Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification

Meenakshi Aggarwal, Vikas Khullar (), Nitin Goyal, Aman Singh, Amr Tolba, Ernesto Bautista Thompson and Sushil Kumar
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Meenakshi Aggarwal: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Vikas Khullar: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Nitin Goyal: Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendragarh 123031, Haryana, India
Aman Singh: Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
Amr Tolba: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Ernesto Bautista Thompson: Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
Sushil Kumar: Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendragarh 123031, Haryana, India

Agriculture, 2023, vol. 13, issue 5, 1-24

Abstract: Rice is a staple food for roughly half of the world’s population. Some farmers prefer rice cultivation to other crops because rice can thrive in a wide range of environments. Several studies have found that about 70% of India’s population relies on agriculture in some way and that agribusiness accounts for about 17% of India’s GDP. In India, rice is one of the most important crops, but it is vulnerable to a number of diseases throughout the growing process. Farmers’ manual identification of these diseases is highly inaccurate due to their lack of medical expertise. Recent advances in deep learning models show that automatic image recognition systems can be extremely useful in such situations. In this paper, we propose a suitable and effective system for predicting diseases in rice leaves using a number of different deep learning techniques. Images of rice leaf diseases were gathered and processed to fulfil the algorithmic requirements. Initially, features were extracted by using 32 pre-trained models, and then we classified the images of rice leaf diseases such as bacterial blight, blast, and brown spot with numerous machine learning and ensemble learning classifiers and compared the results. The proposed procedure works better than other methods that are currently used. It achieves 90–91% identification accuracy and other performance parameters such as precision, Recall Rate, F1-score, Matthews Coefficient, and Kappa Statistics on a normal data set. Even after the segmentation process, the value reaches 93–94% for model EfficientNetV2B3 with ET and HGB classifiers. The proposed model efficiently recognises rice leaf diseases with an accuracy of 94%. The experimental results show that the proposed procedure is valid and effective for identifying rice diseases.

Keywords: rice leaf disease; machine learning; deep learning; ensemble learning; segmentation; pre-trained models (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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