Advanced Tea Crop Disease Study: High-Resolution Dataset for Precision Agriculture and Pathological Insight
Description
1. The horticulture industry places a high value on tea plants because of their economic importance and widespread consumption. Nevertheless, several diseases might substantially negatively influence their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution images of tea plant leaves were taken from two different garden at the Moulvi Bazar site in Sylhet, Bangladesh, and are included in this dataset. The images are divided into four distinct classes: Healthy (2270 images), Tea Leaf Blight (509 images), Tea Red Leaf Spot (561 images), and Tea Red Scab (620 images). These classes represent both damaged and healthy leaves. The dataset has 3960 images in total. Comprehensive comments describing the condition's nature and severity are included with every photograph. This data is essential for accurate and trustworthy model training and validation. 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 tea 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 two different tea garden at the Moulvi Bazar site in Sylhet, Bangladesh, a total of 3960 images depicting Healthy (2270 images), Tea Leaf Blight (509), Tea Red Leaf Spot (561), and Tea Red Scab (620) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 4000 augmented images are made from these original photos to increase the quantity of data sets.