Advanced Tea Crop Disease Study: High-Resolution Dataset for Precision Agriculture and Pathological Insight

Published: 9 July 2024| Version 1 | DOI: 10.17632/tt2smzrzrs.1
MD Hasan Ahmad


1. The horticulture industry places a high value on tea plants because of their economic importance and widespread consumption. 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 tea plant leaves were taken at the Moulvi Bazar site in Sylhet, Bangladesh, and are included in this dataset. The photos are divided into four different classes: Healthy (73 images), Tea Leaf Blight (60 images), Tea Red Leaf Spot (60 images), and Tea Red Scab (60 images). These classes represent both damaged and healthy leaves. The dataset has 253 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 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 Moulvi Bazar site in Sylhet, Bangladesh, a total of 253 photos depicting Healthy (73), Tea Leaf Blight (60), Tea Red Leaf Spot (60), and Tea Red Scab (60) 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.



Daffodil International University


Computer Vision, Image Processing, Image Acquisition, Machine Learning, Image Classification, Deep Learning