{"title":"Lightweight convolutional neural network-based plant disease identification for protection and landscape design","authors":"YuYang Wang, Feng Jiang, Hui Zhou","doi":"10.1016/j.cropro.2024.106828","DOIUrl":null,"url":null,"abstract":"<div><p>Plant diseases significantly impact landscape design, necessitating comprehensive consideration and effective management measures to ensure the health, aesthetics, and sustainability of landscapes. Early detection and timely control of plant diseases are crucial, but traditional monitoring methods, which rely on manual observation and sample collection, are inadequate for covering large garden areas and may delay necessary treatments. This study addresses these challenges by constructing a small Rosa chinensis disease dataset through field collection and data augmentation techniques. We propose MixResCoAtNet, an improved model based on the lightweight MixNet framework, to identify and categorize diseases from plant leaf images using convolutional neural networks (CNNs). Comparison experiments with various classical convolutional network models demonstrate that MixResCoAtNet outperforms these models, offering more competitive performance. Additionally, due to its lighter structure, MixResCoAtNet shows greater potential for deployment on mobile devices, facilitating real-time and efficient plant disease monitoring and management in landscape design.</p></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-06-27","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/S0261219424002564","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
Plant diseases significantly impact landscape design, necessitating comprehensive consideration and effective management measures to ensure the health, aesthetics, and sustainability of landscapes. Early detection and timely control of plant diseases are crucial, but traditional monitoring methods, which rely on manual observation and sample collection, are inadequate for covering large garden areas and may delay necessary treatments. This study addresses these challenges by constructing a small Rosa chinensis disease dataset through field collection and data augmentation techniques. We propose MixResCoAtNet, an improved model based on the lightweight MixNet framework, to identify and categorize diseases from plant leaf images using convolutional neural networks (CNNs). Comparison experiments with various classical convolutional network models demonstrate that MixResCoAtNet outperforms these models, offering more competitive performance. Additionally, due to its lighter structure, MixResCoAtNet shows greater potential for deployment on mobile devices, facilitating real-time and efficient plant disease monitoring and management in landscape design.
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.