摘要
为快速、准确识别马铃薯芽眼,提高种薯发芽率,提出一种基于卷积神经网络的马铃薯芽眼识别方法。针对多视角和不同程度重叠的马铃薯芽眼图像,通过数据增广及图像预处理建立数据库。在此基础上,利用YOLOv3网络的高性能特征提取特性,实现马铃薯芽眼的快速准确识别。结果表明:YOLOv3网络对含有单个无遮挡芽眼的样本、含有多个有遮挡芽眼的样本及含有机械损伤、虫眼及杂质的样本均能够实现良好稳定的识别,最终检测精确度P为97.97%,召回率R为96.61%,调和平均值F1为97%,识别平均精度mAP为98.44%,单张检测时间为0.018 s。对比分析YOLOv4-tiny及SSD等网络后可知,YOLOv3模型可同时满足马铃薯芽眼识别的精度与速度要求。因此,YOLOv3网络对马铃薯芽眼识别具有良好的鲁棒性,为马铃薯切种机实现自动化切种奠定基础。
In order to quickly and accurately identify potato buds and improve the germination rate of seed potatoes,a potato buds recognition method based on convolutional neural network is proposed.The database of the potato buds images with multiple perspectives and different degrees of overlap is established by data augmentation and image preprocessing.Based on this,the rapid and accurate recognition of the potato buds is achieved by using the high-performance feature extraction characteristics of the YOLOv3 network.The result shows that the YOLOv3 network can achieve good and stable recognition for samples containing a single unobstructed bud,samples containing multiple obstructed buds,and samples containing mechanical damage as well as bug eyes and impurities.The final detection accuracy P is 97.97%,the recall rate R is 96.61%,the harmonic mean F1 is 97%,the mean recognition accuracy mAP is 98.44%,and the single detection time is 0.018 s.After comparing and analyzing networks such as YOLOv4-tiny and SSD,the YOLOv3 model can simultaneously meet the accuracy and speed requirements of potato buds recognition.Therefore,the YOLOv3 network has good robustness to the potato buds recognition,which lays the foundation for the realization of an automatic potato seed cutting machine.
作者
史方青
王虎林
黄华
Shi Fangqing;Wang Hulin;Huang Hua(School of Mechanical&Electrical Engineering,Lanzhou University of Technology,Lanzhou,730000,China)
出处
《中国农机化学报》
北大核心
2022年第6期159-165,共7页
Journal of Chinese Agricultural Mechanization
基金
国家自然科学基金(51965037)
兰州理工大学研究生教育质量工程(256017)。
