文献类型： 外文期刊
作者： Wang, Jiale 1 ; Chen, Yan 1 ; Huang, Jianxiang 1 ; Jiang, Xunyuan 1 ; Wan, Kai 1 ;
作者机构： 1.Guangdong Acad Agr Sci, Inst Qual Stand & Monitoring Technol Agroprod, Guangzhou 510000, Peoples R China
2.Guangdong Prov Key Lab Qual & Safety Risk Assessme, Guangzhou, Guangdong, Peoples R China
3.Minist Agr & Rural Affairs Peoples Republ China, Key Lab Testing & Evaluat Agroprod Safety & Qual, Guangzhou, Guangdong, Peoples R China
关键词： application; bibliometrics; deep learning; development trends; pest management
期刊名称：JOURNAL OF APPLIED ENTOMOLOGY （ 影响因子：1.9； 五年影响因子：2.1 ）
ISSN： 0931-2048
年卷期： 2024 年
页码：
收录情况： SCI
摘要： Insects have flourished in various ecosystems owing to their evolutionary prowess. However, certain behaviours have led specific species to be classified as pests in human-dominated settings. Ensuring accurate pest identification and assessing risks are vital for both agricultural productivity and effective pest control. While traditional methods, based on manual checks and expert opinions, tend to be time-consuming and error-prone, machine learning (ML)-a branch of artificial intelligence-has brought groundbreaking shifts in computer vision and predictive analytics, paving the way for advanced agricultural methods. This study delves into a bibliometric analysis of the confluence between ML and pest control from 1999 to 2022. Drawing data from 2348 publications in the Web of Science (WoS) databases, we identified a marked uptick in interest after 2017-a decade marked by a 40-fold growth in publication numbers. An examination of 706 WoS core articles offered insights into temporal and geographic trends, co-citation patterns, key publications, and recurring keywords. Also, we spotlight major ML techniques employed in pest management and hint at promising directions for subsequent research. Overall, this paper serves as an exhaustive resource for individuals intrigued by the intersection of computer science and agriculture.
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