Early Detection and Classification of Apple Leaf Diseases by utilizing IFPA Genetic Algorithm with MC-SVM, SVI and Deep Learning Methods

Donu Jose V; K Santhi

doi:10.17485/IJST/v15i29.1235

Article

Early Detection and Classification of Apple Leaf Diseases by utilizing IFPA Genetic Algorithm with MC-SVM, SVI and Deep Learning Methods

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i29.1235

Year: 2022, Volume: 15, Issue: 29, Pages: 1440-1450

Original Article

Early Detection and Classification of Apple Leaf Diseases by utilizing IFPA Genetic Algorithm with MC-SVM, SVI and Deep Learning Methods

Donu Jose V^1*, K Santhi²

¹Research Scholar, Department of Computer Science, Dr.N.G.P Arts and Science College, Coimbatore, 641048, Tamilnadu, India
²Professor, Department of Information Technology, Dr.N.G.P Arts and Science College, Coimbatore, 641048, Tamilnadu, India

*Corresponding Author
Email: [email protected]

Received Date:12 June 2022, Accepted Date:22 June 2022, Published Date:28 July 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To propose a new model for early detection and classification of apple leaf diseases by means of genetic algorithms and advanced deep learning methods in order to minimize plant degradation and maximize detection accuracy. Methods: A feature selection and extraction task is carried out by using an Improvised Flower Pollination Technique (IFPA) based genetic algorithm. Deep learning techniques such as Multi Class Support Vector Machine (MC-SVM) and Spectral Vegetation Indices (SVI) are used to classify and detect disease. The Region Growing Algorithm (RGA) and the Stochastic Gradient Descent Method (SGDM) are used to detect leaf colour segments and identify the disease at an early stage based on shape, texture, and colour features. IFPA-GA with MC-SVM and SVI extracts the features from the apple leaf dataset (collected from the Apple Experiment Station of Northwest A&F University, China) and then selects the appropriate function to perform classification and detection of disease at early occurrence and to improve the accuracy level. To demonstrate the efficiency of the proposed algorithm, MATLAB is used for implementation. The performance results are evaluated and compared to the existing models such as Faster R-CNN, R-SSD, and INARSSD. Findings: Early disease detection and classification of leaf diseases are achieved with 94.09% accuracy level, 93.07% Speed, 94.01% sensitivity, 93.38% specificity, 95.01% precision, 93.17% recall, 190 True Positive, 110 True Negative, and 92.07% F-Score to detect the disease and classify it in an optimized manner, which is high compared to the existing versions. Novelty: According to the findings of the comprehensive study, the proposed detection and classification method IFPA-GA with SVM-SVI outperforms Faster R-CNN, R-SSD, and INAR-SSD in terms of accuracy and speed of apple leaf disease detection at an early occurrence by classifying it in a robust manner.

Keywords: Apple Leaf Disease Detection; SVM; Genetic Algorithm; Deep Learning; Image Processing

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Copyright

© 2022 Jose V & Santhi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)