ArticleScientific reports2022
Research on recognition method of leaf diseases of woody fruit plants based on transfer learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 20 citations in OpenAlex.
- An interpretable crop leaf disease and pest identification model based on prototypical part network and contrastive learning.Scientific reports · 2025Article
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production.Sensors (Basel, Switzerland) · 2025Review
- The pipelines of deep learning-based plant image processing.Quantitative plant biology · 2025Review
- PND-Net: plant nutrition deficiency and disease classification using graph convolutional network.Scientific reports · 2024Article
- A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images.Frontiers in plant science · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Fruit leaf diseases have a significant impact on the later development and maturity of fruits, so rapid and accurate identification of fruit leaf diseases plays an important role in the development of fruit production. In this paper, the leaf disease data set of 6 kinds of fruits is divided into 25 categories according to the species-the type of the disease-the severity, and we propose an improved model based on ResNet101 to identify woody fruit plant leaf diseases, in which a global average pooling layer is used to reduce model training parameters, layer normalization, dropout and L2 regularization are used to prevent model overfitting, SENet attention mechanism is used to improve the model's ability to extract features. At the same time, transfer learning is used to reduce training time and training parameters. Experimental results show that the overall accuracy of woody fruit plant leaf recognition based on this model can reach 85.90%. Compared with the classic ResNet network, the accuracy is increased by 1.20%, and the model parameters are reduced by 98.14%. Therefore, the model proposed in this paper provides a better solution for the identification of leaf diseases of woody fruit plants and has a higher accuracy rate.
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