{"title":"A high-available segmentation algorithm for corn leaves and leaf spot disease based on feature fusion","authors":"","doi":"10.1016/j.cropro.2024.106957","DOIUrl":null,"url":null,"abstract":"<div><p>Detection and identification of corn diseases are crucial for disease control, and the segmentation of corn disease leaf image is a key step to achieve this goal. However, the images of diseased leaves in real fields are usually very complex, with characteristics of irregular shapes, blurred boundaries and unsharp background, which poses great challenges to disease prevention. To address this issue, our team constructed a dataset of diseased leaves with 857 images. Additionally, this paper proposes a high-availability segmentation algorithm for corn leaves with leaf spot disease, called SEF-UNet, which uses Res-UNet as the backbone network. The algorithm references SElayer and ELA (Efficient Local Attention). Simultaneously,we implement a feature fusion network that focuses on the output of each layer. Experimental results indicate that the Mean Intersection over Union (mIOU),Mean Pixel Accuracy (mPA), Mean Precision (mPrecision), and Mean Recall (mRecall),metrics of SEF-UNet network reach 92.62%, 95.74%, 96.63% and 95.64%.We compared our proposed method with UNet, Res-UNet, PspNet, DeepLabv3+, DANet, CCNet, Segformer-b3, and SEF-UNet under the same experimental conditions. The results demonstrate that our method significantly improves the accuracy of diseased leaf image segmentation. It provides a reference method for disease monitoring, as well as a technical basis for assessing disease severity.</p></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Crop Protection","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0261219424003855","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
Abstract
Detection and identification of corn diseases are crucial for disease control, and the segmentation of corn disease leaf image is a key step to achieve this goal. However, the images of diseased leaves in real fields are usually very complex, with characteristics of irregular shapes, blurred boundaries and unsharp background, which poses great challenges to disease prevention. To address this issue, our team constructed a dataset of diseased leaves with 857 images. Additionally, this paper proposes a high-availability segmentation algorithm for corn leaves with leaf spot disease, called SEF-UNet, which uses Res-UNet as the backbone network. The algorithm references SElayer and ELA (Efficient Local Attention). Simultaneously,we implement a feature fusion network that focuses on the output of each layer. Experimental results indicate that the Mean Intersection over Union (mIOU),Mean Pixel Accuracy (mPA), Mean Precision (mPrecision), and Mean Recall (mRecall),metrics of SEF-UNet network reach 92.62%, 95.74%, 96.63% and 95.64%.We compared our proposed method with UNet, Res-UNet, PspNet, DeepLabv3+, DANet, CCNet, Segformer-b3, and SEF-UNet under the same experimental conditions. The results demonstrate that our method significantly improves the accuracy of diseased leaf image segmentation. It provides a reference method for disease monitoring, as well as a technical basis for assessing disease severity.
期刊介绍:
The Editors of Crop Protection especially welcome papers describing an interdisciplinary approach showing how different control strategies can be integrated into practical pest management programs, covering high and low input agricultural systems worldwide. Crop Protection particularly emphasizes the practical aspects of control in the field and for protected crops, and includes work which may lead in the near future to more effective control. The journal does not duplicate the many existing excellent biological science journals, which deal mainly with the more fundamental aspects of plant pathology, applied zoology and weed science. Crop Protection covers all practical aspects of pest, disease and weed control, including the following topics:
-Abiotic damage-
Agronomic control methods-
Assessment of pest and disease damage-
Molecular methods for the detection and assessment of pests and diseases-
Biological control-
Biorational pesticides-
Control of animal pests of world crops-
Control of diseases of crop plants caused by microorganisms-
Control of weeds and integrated management-
Economic considerations-
Effects of plant growth regulators-
Environmental benefits of reduced pesticide use-
Environmental effects of pesticides-
Epidemiology of pests and diseases in relation to control-
GM Crops, and genetic engineering applications-
Importance and control of postharvest crop losses-
Integrated control-
Interrelationships and compatibility among different control strategies-
Invasive species as they relate to implications for crop protection-
Pesticide application methods-
Pest management-
Phytobiomes for pest and disease control-
Resistance management-
Sampling and monitoring schemes for diseases, nematodes, pests and weeds.