An automatic system for pest recognition and forecasting. Issue 2 (30th October 2021)

• Record Type: Journal Article
• Authors: Wang, Rujing; Li, Rui; Chen, Tianjiao; Zhang, Jie; Xie, Chengjun; Qiu, Kun; Chen, Peng; Du, Jianming; Chen, Hongbo; Shao, FangRong; Hu, Haiying; Liu, Haiyun
• Is Part Of: Pest management science. Volume 78:Issue 2(2022)
• Page Start: 711
• Page End: 721
• Publication Date: 2021-10-30
• Subjects: pest counting -- pest control -- computer vision -- deep learning -- integrated pest management; Pests -- Control -- Periodicals; Pesticides -- Periodicals; 632.9
• DOI: 10.1002/ps.6684
• Languages: English
• ISSNs: 1526-498X

Abstract

Abstract: BACKGROUND: Pests cause significant damage to agricultural crops and reduce crop yields. Use of manual methods of pest forecasting for integrated pest management is labor‐intensive and time‐consuming. Here, we present an automatic system for monitoring pests in large fields, with the aim of replacing manual forecasting. The system comprises an automatic detection and counting system and a human–computer data statistical fitting system. Image data sets of the target pests from large fields are first input into the system. The number of pests in the image is then counted both manually and using the automatic system. Finally, a mapping relationship between counts obtained using the automated system and by agricultural experts is established using the statistical fitting system. RESULTS: Trends in the pest‐count curves produced using the manual and automated counting methods were very similar. To sample the number of pests for manual statistics, plants were shaken to transfer the pests from the plant to a plate. Hence, pests hiding within plant crevices were also sampled and included in the count, whereas the automatic method counted only the pests visible in the images. Therefore, the computer index threshold was much lower than the manual index threshold. However, the proposed system correctly reflected trends in pest numbers obtained using computer vision. CONCLUSION: The experimental results demonstrate that our automatic pest‐monitoring system can generate pest grades and can replace manual forecasting methods in large fields. © 2021 Society of Chemical Industry. Abstract : The framework of the automatic pest‐monitoring system comprises three subsystems: (i) an automatic detection and counting system based on deep‐learning techniques, (ii) a human–computer data statistical fitting system, and (iii) an integrated pest management system. The experimental results demonstrate that this system can automatically generate pest grades.