ArticlePlants (Basel, Switzerland)2022
Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases.
Article in Plants (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 36 citations in OpenAlex.
- Review
- Toward smart agriculture: a hybrid mamba-transformer vision framework for plant disease detection.Frontiers in plant science · 2026Article
- PnNAC03 from Panax notoginseng functions in positively regulating saponins and lignin biosynthesis during cell wall formation.Plant cell reports · 2025Article
- Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx.Plants (Basel, Switzerland) · 2025Article
- Enhancing the dataset of CycleGAN-M and YOLOv8s-KEF for identifying apple leaf diseases.PloS one · 2025Article
- Article
- Lightweight Algorithm for Apple Detection Based on an Improved YOLOv5 Model.Plants (Basel, Switzerland) · 2023Article
- Study on the Detection Method for Daylily Based on YOLOv5 under Complex Field Environments.Plants (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.
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