Comparison of key data before and after pruning.

<div><p>Sugarcane stem node detection is critical for monitoring sugarcane growth, enabling precision cutting, reducing spuriousness, and improving breeding for resistance to downfall. However, in complex field environments, sugarcane stem nodes often suffer from reduced detection accura...

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Bibliographic Details
Main Author:	Chunming Wen (17616274) (author)
Other Authors:	Leilei Liu (704957) (author), Shangping Li (17616277) (author), Yang Cheng (125394) (author), Qingquan Liang (22272169) (author), Kaihua Li (20826552) (author), Youzong Huang (5204372) (author), Xiaozhu Long (22272172) (author), Hongliang Nong (18017892) (author)
Published:	2025
Subjects:	Molecular Biology Developmental Biology Cancer Science Policy Plant Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified monitoring sugarcane growth experimental results show enabling precision cutting computational cost decreased complex field environments resolution detection layer object detection layer increased complexity introduced feature fusion layer better handle multi small target detection scale feature representations model &# 8217 lightweight shared detail bounding box regression p2 detection layer bounding box detection head detection caused p2 small overall complexity fusion module better deal better align using soft study incorporates shadowing effects parameter sharing parameter count paper proposes paper adopts original yolo11n model size maximum suppression improving breeding enhanced convolution background interference algorithm based
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Comparison of key data before and after pruning.

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