Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking
This addresses the challenge of limited crop protection experts in agriculture by providing a public dataset and benchmarking for automated disease detection, though it is incremental as it applies existing methods to new data.
The paper tackles the problem of automating paddy disease identification by introducing the Paddy Doctor dataset, which contains 16,225 annotated leaf images across 13 classes, and benchmarks it with models like ResNet34 achieving a 97.50% F1-score.
One of the critical biotic stress factors paddy farmers face is diseases caused by bacteria, fungi, and other organisms. These diseases affect plants' health severely and lead to significant crop loss. Most of these diseases can be identified by regularly observing the leaves and stems under expert supervision. In a country with vast agricultural regions and limited crop protection experts, manual identification of paddy diseases is challenging. 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 availability of public datasets with detailed disease information limits the practical implementation of accurate disease detection systems. This paper presents \emph{Paddy Doctor}, a visual image dataset for identifying paddy diseases. Our dataset contains 16,225 annotated paddy leaf images across 13 classes (12 diseases and normal leaf). We benchmarked the \emph{Paddy Doctor} dataset using a Convolutional Neural Network (CNN) and four transfer learning based models (VGG16, MobileNet, Xception, and ResNet34). The experimental results showed that ResNet34 achieved the highest F1-score of 97.50%. We release our dataset and reproducible code in the open source for community use.