Yashiro Toshihisa, Takayama Tomohiko, Sugiura Ryo, Matsumura Masaya, Sanada-Morimura Sachiyo
Koshi Campus, Institute for Plant Protection, National Agriculture and Food Research Organization (NARO), Koshi, 861-1192, Japan.
Kurume Campus, Kyushu Okinawa Agricultural Research Center, National Agriculture and Food Research Organization (NARO), Kurume, 839-8503, Japan.
Sci Rep. 2025 Jul 1;15(1):22078. doi: 10.1038/s41598-025-05908-y.
Rice planthoppers are the most economically important insect pests of rice in Asia. Traditional surveys to examine their abundance and composition in paddy fields involve human visual inspection, which requires considerable time and effort by expert entomologists. We previously developed a deep learning-based object detection system which can detect rice planthopper individuals from scanned images of survey boards, categorize, and count planthopper individuals by 18 categories, based on species, developmental stages, adult sexes, and adult wing-forms, with a mean average precision (mAP) of 79%. In this study, we modified the system by reconsidering the categories of planthopper individuals to be counted and by additional supervised training. The modified system can count rice planthopper individuals captured on survey boards across 17 categories with a mAP of 91%. We also showed that by using the system developed here, classification and counting of rice planthopper individuals can be completed in approximately six minutes per survey board, which can take more than an hour for human experts. Thus, this high-performance system can greatly save time and reduce labor costs for monitoring the occurrence, reproduction, and population growth of the rice planthoppers in paddy fields, which could increase the efficiency of their management.
稻飞虱是亚洲对水稻经济影响最为重大的害虫。传统的稻田稻飞虱数量及种类调查方法是人工目视检查，这需要专业昆虫学家投入大量时间和精力。我们此前开发了一种基于深度学习的目标检测系统，该系统能够从调查板的扫描图像中检测出稻飞虱个体，并根据物种、发育阶段、成虫性别和成虫翅型，将稻飞虱个体分为18类进行分类和计数，平均精度均值（mAP）为79%。在本研究中，我们通过重新考虑待计数的稻飞虱个体类别并进行额外的监督训练，对该系统进行了改进。改进后的系统能够对调查板上捕获的稻飞虱个体按17类进行计数，mAP为91%。我们还表明，使用此处开发的系统，每块调查板对稻飞虱个体的分类和计数大约可在6分钟内完成，而人工专家则需要一个多小时。因此，这个高性能系统能够极大地节省时间并降低稻田稻飞虱发生、繁殖和种群增长监测的劳动力成本，从而提高其管理效率。
