|Table of Contents|

Study on Segmentation Algorithm of Cotton Verticillium WiltDisease Spot in Cotton Field Under Complex Background(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年04期
Page:
127-134
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
Study on Segmentation Algorithm of Cotton Verticillium WiltDisease Spot in Cotton Field Under Complex Background
Author(s):
Yan Jingkun; Huang Yuxian; Qin Weisen; Gao Pan
College of Information Science and Technology,Shihezi University,Shihezi 832003,China
Keywords:
machine vision; deep learning; semantic segmentation; cotton disease; field environment
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2021.04.017
Abstract:
In the process of cotton verticillium wilt segmentation and spot extraction using machine vision technology in the field environment,there will be over-segmentation and missegmentation due to the influence of the light and shade of weeds with similar pixels of cotton leaves. In order to solve the problems,a two-stage segmentation algorithm combining the DeepLabv3+ model based on transfer learning and the K-means clustering mechanism of centroid selection was proposed in this paper. First,the infected leaves were extracted from the complex background by the improved DeepLabv3+ segmentation model based on deep enhancement. Then,the initial centroid was selected from the HSV color space of the leaves,and the disinfected patches were obtained by the K-means clustering algorithm. Last,the deep transfer learning technology can be used to transfer the knowledge learned from the source domain(Kaggle NPDD data set)to the target domain(cotton infected leaves),and the problems of over-segmentation and missegmentation were effectively alleviated. The results showed that the segmentation index value of cotton infected leaves was 98.87%,and that of verticillium wilt spot was 87.29%. The algorithm proposed in this paper can effectively segment infected cotton leaves and disinfected cotton patches in complex background images,with stronger timing and higher accuracy. It can provide technical support for further recognition and processing of cotton disinfected leaves images in the future,and provide an algorithm reference for the development of crop pest identification technology.

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Last Update: 2021-12-15