The parameters of the training phase.

<div><p>Detecting <i>Trichosanthes Kirilowii</i> Maxim (Cucurbitaceae) in complex mountain environments is critical for developing automated harvesting systems. However, the environmental characteristics of brightness variation, inter-plant occlusion, and motion-induced blurr...

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Bibliographic Details
Main Author:	Zhongjian Xie (4633099) (author)
Other Authors:	Xinwei Chen (255125) (author), Weilin Wu (13736) (author), Yao Xiao (227567) (author), Yuanhang Li (5017802) (author), Yaya Zhang (11055897) (author), ZhuXuan Wan (20435933) (author), Weiqi Chen (1806721) (author)
Published:	2025
Subjects:	Biotechnology Cancer Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified range aggregation network mean average precision knowledge distillation techniques high computational intensity experimental results showed experiment significantly enhanced complex mountain environments channel pruning techniques 9 %, 63 8 %, 88 scale feature layer 6 %, 68 6 %, 3 div >< p 8 %, 0 yolov8 ), kpd trichosanthes kirilowii </ gd reached 93 4 %, respectively 0 %, 0 improved network kpd tiny </ p lightweight convolutional module 6 %, kirilowii </ 4 %, feature extraction named kpd study proposes standard convolutions stage algorithms similar studies plant occlusion mainstream single induced blurring improved yolov7 harvesting operations environmental characteristics efficient long dynamic head detection algorithm detection accuracy brightness variation
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The parameters of the training phase.

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