Design a classifier to classify diseases in paddy based on leaf color

There is an increasing demand for automated systems capable of accurately diagnosing paddy diseases, which would help lower pesticide usage and prevent yield loss. Yet, the absence of publicly available datasets with annotated disease labels has posed a challenge to the development and benchmarking of advanced deep learning models. To address this issue, we created and open-sourced the Paddy Doctor dataset, facilitating the development of reliable and effective paddy disease diagnosis systems.

"Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking" is a specialized dataset designed to aid in the development and evaluation of machine learning models for the automated classification of diseases in paddy (rice) plants. This dataset typically includes a diverse collection of high-quality images of paddy leaves affected by various diseases, along with healthy samples. Each image is labeled according to the type of disease or condition it represents.

The winners will be awarded from a prize pool of 40,000 rupees.

 Reference: Petchiammal A, Briskline Kiruba S, Murugan D, Pandarasamy Arjunan, November 18, 2022, "Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking", IEEE Dataport, doi: https://dx.doi.org/10.21227/hz4v-af08.