ArticleData in brief2024
BDPapayaLeaf: A dataset of papaya leaf for disease detection, classification, and analysis.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system.Scientific reports · 2026Article
- Agri-vision Bangladesh: A multi-crop augmented image dataset for automated disease diagnosis in Bottle Gourd, Zucchini, Papaya, and Tomato.Data in brief · 2026Article
- An annotated dataset of images of Chinese giant salamanders.Data in brief · 2026Article
- A lightweight convolutional neural network for real-time monitoring of smart mango orchard systems.Scientific reports · 2026Article
- HASPNet: a hierarchically attentive signal-preserving network for papaya leaf disease classification with explainable deep learning.Frontiers in artificial intelligence · 2026Article
- CS-D: A dataset for disease in tea leaf (Camellia sinensis) from Assam.Data in brief · 2025Article
- Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs).BioData mining · 2025Article
- Epidemiological studies on the incidence of papaya ringspot disease under Indian sub-continent conditions.Scientific reports · 2025Article
- CNNAttLSTM: an attention-enhanced CNN-LSTM architecture for high-precision jackfruit leaf disease classification.Frontiers in plant science · 2025Article
- BDPapayaLeaf: A dataset of papaya leaf for disease detection, classification, and analysis.Data in brief · 2024Article
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4 authors.
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Abstract
Papaya is a popular vegetable and fruit in both developing and developed countries. Nonetheless, Bangladesh's agricultural landscape is significantly influenced by papaya cultivation. However, disease is a common impediment to papaya productivity, adversely affecting papaya quality and yield and leading to substantial economic losses for farmers. Research suggests that computer-aided disease diagnosis and machine learning (ML) models can improve papaya production by detecting and classifying diseases. In this line, a dataset of papaya is required to diagnose the disease. Moreover, like many other fruits, papaya disease may vary from country to country. Therefore, the country-based papaya disease dataset is required. In this study, a papaya dataset is collected from Dhaka, Bangladesh. This dataset contains 2159 original images from five classes, including the healthy control class and four papaya leaf diseases: Anthracnose, Bacterial Spot, Curl, and Ring spot. Besides the original images, the dataset contains 210 annotated data for each of the five classes. The dataset contains two types of data: the
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