Automated Corn Seed Fusarium Disease Classification System Using Hybrid Feature Space and Conventional Machine Learning Techniques

Corn Seed Fusarium Disease Classification

Authors

  • Samreen Naeem Department Food Science, College of Tourism & Hotel Management (COTHM), Bahawalpur.Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur, Pakistan.
  • Aqib Ali Department of Computer Science, Concordia College Bahawalpur, Bahawalpur, Pakistan. Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur, Pakistan.
  • Jamal Abdul Nasir Department of Statistics, GC University Lahore, Pakistan.
  • Arooj Fatima Institute of Business, Management & Administrative Studies, The Islamia University of Bahawalpur, Pakistan;
  • Farrukh Jamal Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
  • Muhammad Munawar Ahmed Department of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
  • Muhammad Rizwan Department of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
  • Sania Anam Department of Computer Science, Govt Degree College for Women Ahmadpur East, Bahawalpur, Pakistan.
  • Muhammad Zubair Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur, Pakistan

DOI:

https://doi.org/10.53560/PPASA(58-2)692

Keywords:

Fusarium Disease, Corn Seed, LogitBoost, Machine Learning

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.

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Published

2021-12-24

How to Cite

Naeem, S., Ali, A. ., Nasir, J. A. ., Fatima, A. ., Jamal, F. ., Ahmed, M. M. ., Rizwan, M. ., Anam, S. ., & Zubair, M. . (2021). Automated Corn Seed Fusarium Disease Classification System Using Hybrid Feature Space and Conventional Machine Learning Techniques: Corn Seed Fusarium Disease Classification. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 58(2), 1–10. https://doi.org/10.53560/PPASA(58-2)692

Issue

Vol. 58 No. 2 (2021)

Section

Articles