Jamun Leaf Disease Detection

Published: 4 December 2023| Version 2 | DOI: 10.17632/43d75vptz4.2


Description The Jamun Leaf Disease Detection Dataset is a comprehensive collection of high-quality images of jamun leaves, designed to facilitate the development of robust jamun leaf disease detection systems using deep learning models. The dataset was meticulously gathered from two prominent areas in Cumilla, Bangladesh: Titas and Barura. It encompasses a diverse range of jamun leaf conditions encountered in real-world jamun cultivation, ensuring its practical relevance. Dataset Characteristics Origin: Titas and Barura, Cumilla, Bangladesh Image Type: Color photographs Image Resolution: Varies Number of Images: Adequate for training and testing deep learning models Number of Classes: 6 Bacterial_Spot Brown Blight Dry Healthy Powdery Mildew Sooty Mold Usage The Jamun Leaf Disease Detection Dataset is primarily intended for developing and evaluating deep learning models for jamun leaf disease detection. It can also be used for various research purposes, such as analyzing the prevalence of different jamun leaf diseases and investigating the impact of environmental factors on leaf health. Significance The Jamun Leaf Disease Detection Dataset is a valuable resource for advancing the field of precision agriculture in Bangladesh. By enabling the development of accurate jamun leaf disease detection systems, it has the potential to significantly enhance jamun cultivation practices, leading to increased productivity and reduced economic losses.



Daffodil International University


Agricultural Science, Image Processing, Agricultural Engineering, Machine Learning