Deep learning has been widely used in intelligent identification in the progress of real-time monitoring of crop pests. Based on the identification model of tea leafhopper, Empoasca onukii, the application of deep learning in field leafhopper recognition was introduced to improve the precision of field population investigation of E. onukii. In this paper, a method of identification and count of E. onukii in tea garden was designed. Firstly, yellow sticky card was used to attract tea leafhoppers, and images of cards were collected by camera and uploaded to the web server. Then, target detection algorithm deployed by the server was used to identify and count the leafhoppers in the images. Through algorithm screening, YOLOv3 was determined as the recognition algorithm, and the improved soft-NMS was used to replace the original NMS. K-means clustering method was used to calculate the size of the new prior frame, so as to improve the speed and precision of YOLOv3. The results show that the average precision of the optimized algorithm could reach more than 95.35% comparing with the real number of leafhoppers on the sticky card. Therefore, the combination of the sticky card trapping, target recognition algorithm and internet of things technology could realize the real-time monitoring of population for E. onukii, which could provide a reference for other insects with color preference and integrated pest management in tea gardens.
BIAN Lei
,
HE Xudong
,
JI Huihua
,
CAI Xiaoming
,
LUO Zongxiu
,
CHEN Huacai
,
CHEN Zongmao
. Research and Application of Intelligent Identification of Empoasca onukii Based on Machine Vision[J]. Journal of Tea Science, 2022
, 42(3)
: 376
-386
.
DOI: 10.13305/j.cnki.jts.20220506.001
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