A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

Shweta Lamba; Vinay Kukreja; Junaid Rashid; Thippa Reddy Gadekallu; Jungeun Kim; Anupam Baliyan; Deepali Gupta; Shilpa Saini

doi:10.3389/fpls.2023.1234067

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

Front Plant Sci. 2023 Sep 5:14:1234067. doi: 10.3389/fpls.2023.1234067. eCollection 2023.

Authors

Shweta Lamba¹, Vinay Kukreja², Junaid Rashid³, Thippa Reddy Gadekallu^{4

5

6

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8}, Jungeun Kim⁹, Anupam Baliyan¹⁰, Deepali Gupta², Shilpa Saini¹⁰

Affiliations

¹ Chandigarh Engineering College, CGC Landran, Mohali, India.
² Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
³ Department of Data Science, Sejong University, Seoul, Republic of Korea.
⁴ Department of Research and Development, Zhongda Group, Jiaxing, Zhejiang, China.
⁵ Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
⁶ School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
⁷ College of Information Science and Engineering, Jiaxing University, Jiaxing, China.
⁸ Division of Research and Development, Lovely Professional University, Phagwara, India.
⁹ Department of Software and CMPSI, Kongju National University, Cheonan, Republic of Korea.
¹⁰ Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, India.

Abstract

Introduction: Paddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.

Methods: In this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.

Results: Three infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.

Discussion: The findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models.

Keywords: generative adversarial network; multi-class classification; paddy diseases; severity classification; severity detection.

Grants and funding

This research was partly supported by the Technology Development Program of MSS (No. S3033853) and by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2020R1I1A3069700).