摘要
Verticillium wilt is one of the most critical cotton diseases,which is widely distributed in cotton-producing countries.However,the conventional method of verticillium wilt investigation is still manual,which has the disadvantages of subjectivity and low efficiency.In this research,an intelligent vision-based system was proposed to dynamically observe cotton verticillium wilt with high accuracy and high throughput.Firstly,a 3-coordinate motion platform was designed with the movement range 6,100 mm×950 mm×500 mm,and a specific control unit was adopted to achieve accurate movement and automatic imaging.Secondly,the verticillium wilt recognition was established based on 6 deep learning models,in which the VarifocalNet(VFNet)model had the best performance with a mean average precision(mAP)of 0.932.Meanwhile,deformable convolution,deformable region of interest pooling,and soft non-maximum suppression optimization methods were adopted to improve VFNet,and the mAP of the VFNet-Improved model improved by 1.8%.The precision–recall curves showed that VFNet-Improved was superior to VFNet for each category and had a better improvement effect on the ill leaf category than fine leaf.The regression results showed that the system measurement based on VFNet-Improved achieved high consistency with manual measurements.Finally,the user software was designed based on VFNet-Improved,and the dynamic observation results proved that this system was able to accurately investigate cotton verticillium wilt and quantify the prevalence rate of different resistant varieties.In conclusion,this study has demonstrated a novel intelligent system for the dynamic observation of cotton verticillium wilt on the seedbed,which provides a feasible and effective tool for cotton breeding and disease resistance research.
基金
supported by grants from the Major Project of Hubei Hongshan Laboratory(2022hszd004)
the National Natural Science Foundation of China(32270431 and U21A20205)
the Key Research and Development Plan of Hubei Province(2022BBA0045 and 2020000071)
the Fundamental Research Funds for the Central Universities(2662022YJ018 and 2662019QD053).
作者简介
Address correspondence to:Peng Song,songp@mail.hzau.edu.cn;Address correspondence to:Longfu Zhu,lfzhu@mail.hzau.edu.cn
