您的位置: 首页 > 院士专题 > 专题 > 详情页

Segmentation and counting algorithm for touching hybrid rice grains

杂交水稻籽粒触摸分割计数算法

关键词:
来源:
COMPUTERS AND ELECTRONICS IN AGRICULTURE
全文链接1:
http://agri.ckcest.cn/topic/downloadFile/9718c2b1-aa8c-4d72-bd82-dd006e7f2483
全文链接2:
http://agri.ckcest.cn/topic/downloadFile/9718c2b1-aa8c-4d72-bd82-dd006e7f2483
类型:
学术文献
语种:
英语
原文发布日期:
2019-05-02
摘要:
The ability to segment and count of touching hybrid rice grains can enable the automatic evaluation of seeding performance. In this paper, an algorithm that separates and counts touching rice grain, which consists of the watershed algorithm, an improved corner point detection algorithm, and neural network classification algorithm, is presented. To reduce the over-segmentation regions caused by the watershed algorithm, wavelet transform and Gaussian filter are first applied to enhance the contrast intensity of grayscale image and to reduce noise, followed by an improved corner point detection algorithm based on adaptive-radius circular template. The over-segmentation regions are identified and merged by detecting whether the end points of the splitting lines coincide with the corner points. Considering that regions of different grain quantity vary in appearance and corner point characteristic, a Back Propagation (BP) neural network classifier is employed to classify the under-segmentation regions into five categories: one grain, two grains, three grains, four grains, and more than four grains. The proposed algorithm was tested on three hybrid rice varieties under different realistic touching scenarios formed in the sowing process. The tests results showed that the corner point detection algorithm using an adaptive-radius circular template achieved better corner point accuracy than that using a fixed-radius template, and the over-segmentation regions were more accurately merged. For grain regions of different grain quantity, BP neural classifier achieved an average classification accuracy of 92.4%, which was suitable for counting rice grains in under-segmentation regions. The overall segmentation and counting method proposed in this study could achieve an average accuracy of 94.63%, which was verified by manual counting results.
相关推荐

意 见 箱

匿名:登录

个人用户登录

找回密码

第三方账号登录

忘记密码

个人用户注册

必须为有效邮箱
6~16位数字与字母组合
6~16位数字与字母组合
请输入正确的手机号码

信息补充