ArticleSensors (Basel, Switzerland)2021
Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Analysis of tomato bacterial wilt transmission dynamics with behavioral responses: A mathematical and optimal control approach.Parasite epidemiology and control · 2026Article
- Fungal infections: Classification performance and detectability with machine learning models.Scientific reports · 2026Article
- Artificial Intelligence (AI) in Detection of Abiotic Stress in Plants: A Review.Sensors (Basel, Switzerland) · 2026Review
- Genetic enhancement of root, tuber and cereal crops via pangenomics, multi-omics integration and AI-driven prediction.Frontiers in plant science · 2026Review
- Lightweight dual-stage feature refinement for black gram leaf disease classification using ConViTSE.Scientific reports · 2025Article
- A novel hybrid fruit fly and simulated annealing optimized faster R-CNN for detection and classification of tomato plant leaf diseases.Scientific reports · 2025Article
- PlantCareNet: an advanced system to recognize plant diseases with dual-mode recommendations for prevention.Plant methods · 2025Article
- An Efficient Model for Leafy Vegetable Disease Detection and Segmentation Based on Few-Shot Learning Framework and Prototype Attention Mechanism.Plants (Basel, Switzerland) · 2025Article
- Deciphering the anthelmintic mechanisms ofIn silico pharmacology · 2025Article
- CNNAttLSTM: an attention-enhanced CNN-LSTM architecture for high-precision jackfruit leaf disease classification.Frontiers in plant science · 2025Article
- Harnessing artificial intelligence for sustainable rice leaf disease classification.Frontiers in plant science · 2025Article
- Classification of tomato leaf images for detection of plant disease using conformable polynomials image features.MethodsX · 2024Article
- Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Disease in a Typical South India Station.Sensors (Basel, Switzerland) · 2024Article
- Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing.Sensors (Basel, Switzerland) · 2024Article
- Bridging Convolutional Neural Networks and Transformers for Efficient Crack Detection in Concrete Building Structures.Sensors (Basel, Switzerland) · 2024Article
- Maize Leaf Disease Recognition Based on Improved Convolutional Neural Network ShuffleNetV2.Plants (Basel, Switzerland) · 2024Article
- An efficient deep learning model for tomato disease detection.Plant methods · 2024Article
- Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework.Frontiers in artificial intelligence · 2024Article
- TomatoGuard-YOLO: a novel efficient tomato disease detection method.Frontiers in plant science · 2024Article
- Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification.PloS one · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease's effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.
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