Automated Corn Seed Fusarium Disease Classification System Using Hybrid Feature Space and Conventional Machine Learning Techniques | [Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences • 2021]

Author(s):

1. Samreen Naeem: Department Food Science, College of Tourism & Hotel Management (COTHM), Bahawalpur, Pakistan; Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur, Pakistan.

2. Aqib Ali: Department of Computer Science, Concordia College Bahawalpur, Bahawalpur, Pakistan; Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur, Pakistan.

3. Jamal Abdul Nasir: Department of Statistics, GC University Lahore, Pakistan

4. Arooj Fatima: Institute of Business, Management & Administrative Studies, The Islamia University of Bahawalpur, Pakistan

5. Farrukh Jamal: Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

6. Muhammad Munawar Ahmed: Department of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

7. Muhammad Rizwan: Department of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

8. Sania Anam: Department of Computer Science, Govt Degree College for Women Ahmadpur East, Bahawalpur, Pakistan

9. Muhammad Zubair: Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur, Pakistan

Abstract:

The purpose of this learning is to detect the Corn Seed Fusarium Disease using Hybrid Feature Space and Conventional machine learning (ML) approaches. A novel machine learning approach is employed for the classification of a total of six types of corn seed are collected which contain Infected Fusarium (moniliforme, graminearum, gibberella, verticillioides, kernel) as well as healthy corn seed, based on a multi-feature dataset, which is the grouping of geometric, texture and histogram features extracted from digital images. For each corn seed image, a total of twenty-five multi-features have been developed on every area of interest (AOI), sizes (50 × 50), (100 × 100), (150 × 150), and (200 × 200). A total of seven optimized features were selected by using a machine learning-based algorithm named “Correlation-based Feature Selection”. For experimentation, “Random forest”, “BayesNet” and “LogitBoost” have been employed using an optimized multi-feature user-supplied dataset divided with 70% training and 30 % testing. A comparative analysis of three ML classifiers RF, BN, and LB have been used and a considerably very high classification ratio of 96.67 %, 97.22 %, and 97.78 % have been achieved respectively when the AOI size (200×200) have been deployed to the classifiers.

Page(s): 1-10

DOI: DOI not available

Published: Journal: Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, Volume: 58, Issue: 2, Year: 2021

Keywords:

machine learning , Corn Seed , Fusarium Disease , LogitBoost

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