Machine learning approach for the classification of corn seed using hybrid features

Aqib Ali, Salman Qadri, Wali Khan Mashwani, Samir Brahim Belhaouari*, Samreen Naeem, Sidra Rafique, Farrukh Jamal, Christophe Chesneau, Sania Anam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

60 Citations (Scopus)

Abstract

Seed purity is an important indicator of crop seed quality. On the other side, corn is an important crop of the modern agricultural industry with more than 40% grain Worldwide production. The purpose of this study was to examine the feasibility of a machine learning (ML) approach for classifying different types of corn seeds. The seed digital images (DI) of six corn varieties were Desi Makkai, Sygenta ST-6142, Kashmiri Makkai, Pioneer P-1429, Neelam Makkai, and ICI 339. This was achieved through a digital camera in a natural environment without a complicated laboratory system. The acquired DI dataset converted to a hybrid feature dataset, which is the combination of histogram, texture, and spectral features. For each corn seed image, a total of fifty-five hybrid-features was acquired on every non-overlapping region of interest (ROI), sizes (75 × 75), (100 × 100), (125 × 125) and (150 × 150). The nine optimized features have been acquired by employing the correlation-based feature selection (CFS) technique with the Best First search algorithm. To build the classification models, Random forest (RF), BayesNet (BN), LogitBoost (LB), and Multilayer Perceptron (MLP) were employed using optimized multi-feature using (10-fold) cross-validation approach. A comparative analysis of four ML classifiers, the MLP performed outstanding classification accuracy (98.93%), on ROIs size (150 × 150). The accuracy values by MLP on six corn seed verities named Desi Makkai, Sygenta ST-6142, Kashmiri Makkai, Pioneer P-1429, Neelam Makkai, ICI- 339 was 99.8%, 97%, 98.5%, 98.6%, 99.9%, and 99.4%, respectively.

Original languageEnglish
Pages (from-to)1097-1111
Number of pages15
JournalInternational Journal of Food Properties
Volume23
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020
Externally publishedYes

Keywords

  • Corn seeds
  • classification
  • correlation-based feature selection
  • machine learning
  • multilayer perceptron

Fingerprint

Dive into the research topics of 'Machine learning approach for the classification of corn seed using hybrid features'. Together they form a unique fingerprint.

Cite this