Different model detection results comparison.

<div><p>This study proposes the S-YOLOv10-ASI algorithm to improve the accuracy of tea identification and harvesting by robots, integrating a slice-assisted super-reasoning technique. The algorithm improves the partial structure of the YOLOv10 network through space-to-depth convolution....

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Bibliographic Details
Main Author:	Chunhua Yang (346871) (author)
Other Authors:	Wenxia Yuan (3778042) (author), Qiang Zhao (105948) (author), Zejun Wang (4045592) (author), Bowu Song (21647663) (author), Xianqiu Dong (21647666) (author), Yuandong Xiao (21647669) (author), Shihao Zhang (5165825) (author), Baijuan Wang (12588724) (author)
Published:	2025
Subjects:	Genetics Biotechnology Developmental Biology Cancer Plant Biology Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified experimental results demonstrate approximately 10 %. loss function calculation anji white tea 10 %, 7 1 %, 6 classification loss 99 %, 69 %, target tea xlink "> validation set two leaves training set study proposes stage transmission single bud seen improvements resolves conflicts recognition ability reasoning technique reasoning algorithm partial structure one leaf one bud low resolution key layers fresh leaves depth convolution assisted super asi algorithm ap values adjacent layers
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Different model detection results comparison.

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