Detection and counting of pigment glands in cotton leaves using improved U-Net

Author:

She Lixuan,Wang Nan,Xu Yaxuan,Wang Guoning,Shao Limin

Abstract

Gossypol, as an important oil and raw material for feed, is mainly produced by cotton pigment gland, and has a wide range of applications in the fields of pharmaceutics, agriculture and industry. Accurate knowledge of the distribution of pigment gland in cotton leaves is important for estimating gossypol content. However, pigment glands are extremely small and densely distributed, manual counting is laborious and time-consuming, and difficult to count quickly and accurately. It is thus necessary to design a fast and accurate gland counting method. In this paper, the machine vision imaging technology is used to establish an image acquisition platform to obtain cotton leaf images, and a network structure is proposed based on deep learning, named as Interpolation-pooling net, to segment the pigment glands in the cotton leaf images. The network adopts the structure of first interpolation and then pooling, which is more conducive to the extraction of pigment gland features. The accuracy of segmentation of the model in cotton leaf image set is 96.7%, and the mIoU (Mean Intersection over Union), Recall, Precision and F1-score is 0.8181, 0.8004, 0.8004 and 0.8004 respectively. In addition, the number of pigment glands in cotton leaves of three different densities was measured. Compared with manual measurements, the square of the correlation coefficient (R2) of the three density pigment glands reached 0.966, 0.942 and 0.91, respectively. The results show that the proposed semantic segmentation network based on deep learning has good performance in the detection and counting of cotton pigment glands, and has important value for evaluating the gossypol content of different cotton varieties. Compared with the traditional chemical reagent determination method, this method is safer and more economical.

Publisher

Frontiers Media SA

Subject

Plant Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3