Advanced Dataset on Money Plant Diseases for AI Pathology Research
Description
1. The horticulture industry places a high value on money plants because of their hardiness and aesthetic attractiveness. Nevertheless, several illnesses might have a substantial negative influence on their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution photos of money plant leaves were taken at the Savar demonstration site in Dhaka, Bangladesh, and are included in this dataset. The photos are divided into three different classes: Manganese Toxicity (72 images), Bacterial Wilt Disease (66 images), and Healthy (175) images. These classes represent both damaged and healthy leaves. The dataset has 313 photos in total. Comprehensive comments that describe the nature and severity of the condition are included with every photograph. For accurate and trustworthy model training and validation, this data is essential. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information. 2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems. 3. To create deep learning techniques, an extensive money plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset. 4. From the Savar demonstration place in Dhaka, Bangladesh, a total of 313 photos depicting Bacterial Wilt Disease (66), Healthy (175), and Manganese Toxicity (72) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 15,000 augmented images are made from these original photos in order to increase the quantity of data sets.