Real Time Classification and Detection of Apple Leaf Diseases at Early Stage by Employing Enhanced Paddy Field Pattern Search Method (EPF-PSM) with 2LC

Jose V Donu; K Santhi

doi:10.17485/IJST/v16i22.1206

Article

Real Time Classification and Detection of Apple Leaf Diseases at Early Stage by Employing Enhanced Paddy Field Pattern Search Method (EPF-PSM) with 2LC

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i22.1206

Year: 2023, Volume: 16, Issue: 22, Pages: 1682-1693

Original Article

Real Time Classification and Detection of Apple Leaf Diseases at Early Stage by Employing Enhanced Paddy Field Pattern Search Method (EPF-PSM) with 2LC

Jose V Donu^1*, K Santhi²

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

*Corresponding Author
Email: [email protected]

Received Date:18 May 2023, Accepted Date:21 May 2023, Published Date:07 June 2023

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

Abstract

Objectives: To propose a suitable machine learning approach to detect and classify the leaf diseases in apple trees at the pre-mature state to reduce plant degradation. In order to boost the accuracy level, EPF-PSM (Enhanced Paddy Field-Based Pattern Search Method) is employed along with 2LC (Two Level Classifier) technique. Methods: Preprocessing images, which include FS and FE, is done by employing EPF-PSM to detect the apple leaf disease. 2LC classifier is utilized to classify the type of disease based on the available features like gradient, pixel, edges, etc. The decision tree algorithm is employed to search for patterns in images and compare them for accurate prediction. With MC-Apriori (Multi Label Classification Apriori), the relationship between infected and non-infected leaves in search space is explored, and RGB scheme (RGBCS) is employed to discover the colour depth ratio of infected leaves. EPFPSM extracts attributes from the ALDD-NW dataset (collected at the Apple Experiment Station of Northwest A&F University, China) and selects the suitable function to perform ALD classification, pre-mature detection, and accuracy. MATLAB software is utilized for implementation and assessment of the novel ML approach. The findings are compared with contemporary models such as the IFPA-GA with SVM-SVI, the FR-CNN, the R-SSD, and the INARSSD. Findings: The suggested model gives 98.01% accuracy rate, 97.06% detection speed in 29 seconds, 98.04% sensitivity, 95.92% specificity, 98.01% precision, 96.17% recall, 98.4% TPR, 99.03% TNR, and 96.07% F-Score, which is comparatively higher than the existing methods in terms of detection and classification of ALD. Novelty: The results shows that the suggested machine learning (ML) technique EPF-PSM with 2L-C has the ability to produce accurate detection and classification of ALD at the pre-mature stage, which helps the farmers to treat apple plants properly. The evident results outperform the prevailing methods IFPA-GA with SVM-SVI, FR-CNN, R-SSD, and INARSSD.

Keywords: Apple Leaf Disease Detection; Classification Techniques; Machine Learning Algorithm; Pattern Search Method; Paddy Field Method; Image Processing

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Copyright

© 2023 Donu & 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)