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A lightweight YOLOv11n-based framework for accurate maize seedling detection and counting in field environments

Shi-Chao Wang Xin-Jing Feng Yi-Ze Wu Hao Ye Xiao-Li Wei Kun Li Xiang Li
Sep 2026 · Journal of Real-Time Image Processing · Vol 23 · 0 citations · 34 references
Smart Agriculture and AI

TL;DR

This study develops a lightweight maize seedling detection and counting model for field environments, with three key improvements based on the YOLOv11n architecture, providing an efficient and practical solution for automatic maize seedling detection and counting in field scenarios.

DOI Semantic Scholar
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