ArticleData in brief2024
A comprehensive cotton leaf disease dataset for enhanced detection and classification.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Cotton Leaf Spot Detection Based on an Improved YOLOv11n Model.Journal of imaging · 2026Article
- TriAttnNet based deep learning model for automated cotton pest detection and disease classification.Scientific reports · 2026Article
- Explainable transformer framework for fast cotton leaf diagnostics and fabric defect detection.iScience · 2026Article
- An attention enhanced CNN ensemble for interpretable and accurate cotton leaf disease classification.Scientific reports · 2026Article
- Smartphone image dataset for turmeric plant leaf disease from Bangladesh spice fields.Data in brief · 2025Article
- Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs).BioData mining · 2025Article
- DLCPD-25: A Large-Scale and Diverse Dataset for Crop Disease and Pest Recognition.Sensors (Basel, Switzerland) · 2025Article
- Classification of cotton leaf disease using YOLOv8 based k-fold cross validation deep learning method for precision agriculture.Scientific reports · 2025Article
- Okra disease dataset for classification and segmentation: Dataset collection, analysis and applications.Data in brief · 2025Article
- Resource-Efficient Cotton Network: A Lightweight Deep Learning Framework for Cotton Disease and Pest Classification.Plants (Basel, Switzerland) · 2025Article
- Explainable AI for Cotton Leaf Disease Classification: A Metaheuristic-Optimized Deep Learning Approach.Food science & nutrition · 2025Article
- A Comprehensive Review of Deep Learning Applications in Cotton Industry: From Field Monitoring to Smart Processing.Plants (Basel, Switzerland) · 2025Review
- A data-driven approach to turmeric disease detection: Dataset for plant condition classification.Data in brief · 2025Article
- A comprehensive image dataset for the identification of eggplant leaf diseases and computer vision applications.Data in brief · 2025Article
- A comprehensive hog plum leaf disease dataset for enhanced detection and classification.Data in brief · 2025Article
- Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning research.Data in brief · 2025Article
- Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm.PloS one · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The creation and use of a comprehensive cotton leaf disease dataset offer significant benefits in agricultural research, precision farming, and disease management. This dataset enables the development of accurate machine learning models for early disease detection, reducing manual inspections and facilitating timely interventions. It serves as a benchmark for testing algorithms and training deep learning models, aiding in automated monitoring and decision support tools in precision agriculture. This leads to targeted interventions, reduced chemical use, and improved crop management. Global collaboration is fostered, contributing to the development of disease-resistant cotton varieties and effective management strategies, ultimately reducing economic losses and promoting sustainable farming. Field surveys conducted from October 2023 to January 2024 ensured meticulous image capture under diverse conditions. The images are categorized into eight classes, representing specific disease manifestations, pests, or environmental stress in cotton plants. The dataset comprises 2137 original images and 7000 augmented images, enhancing deep learning model training. The Inception V3 model demonstrated high performance, with an overall accuracy of 96.03 %. This underscores the dataset's potential in advancing automated disease detection in cotton agriculture.
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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.