Automated and Preventive Paddy Disease Diagnosis System
Diseases and pests severely affect paddy farming and lead to as much as 70% loss in the total yield. Expert supervision is usually required to mitigate these diseases. With the limited availability of crop protection experts, manual disease identification is a tedious task. Thus, to add a solution to this problem, it is necessary to automate the disease identification process and provide easily accessible decision support tools to enable effective crop protection measures. However, the lack of public datasets with detailed disease information limits the practical implementation of automated disease detection systems leveraging advanced image processing and deep learning techniques.
A subset of this dataset (10,407 annotated images) has been used in the Kaggle competition - https://www.kaggle.com/c/paddy-disease-classification/
The Paddy Doctor dataset submitted in an IEEE DataPort - https://ieee-dataport.org/documents/paddy-doctor-visual-image-dataset-automated-paddy-disease-classification-and-benchmarking
Note: The final and complete dataset will be released after compeltition of the competition (by end of Auguest 2022).