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

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.

Data of history: An open-source and multiformat wall image dataset of Panam city, a historical place.

Historical data on monuments offers valuable insights into that period's past sculpture, architecture, and preferences. Realising the importance of historical data and the scarcity of data on historical places, this study presents a dataset collected from Panam City. Panam City, established in the late 1300s century, was the capital of the fifteenth-century Bengal ruler Isa Khan. The city was once an important trading and political centre and is now considered a world heritage site by the United Nations Educational Scientific and Cultural Organisation (UNESCO). Panam City is located in Sonargaon, Dhaka, Bangladesh. The aim of data collection is to capture past architectural design, materials used for the building, and the current state of the walls and structures of Panam City. This dataset can benefit researchers, architects, archaeologists, and cultural organisations. Historians and architects can gain insights into the wall's construction methods and materials, informing future restoration efforts. Historic datasets can create exciting AR/VR experiences by digitizing and 3D modelling historical artefacts and environments, integrating them into AR/VR platforms using game engines and development tools, and enhancing the user experience with interactive storytelling and educational content. Tourism boards and cultural heritage organisations can leverage this resource to develop engaging experiences that highlight the rich history and significance of Panama City. By making this data accessible, this study contributes to understanding and appreciating Panam City's historical significance while promoting innovative approaches to heritage preservation in the digital age. This dataset contains 2292 images of degraded wall classes such as Artistic, Corroded Brick, Corroded Plaster, Fungus, and Living Plant.

Multi-format open-source sweet orange leaf dataset for disease detection, classification, and analysis.

In Bangladesh, sweet orange cultivation has been popular among fruit growers as the fruit is in demand. However, the disease of sweet oranges decreases fruit production. Research suggests that computer-aided disease diagnosis and machine learning (IML) models can improve fruit production by detecting and classifying diseases. In this line, a dataset of sweet oranges is required to diagnose the disease. Moreover, like many other fruits, sweet orange disease may vary from country to country. Therefore, in Bangladesh, a sweet orange dataset is required. Lastly, since different ML algorithms require datasets in various formats, only a few existing datasets fulfil the necessity. To fulfil the limitations, a sweet orange dataset in Bangladesh is collected. The dataset was collected in August and comprises high-quality images documenting multiple disease conditions, including Citrus Canker, Citrus Greening, Citrus Mealybugs, Die Back, Foliage Damage, Spiny Whitefly, Powdery Mildew, Shot Hole, Yellow Dragon, Yellow Leaves, and Healthy Leaf. These images provide an opportunity to apply machine learning and computer vision techniques to detect and classify diseases. This dataset aims to help researchers advance agri engineering through ML. Other sweet orange growing countries with having similar environments may find helpful information. Lastly, such experiments using our dataset will assist farmers in taking preventive measures and minimising economic losses.