计算机科学
人工智能
特征(语言学)
过程(计算)
任务(项目管理)
主管(地质)
过度拟合
模式识别(心理学)
计算机视觉
人工神经网络
工程类
哲学
语言学
系统工程
地貌学
地质学
操作系统
作者
Yan Zhang,Manzhou Li,Xiaojing Ma,Xiaotong Wu,Yaojun Wang
标识
DOI:10.3389/fpls.2022.787852
摘要
Counting wheat heads is a time-consuming process in agricultural production, which is currently primarily carried out by humans. Manually identifying wheat heads and statistically analyzing the findings has a rigorous requirement for the workforce and is prone to error. With the advancement of machine vision technology, computer vision detection algorithms have made wheat head detection and counting feasible. To accomplish this traditional labor-intensive task and tackle various tricky matters in wheat images, a high-precision wheat head detection model with strong generalizability was presented based on a one-stage network structure. The model's structure was referred to as that of the YOLO network; meanwhile, several modules were added and adjusted in the backbone network. The one-stage backbone network received an attention module and a feature fusion module, and the Loss function was improved. When compared to various other mainstream object detection networks, our model outperforms them, with a mAP of 0.688. In addition, an iOS-based intelligent wheat head counting mobile app was created, which could calculate the number of wheat heads in images shot in an agricultural environment in less than a second.
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