Corn leaf recognition method based on YOLOv5 model
Corn leaf is the main organ of corn for photosynthesis,and the rapid detection of its physiological ecology plays a crucial role in the quality and yield of corn.Therefore,this paper proposes a corn leaf recognition method based on the YOLOv5 model.The image data of corn plant growth period is acquired by using Kinect 2.0 camera level,and by using YOLOv5 model,corn leaf target detection is realized and compared with YOLOv3 and YOLOv7.The result shows that the precision,recall,and F1-score of the YOLOv5 model reached 94.7%,95.2%,and 93.4%,respectively,and compared with YOLOv3 and YOLOv7,its mean average precision value is improved by 3.9% and 1.4% .The results injects strong impetus to the research and development of real-time detection system for corn leaf,and also demonstrates its indispensable technical support in the field of artificial intelligence breeding and estimation yield.
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