WHEAT GRAINS AUTOMATIC COUNTING BASED ON LIGHTWEIGHT YOLOv8
Na MA,
Zhongtao LI,
Qingzhong KONG
Abstract:In order to accurately and quickly achieve wheat grain detection and counting, and to efficiently evaluate wheat quality and yield, a lightweight YOLOv8 algorithm is proposed to automatically count wheat grains in different scenarios. Firstly, wheat grain images are collected under three scenarios: no adhesion, slight adhesion, and severe adhesion, to create a dataset. Then, the neck network of YOLOv8 is modified to a bidirectional weighted fusion BiFPN to establish the wheat grain detection model. Finally, th… Show more
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