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BDPapayaLeaf: A dataset of papaya leaf for disease detection, classification, and analysis.

Data in brief · 10 Sept 2024 · 10.1016/j.dib.2024.110910

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 whole image and the annotated image . The image will interest data scientists who apply disease detection through a convolutional neural network (CNN) and its variants. Furthermore, the annotated images, such as You Only Look Once (YOLO), U-Net, Mask R-CNN, and Single Shot Detection (SSD), will be helpful for semantic segmentation. Since firm-applicable AI devices and mobile and web applications are in demand, the dataset collected in this study will offer multiple options for integrating ML models into AI devices. In countries with weather and climate similar to Bangladesh, data scientists may use their dataset in that context.

Plant phenotyping relevance

パパイヤ葉の病害状態を画像とアノテーションで記録したデータセット自体が中心であり、植物病害表現型の検出・分類・セグメンテーションに再利用可能な資源を提供している。

abstractThis 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.
abstractBesides the original images, the dataset contains 210 annotated data for each of the five classes.
abstractFurthermore, the annotated images, such as You Only Look Once (YOLO), U-Net, Mask R-CNN, and Single Shot Detection (SSD), will be helpful for semantic segmentation.

Code and data availability

The paper is a Data in Brief article describing the BDPapayaLeaf dataset of 2159 papaya leaf images (5 classes) with XML/TXT annotations, publicly deposited on Mendeley Data with a direct URL and DOI provided in the article.

Datasetpublic

d location, and the annotations were saved in XML and TXT forms for usage with various models. Data Source Location City: Changao, Ashulia, Dhaka Country: Bangladesh Coordinates: 23° 53′ 2″ N and 90° 19′ 28″ E Data accessibility Repository name: Mendeley Data Data identification number: 10.17632/p997fvf526.1 Direct URL to data: https://data.mendeley.com/datasets/p997fvf526/2 1 Value of the Data •

Open resource ↗Mendeley Data · 10.17632/p997fvf526.1 · lines:1-49

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