https://scholars.lib.ntu.edu.tw/handle/123456789/634303
標題: | Edge-based wireless imaging system for continuous monitoring of insect pests in a remote outdoor mango orchard | 作者: | Rustia, Dan Jeric Arcega Lee, Wei Che Lu, Chen Yi Wu, Ya Fang Shih, Pei Yu Chen, Sheng Kuan Chung, Jui Yung TA-TE LIN |
關鍵字: | Internet of things | Mango insect pests | Remote monitoring system | Sticky paper trap | Tree-based classifiers | 公開日期: | 1-八月-2023 | 卷: | 211 | 來源出版物: | Computers and Electronics in Agriculture | 摘要: | Mango production is a prominent tropical fruit industry worldwide. However, outdoor mango cultivation is susceptible to crop damage caused by insect pests and harsh environmental conditions. Integrated pest management (IPM) has emerged as a proposed solution to this problem. IPM utilizes data-driven and environmentally-friendly methods to suppress insect pest populations. Nevertheless, the collection of insect pest population data remains a laborious process, necessitating automation. This paper presents an image-based monitoring system to automatically record insect pest populations and environmental conditions in mango orchards. The system comprises solar-powered sensor nodes capable of periodically acquiring and analyzing sticky paper trap images. A modular deep learning-based algorithm was developed to detect and classify insect pests into seven classes, including major insect pests of mango such as thrips, mango leafhopper, and oriental fruit fly, with an average classification F1-score of 0.96. Unlike other insect counting algorithms, the algorithm reliably classifies insect pests according to different taxonomic levels even in non-laboratory environments. The monitoring system was tested and deployed in a remote mango orchard for over two years. The collected spatiotemporal information was analyzed to demonstrate the benefits of using the proposed system and recommend new IPM strategies. Temporal data analysis revealed a significant decrease in the count of selected insect pests after using the system, enabling identification of insect hotspots through statistical methods. This work presents a breakthrough in hardware and software solutions for developing smarter insect pest monitoring systems, leading to better IPM strategies. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164301722&doi=10.1016%2fj.compag.2023.108019&partnerID=40&md5=da961c295b05973da1b93171530dcfb5 https://scholars.lib.ntu.edu.tw/handle/123456789/634303 |
ISSN: | 01681699 | DOI: | 10.1016/j.compag.2023.108019 |
顯示於: | 生物機電工程學系 |
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