ArticleData in brief2024
Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.
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 18 papers.
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Who cites it
18 citing papers in PubMed.
- Letter to the Editor. Re: "Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications," Data in brief 53 (2024) 110268.Data in brief · 2026Article
- A curated dataset of 3,477 high-resolution Grapevine (Vitis vinifera) leaf images for automated detection of Black Rot, Esca, and Leaf Blight diseases.Scientific data · 2026Article
- PLDC-Net: A Domain-Specific Base Model for Plant Leaf Disease Classification Domain Adaptation Tasks.Plant direct · 2026Article
- ReMA: a residual gated multi-head attention module for MobileViT in sugarcane diseases and disease recognition.Frontiers in plant science · 2026Article
- Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications.Current research in food science · 2026Review
- CaneFocus-Net: A Sugarcane Leaf Disease Detection Model Based on Adaptive Receptive Field and Multi-Scale Fusion.Sensors (Basel, Switzerland) · 2025Article
- Grapes leaf disease dataset for precision agriculture.Data in brief · 2025Article
- A comprehensive dataset of mandarin leaf images for classification.Data in brief · 2025Article
- Assessing the performance of domain-specific models for plant leaf disease classification: a comprehensive benchmark of transfer-learning on open datasets.Scientific reports · 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
- Facilitating spice recognition and classification: An image dataset of Indian spices.Data in brief · 2024Article
- A dataset revolutionizing Indian bay leaf analysis.Data in brief · 2024Article
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- Multi-object urban dataset: A resource for detecting pedestrians, traffic and motorbikes.Data in brief · 2024Article
- HelmetML: A dataset of helmet images for machine learning applications.Data in brief · 2024Article
- Dried fish dataset for Indian seafood: A machine learning application.Data in brief · 2024Article
- Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.Data in brief · 2024Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the "Sugarcane Leaf Dataset," we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields.
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