ArticleJournal of imaging2023
BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 74 citations in OpenAlex.
- GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management.Scientific reports · 2026Article
- Explainable VGG16 transfer learning with SHAP and grad-CAM for wax moth pest and infestation detection in honeybee apiaries using imaging data.Scientific reports · 2026Article
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- CropCLR-Wheat: A Label-Efficient Contrastive Learning Architecture for Lightweight Wheat Pest Detection.Insects · 2025Article
- Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection.Scientific reports · 2025Article
- Article
- Principal component analysis and fine-tuned vision transformation integrating model explainability for breast cancer prediction.Visual computing for industry, biomedicine, and art · 2025Article
- Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization.Frontiers in plant science · 2024Article
- Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism.Frontiers in plant science · 2024Article
- Plant pest and disease lightweight identification model by fusing tensor features and knowledge distillation.Frontiers in plant science · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.