Jute Leaf Disease Detection Dataset

This comprehensive dataset cover 920 images of jute leaves still from the agricultural heartlands of Dinajpur and Brahmanbaria, Bangladesh. It classify each image into one of three classes: Healthy, Cercospora Leaf Spot, or Golden Mosaic. Developed with the cooperation of local agricultural authorities, this dataset serves as a vital tool for advancing agritech. It enables the development of precise machine learning algorithms for detecting and classifying diseases in jute plants, contributing to enhanced crop health and sustainable farming initiatives. Perfect for agritech developers and AI researchers, this dataset is geared toward improving jute crop management and disease control.

Image Diversity

The dataset comprises thousands of images captured under various environmental conditions, illustrating different stages of disease progression. 

Detailed Annotations

Each image is meticulously annotated with disease type, severity level, and affected plant part, providing comprehensive information for precise disease diagnosis. The annotations facilitate accurate machine learning and deep learning model training.

Applications

This dataset is invaluable for developing automated disease detection systems, advancing agricultural research, and improving crop yield management. By leveraging this data, researchers can enhance early detection and effective management of petal problem, ultimately contributing to better crop health and increased agricultural productivity.