ArticleSensors (Basel, Switzerland)2020
Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 137 citations in OpenAlex.
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- Advanced clustering and transfer learning based approach for rice leaf disease segmentation and classification.PeerJ. Computer science · 2025Article
- A systematic review of deep learning techniques for apple leaf diseases classification and detection.PeerJ. Computer science · 2025Review
- An improved YOLOv5-based apple leaf disease detection method.Scientific reports · 2024Article
- Deep migration learning-based recognition of diseases and insect pests in Yunnan tea under complex environments.Plant methods · 2024Article
- Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection.Frontiers in plant science · 2024Review
- An Analysis of Plant Diseases Identification Based on Deep Learning Methods.The plant pathology journal · 2023Article
- Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Cotton leaf segmentation with composite backbone architecture combining convolution and attention.Frontiers in plant science · 2023Article
- Construction and verification of machine vision algorithm model based on apple leaf disease images.Frontiers in plant science · 2023Article
- Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases.Plants (Basel, Switzerland) · 2022Article
- YOLOv5s-FP: A Novel Method for In-Field Pear Detection Using a Transformer Encoder and Multi-Scale Collaboration Perception.Sensors (Basel, Switzerland) · 2022Article
- Research on recognition method of leaf diseases of woody fruit plants based on transfer learning.Scientific reports · 2022Article
- Assessment for Different Neural Networks with FeatureSelection in Classification Issue.Sensors (Basel, Switzerland) · 2022Article
- Dilated convolution capsule network for apple leaf disease identification.Frontiers in plant science · 2022Article
- A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests.Computational intelligence and neuroscience · 2022Article
- Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Enhancing aphid detection framework based on ORB and convolutional neural networks.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to replace the fully connected layer to reduce the parameters and a batch normalization layer is added to improve the convergence speed. A transfer learning strategy is used to avoid a long training time. The experimental results show that the overall accuracy of apple leaf classification based on the proposed model can reach 99.01%. Compared with the classical VGG16, the model parameters are reduced by 89%, the recognition accuracy is improved by 6.3%, and the training time is reduced to 0.56% of that of the original model. Therefore, the deep convolutional neural network model proposed in this work provides a better solution for the identification of apple leaf diseases with higher accuracy and a faster convergence speed.
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