ArticleMolecular breeding : new strategies in plant improvement2023
Peanut leaf disease identification with deep learning algorithms.
Article in Molecular breeding : new strategies in plant improvement, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Precise identification of tomato leaf diseases based on the real background of DPEN.Scientific reports · 2026Article
- Spectral image classification of asymptomatic peanut leaf diseases based on deep learning algorithms.Plant methods · 2025Article
- Rethinking feature representation and attention mechanisms in intelligent recognition of leaf pests and diseases in wheat.Scientific reports · 2025Article
- A review of plant leaf disease identification by deep learning algorithms.Frontiers in plant science · 2025Review
- An intelligent identification for pest and disease detection in wheat leaf based on environmental data using multimodal data fusion.Frontiers in plant science · 2025Article
- YOLOv5s-BiPCNeXt, a Lightweight Model for Detecting Disease in Eggplant Leaves.Plants (Basel, Switzerland) · 2024Article
- Implementation and Evaluation of Spatial Attention Mechanism in Apricot Disease Detection Using Adaptive Sampling Latent Variable Network.Plants (Basel, Switzerland) · 2024Article
- GLNet: global-local feature network for wheat leaf disease image classification.Frontiers in plant science · 2024Article
- Detection and identification of plant leaf diseases using YOLOv4.Frontiers in plant science · 2024Article
- MixSeg: a lightweight and accurate mix structure network for semantic segmentation of apple leaf disease in complex environments.Frontiers in plant science · 2023Article
- 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
- TeaDiseaseNet: multi-scale self-attentive tea disease detection.Frontiers in plant science · 2023Article
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5 authors.
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Abstract
Peanut is an essential food and oilseed crop. One of the most critical factors contributing to the low yield and destruction of peanut plant growth is leaf disease attack, which will directly reduce the yield and quality of peanut plants. The existing works have shortcomings such as strong subjectivity and insufficient generalization ability. So, we proposed a new deep learning model for peanut leaf disease identification. The proposed model is a combination of an improved X-ception, a parts-activated feature fusion module, and two attention-augmented branches. We obtained an accuracy of 99.69%, which was 9.67%-23.34% higher than those of Inception-V4, ResNet 34, and MobileNet-V3. Besides, supplementary experiments were performed to confirm the generality of the proposed model. The proposed model was applied to cucumber, apple, rice, corn, and wheat leaf disease identification, and yielded an average accuracy of 99.61%. The experimental results demonstrate that the proposed model can identify different crop leaf diseases, proving its feasibility and generalization. The proposed model has a positive significance for exploring other crop diseases' detection. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01370-8.
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