ArticlePlant phenomics (Washington, D.C.)2023
A Precise Image-Based Tomato Leaf Disease Detection Approach Using PLPNet.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- SLR-YOLO: An Improved YOLO-Based Method for Accurate Detection of Potato Leaf Diseases in Complex Field Images.Plants (Basel, Switzerland) · 2026Article
- A Lightweight Real-Time Tomato Leaf Disease Detection System for Edge-Based Smart Agriculture.Sensors (Basel, Switzerland) · 2026Article
- Multi-class classification of plant leaf diseases using a hybrid deep neural transformer system and explainable AI techniques.Scientific reports · 2026Article
- MSP-Net: An Effective Multi-Scale Feature-Aware Detection Network for the Detection of Tomato Leaf Diseases.Plants (Basel, Switzerland) · 2026Article
- Phenotypic analysis method for 3D reconstruction of eggplant seedlings fused with background purification - based on the improved EggplantPointNet++ model and DBSCAN clustering.Frontiers in plant science · 2026Article
- EDDet: efficient deep-fusion and dynamic optimization for small target detection in eggplant diseases.BMC plant biology · 2025Article
- ChatLeafDisease: a chain-of-thought prompting approach for crop disease classification using large language models.Plant phenomics (Washington, D.C.) · 2025Article
- KBNet: A Language and Vision Fusion Multi-Modal Framework for Rice Disease Segmentation.Plants (Basel, Switzerland) · 2025Article
- CBSNet: An Effective Method for Potato Leaf Disease Classification.Plants (Basel, Switzerland) · 2025Article
- Identification of leaves of wild Ussurian Pear (Frontiers in plant science · 2025Article
- Detection techniques for tomato diseases under non-stationary climatic conditions.Frontiers in plant science · 2025Article
- DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments.Plant methods · 2024Article
- AI-powered detection and quantification of post-harvest physiological deterioration (PPD) in cassava using YOLO foundation models and K-means clustering.Plant methods · 2024Article
- Multi-kernel inception aggregation diffusion network for tomato disease detection.BMC plant biology · 2024Article
- A Precise Segmentation Algorithm of Pumpkin Seedling Point Cloud Stem Based on CPHNet.Plants (Basel, Switzerland) · 2024Article
- CRLNet: A Multimodal Peach Detection Network Based on Cooperative Asymptotic Enhancement and the Fusion of Granularity Refinement.Plants (Basel, Switzerland) · 2024Article
- High-Accuracy Tomato Leaf Disease Image-Text Retrieval Method Utilizing LAFANet.Plants (Basel, Switzerland) · 2024Article
- Lightweight cotton diseases real-time detection model for resource-constrained devices in natural environments.Frontiers in plant science · 2024Article
- Pepper-YOLO: an lightweight model for green pepper detection and picking point localization in complex environments.Frontiers in plant science · 2024Article
- Deep learning networks-based tomato disease and pest detection: a first review of research studies using real field datasets.Frontiers in plant science · 2024Review
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7 authors.
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Abstract
Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of environments, which can lead to intraclass variability and interclass similarity in the disease. Tomato plants are commonly planted in soil. When a disease occurs near the leaf's edge, the soil backdrop in the image tends to interfere with the infected region. These problems can make tomato detection challenging. In this paper, we propose a precise image-based tomato leaf disease detection approach using PLPNet. First, a perceptual adaptive convolution module is proposed. It can effectively extract the disease's defining characteristics. Second, a location reinforcement attention mechanism is proposed at the neck of the network. It suppresses the interference of the soil backdrop and prevents extraneous information from accessing the network's feature fusion phase. Then, a proximity feature aggregation network with switchable atrous convolution and deconvolution is proposed by combining the mechanisms of secondary observation and feature consistency. The network solves the problem of disease interclass similarities. Finally, the experimental results show that PLPNet achieved 94.5% mean average precision with 50% thresholds (mAP50), 54.4% average recall (AR), and 25.45 frames per second (FPS) on a self-built dataset. The model is more accurate and specific for the detection of tomato leaf diseases than other popular detectors. Our proposed method may effectively improve conventional tomato leaf disease detection and provide modern tomato cultivation management with reference experience.
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