@article{JIN2026, 
author = {Ye-Ning JIN and Xin-Ru XIAO and Shu-Lian SHAN and Gui-Jun WAN and Fa-Jun CHEN},
title = {Research progress and application status of intelligent monitoring technology for agricultural pests based on the "three tropisms"},
year = {2026},
journal = {Journal of Environmental Entomology},
volume = {48},
number = {1},
pages = {9-28},
keywords = {Insect pests, light-trap monitoring, sex-hormone-trap monitoring, food-trap monitoring, intelligent monitoring and early warning},
url = {https://www.sciopen.com/article/10.3969/j.issn.1674-0858.2026.01.2},
doi = {10.3969/j.issn.1674-0858.2026.01.2},
abstract = {Under global climate change, the occurrence, outbreak and the related mechanisms of agricultural insect pests are increasingly complex and changeable, posing a serious threat to agricultural production. Traditional monitoring and early warning methods are difficult to meet the needs of precision management of insect pests in modern agriculture due to their low efficiency and poor accuracy. Recently many studies were carried out and indicated that the monitoring and early warning technologies of agricultural insect pests, such as the mornitoring technologies of light trapping, sex-hormone trapping and food traping (e.g. pest monitoring light, remote sex-hormone monitoring system etc.), have been rapidly developed based on the phototaxis, chemotaxis and sitotaxis (collectively termed the "three tropisms") of insect pests. These technologies, combined with automatic counting, intelligent identification and the Internet of Things (IoT), have facilitated the construction of intelligent monitoring and early warning systems. This paper reviewed the mechanism of three main monitoring and early warning technologies of insect pests based on their "three tropisms" (i.e., light trapping, sex-hormone trapping, and food trapping), as well as the development status and application prospect of intelligent monitoring and early warning technologies of insect pests, and pointed out the significant advantages of these technologies in improving the monitoring efficiency and prediction accuracy of insect pests. It also analyzed the existing problems and challenges in the application of these technologies (e.g., susceptibility to the environment, and involvement of multiple specialized disciplines and shortage of relevant professionals). In the future, it is necessary to integrate multi-source data and intelligent Al model to further optimize monitoring technologies and build intelligent monitoring and early warning platforms for insect pests in order to ensure agricultural production safety and promote sustainable development of agriculture.}
}