TY - JOUR
T1 - Machine learning approach for the classification of corn seed using hybrid features
AU - Ali, Aqib
AU - Qadri, Salman
AU - Mashwani, Wali Khan
AU - Brahim Belhaouari, Samir
AU - Naeem, Samreen
AU - Rafique, Sidra
AU - Jamal, Farrukh
AU - Chesneau, Christophe
AU - Anam, Sania
N1 - Publisher Copyright:
© 2020, © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Corn seeds
KW - classification
KW - correlation-based feature selection
KW - machine learning
KW - multilayer perceptron
UR - http://www.scopus.com/inward/record.url?scp=85087547221&partnerID=8YFLogxK
U2 - 10.1080/10942912.2020.1778724
DO - 10.1080/10942912.2020.1778724
M3 - Article
AN - SCOPUS:85087547221
SN - 1094-2912
VL - 23
SP - 1097
EP - 1111
JO - International Journal of Food Properties
JF - International Journal of Food Properties
IS - 1
ER -