ArticleScientific reports2023
Construction of deep learning-based disease detection model in plants.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 121 citations in OpenAlex.
- Empathy in the Soil, Agents on the Desktop: Why Plant Pathology Needs a Scientist-in-the-Loop AI Paradigm.The plant pathology journal · 2026Article
- A Deep Learning Model for Chili Pepper Fruit Shape Classification Using DenseNet-121 and CBAM.Plants (Basel, Switzerland) · 2026Article
- AgriX-SENet: Squeeze-and-Excitation-based deep learning framework for explainable plant disease detection in sustainable agriculture.Frontiers in plant science · 2026Article
- Attention guided convolutional neural network with explainable AI for papaya leaf disease detection in edge and drone agricultural systems.Scientific reports · 2025Article
- Classification of cotton leaf disease using YOLOv8 based k-fold cross validation deep learning method for precision agriculture.Scientific reports · 2025Article
- FSID: a novel approach to human activity recognition using few-shot weight imprinting.Scientific reports · 2025Article
- PlantCareNet: an advanced system to recognize plant diseases with dual-mode recommendations for prevention.Plant methods · 2025Article
- A knowledge graph for crop diseases and pests in China.Scientific data · 2025Article
- Article
- Remote sensing-based detection of brown spot needle blight: a comprehensive review, and future directions.PeerJ · 2025Review
- PND-Net: plant nutrition deficiency and disease classification using graph convolutional network.Scientific reports · 2024Article
- ERCP-Net: a channel extension residual structure and adaptive channel attention mechanism for plant leaf disease classification network.Scientific reports · 2024Article
- Deep learning and explainable AI for classification of potato leaf diseases.Frontiers in artificial intelligence · 2024Article
- EfficientRMT-Net-An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately detecting disease occurrences of crops in early stage is essential for quality and yield of crops through the decision of an appropriate treatments. However, detection of disease needs specialized knowledge and long-term experiences in plant pathology. Thus, an automated system for disease detecting in crops will play an important role in agriculture by constructing early detection system of disease. To develop this system, construction of a stepwise disease detection model using images of diseased-healthy plant pairs and a CNN algorithm consisting of five pre-trained models. The disease detection model consists of three step classification models, crop classification, disease detection, and disease classification. The 'unknown' is added into categories to generalize the model for wide application. In the validation test, the disease detection model classified crops and disease types with high accuracy (97.09%). The low accuracy of non-model crops was improved by adding these crops to the training dataset implicating expendability of the model. Our model has the potential to apply to smart farming of Solanaceae crops and will be widely used by adding more various crops as training dataset.
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