A Novel Hybrid Severity Prediction Model for Blast Paddy Disease Using Machine Learning

Shweta Lamba, Vinay Kukreja, Anupam Baliyan, Shalli Rani () and Syed Hassan Ahmed
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Shweta Lamba: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Vinay Kukreja: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Anupam Baliyan: Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali 140055, Punjab, India
Shalli Rani: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Syed Hassan Ahmed: Computer Science, State California University, Los Angles, CA 90032, USA

Sustainability, 2023, vol. 15, issue 2, 1-12

Abstract: Hypothesis : Due to the increase in the losses in paddy yield as a result of various paddy diseases, researchers are working tirelessly for a technological solution to assist farmers in making decisions about disease severity and potential danger to the crop. Early prediction of infection severity would facilitate resources for the treatment of the infection and prevent contamination to the whole field. Methodology : In this study, a hybrid prediction model was developed to predict various levels of severity of blast disease based on diseased plant images. The proposed model is a four-fold severity prediction model. The level of severity is defined based on the percentage of leaf area affected by the disease. The image dataset is derived from both primary and secondary resources. Tools : The features are first extracted with the help of the Convolutional Neural Network (CNN) approach. Then the identification and classification of the severity level of blast disease are conducted using a Support Vector Machine (SVM). Conclusion : Mendeley, Kaggle, GitHub, and UCI are the secondary resources used for dataset generation. The number of images in the dataset is 1908. The proposed hybrid model achieves 97% accuracy.

Keywords: CNN–SVM hybrid classifier; leaf blast; rice diseases; disease severity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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