778
Modern Language Association (MLA)
He, Yong…[et al.]. Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests. Mobile Information Systems No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1193778
American Medical Association (AMA)
He, Yong& Zeng, Hong& Fan, Yangyang& Ji, Shuaisheng& Wu, Jianjian. Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests. Mobile Information Systems. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1193778
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193778