{"title":"Crop disease diagnosis and prediction using two-stream hybrid convolutional neural networks","authors":"","doi":"10.1016/j.cropro.2024.106867","DOIUrl":null,"url":null,"abstract":"<div><p>Crop diseases significantly impact yield and quality, posing a direct threat to food security. The application of Convolutional Neural Networks (CNN) in crop disease recognition has notably improved diagnosis accuracy and efficiency. This study presents an innovative crop disease classification model based on the VGG-16 network. Enhancements include the incorporation of Batch Normalization (BN) and a novel activation function synergizing with Exponential Linear Units (ELU), improving model convergence speed and accuracy. Additionally, Global Average Pooling (GAP) is integrated to streamline the network architecture, and the InceptionV2 module is introduced to extract leaf disease features from different dimensions, enhancing model robustness. Validation on the PlantVillage dataset shows an accuracy rate of 98.89%, demonstrating the model's competitiveness and its potential to support sustainable agricultural production.</p></div>","PeriodicalId":10785,"journal":{"name":"Crop Protection","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-07-30","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/S0261219424002953","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
Crop diseases significantly impact yield and quality, posing a direct threat to food security. The application of Convolutional Neural Networks (CNN) in crop disease recognition has notably improved diagnosis accuracy and efficiency. This study presents an innovative crop disease classification model based on the VGG-16 network. Enhancements include the incorporation of Batch Normalization (BN) and a novel activation function synergizing with Exponential Linear Units (ELU), improving model convergence speed and accuracy. Additionally, Global Average Pooling (GAP) is integrated to streamline the network architecture, and the InceptionV2 module is introduced to extract leaf disease features from different dimensions, enhancing model robustness. Validation on the PlantVillage dataset shows an accuracy rate of 98.89%, demonstrating the model's competitiveness and its potential to support sustainable agricultural production.
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.