A Novel Intelligent System for Dynamic Observation of Cotton Verticillium Wilt

Abstract

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.

Research on Remote-Sensing Identification Method of Typical Disaster-Bearing Body Based on Deep Learning and Spatial Constraint Strategy

“…Various components, such as CBL, SPP, CSP, Focus, Concat, add, and Conv, are integrated for feature processing and fusion. VariFocalNet, on the other hand, is a dense target detection algorithm [33] that extracts feature maps using YOLOv5 as a base model. It uses VariFocal Loss on these feature maps to improve the detection performance and subsequently applies the NMS algorithm in post-processing to eliminate redundant detections.…”

Inhibition of Verticillium Wilt in Cotton through the Application of Pseudomonas aeruginosa ZL6 Derived from Fermentation Residue of Kitchen Waste

“…Verticillium wilt is one of the most critical diseases affecting cotton due to its extensive transmission routes, severe damage, and complex infection mechanism [ 8 ], it is widely distributed in cotton-producing countries [ 9 ]. Cotton Verticillium wilt is primarily caused by the soil fungus V. dahliae [ 10 ] and results in the wilting, fading, and shedding of cotton leaves [ 11 , 12 ].…”