Paddy Doctor: A Visual Image Dataset for Paddy Disease Classification
- URL: http://arxiv.org/abs/2205.11108v1
- Date: Mon, 23 May 2022 07:57:40 GMT
- Title: Paddy Doctor: A Visual Image Dataset for Paddy Disease Classification
- Authors: Petchiammal A, Briskline Kiruba S, D. Murugan, Pandarasamy A
- Abstract summary: Paddy Doctor is a visual image dataset for identifying paddy diseases.
Our dataset contains 13,876 annotated paddy leaf images across ten classes (nine diseases and normal leaf)
We benchmarked the Paddy Doctor using a Convolutional Neural Network (CNN) and two transfer learning approaches, VGG16 and MobileNet.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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 Paddy Doctor, a visual image dataset for identifying paddy diseases.
Our dataset contains 13,876 annotated paddy leaf images across ten classes
(nine diseases and normal leaf). We benchmarked the Paddy Doctor using a
Convolutional Neural Network (CNN) and two transfer learning approaches, VGG16
and MobileNet. The experimental results show that MobileNet achieves the
highest classification accuracy of 93.83\%. We release our dataset and
reproducible code in the open source for community use.
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