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

Figures & data

Table 1. Time and sun light intensity when digital images of six corn seed varieties acquired.

Sr. No.	Corn Seeds Varieties	Sun Light Intensity	Time
1	Sygenta ST-6142	385 Lux	1:00 PM
2	Pioneer P-1429	384 Lux	1:20 PM
3	Kashmiri Makkai	377 Lux	1:45 PM
4	Neelam Makkai	369 Lux	2:15 PM
5	Desi Makkai	365 Lux	2:35 PM
6	ICI 339	340 Lux	3:00 PM

Figure 1. Six corn seed varieties image dataset.

Figure 2. Color to gray level conversion of six corn seed varieties image dataset.

Figure 3. Four non- overlapping ROIs on gray level corn seed varieties image dataset.

Figure 4. Proposed framework for corn seed varieties classification using hybrid-feature.

Table 2. Selected features of correlation-based feature selection (CFS) technique.

Sr. No	Hybrid-Features
1	Histo_Mean
2	Histo_Skew
3	Inertia Average
4	Correlation Average
5	Inverse Diff Average
6	Inverse Diff Range
7	Spectral_DC
8	Ring1
9	Sector2

Table 3. Implemented MLP classifier constraints values.

Parameter	Value
Input Layers	15
Hidden Layers	1
Neurons	15
Learning Rate	0.4
Momentum	0.3
Validation Threshold	20
Epochs	500

Figure 5. Hybrid-feature MLP framework for corn seed varieties classification.

Table 4. Implemented ML classifiers on ROIs size (125 × 125) of corn seed dataset.

Classifier	KS	TP	FP	ROC	MAE	RMSE	Time (sec)	Accuracy (%)
MLP	0.8933	0.947	0.053	0.975	0.053	0.2275	0.39	94.67%
LB	0.9333	0.944	0.011	0.1176	0.0162	0.1176	3.81	94.44%
RF	0.8867	0.943	0.057	0.953	0.0777	0.2294	0.46	94.33%
BN	0.9133	0.928	0.014	0.960	0.0253	0.1549	0.19	92.78%

Table 5. CM showing corn seed classification ROIs size (125 × 125) using MLP.

Classes	Desi Makkai	Syngenta ST-6142	Kasmiri Makkai	Pioneer P-1429	Neelam Makkai	ICI 339	Total	Accuracy %
Desi Makkai	990	0	2	0	8	0	1000	99%
Syngenta ST-6142	6	954	10	0	30	0	1000	95.4%
Kasmiri Makkai	2	10	935	12	0	41	1000	93.5%
Pioneer P-1429	0	0	1	979	0	20	1000	97.9%
Neelam Makkai	53	0	6	1	931	9	1000	93.1%
ICI 339	19	10	41	0	39	891	1000	89.1%

Table 6. Implemented ML classifiers on ROIs size (150 × 150) of corn seed dataset.

Classifier	KS	TP	FP	ROC	MAE	RMSE	Time (sec)	Accuracy (%)
MLP	0.9767	0.988	0.012	1	0.0191	0.0914	1.38	98.83%
LB	0.9733	0.978	0.004	0.999	0.0284	0.0861	0.99	97.78%
RF	0.9667	0.972	0.006	1.000	0.0097	0.0854	0.58	97.22%
BN	0.96	0.967	0.007	0.999	0.0476	0.1179	0.59	96.67%

Figure 6. MLP classification results of six corn seed verities ROI size (125 × 125).

Table 7. CM showing corn seed classification ROIs size (150 × 150) using MLP.

Classes	Desi Makkai	Syngenta ST-6142	Kasmiri Makkai	Pioneer P-1429	Neelam Makkai	ICI 339	Total	Accuracy %
Desi Makkai	998	0	2	0	0	0	1000	99.8%
Syngenta ST-6142	15	970	5	10	0	0	1000	97%
Kasmiri Makkai	2	0	985	2	0	11	1000	98.5%
Pioneer P-1429	0	0	12	986	0	2	1000	98.6%
Neelam Makkai	1	0	0	0	999	0	1000	99.9%
ICI 339	0	0	6	0	0	994	1000	99.4%

Figure 7. MLP classification results of six corn seed verities ROI size (150 × 150).

Figure 8. Comparative analysis of the classification of six corn seed verities on ROIs (125 × 125) and (150 × 150) by using MLP classifier.

Table 8. Comparison of our proposed approach with existing approaches.

Reference	Features	Classifiers	Accuracy
^[Citation13]	Texture Features	ANN	95%
^[Citation14]	Shape + Morphological Features	MLP	96.15%
^[Citation15]	Histogram Features	ANN	92%
^[Citation16]	Morphological Features	NFN	96.73%
^[Citation17]	Statistical Features	ANN	85.72%
^[Citation18]	Geometry + Color Features	ANN	95%
^[Citation19]	Morphological Features	ANN	94%
^[Citation20]	Statistical + Morphological Features	SVM	96%
^[Citation21]	Texture + Histogram Features	BPNN	90%
^[Citation22]	Texture features	SVM	93.05%
^[Citation23]	Wavelength + Discriminant Aanalysis	SVM	98.78%
^[Citation24]	Shape and Color Features	DL	96.67%
^[Citation25]	Remote Sensing	RF	97.65%
^[Citation26]	Hyperspectral Imaging	M2 M	90%
Proposed Methodology	TRGS + Hybrid Features	MLP	98.83%

Pandey, R.; Naik, S.; Marfatia, R. Image Processing and Machine Learning for Automated Fruit Grading System: A Technical Review. Int. J. Comput. Appl. 2013, 81(16), 29–39. DOI: 10.5120/14209-2455.

 Google Scholar

Pourreza, A.; Pourreza, H.; Abbaspour-Fard, M. H.; Sadrnia, H. Identification of Nine Iranian Wheat Seed Varieties by Textural Analysis with Image Processing. Comput. Electron. Agric. 2012, 83, 102–108. DOI: 10.1016/j.compag.2012.02.005.

 Web of Science ®Google Scholar

Silva, C. S.; Sonnadara, U. Classification of rice grains using neural networks; 2013.

 Google Scholar

Pazoki, A. R.; Farokhi, F.; Pazoki, Z. Classification of Rice Grain Varieties Using Two Artificial Neural Networks (MLP and Neuro-Fuzzy). J. Anim. Plant Sci. 2014, 24(1), 336–343.

 Web of Science ®Google Scholar

Khoshroo, A. L. I. R. E. Z. A.; Arefi, A. R. M. A. N.; Masoumiasl, A. S. A. D.; Jowkar, G. H. Classification of Wheat Cultivars Using Image Processing and Artificial Neural Networks. Agric. Commun. 2014, 2(1), 17–22.

 Google Scholar

Shahid, M.; Naweed, M.; Qadri, S. Mutiullah, and EA Rehmani,“Varietal Discrimination of Wheat Seeds by Machine Vision Approach,”. Life Sci. J. 2014, 11(6), 245–252.

 Google Scholar

Dubey, B.; Bhagwat, S.; Shouche, S.; Sainis, J. Potential of Artificial Neural Networks in Varietal Identification Using Morphometry of Wheat Grains. Biosyst. Eng. 2006, 95(1), 61–67. DOI: 10.1016/j.biosystemseng.2006.06.001.

 Web of Science ®Google Scholar

Guevara-Hernandez, F.; Gomez-Gil, J. A Machine Vision System for Classification of Wheat and Barley Grain Kernels. Spanish J. Agric. Res. 2011, 9(3), 9. DOI: 10.5424/sjar/20110903-140-10.

 Web of Science ®Google Scholar

Chen, X.; Xun, Y.; Li, W.; Zhang, J. Combining Discriminant Analysis and Neural Networks for Corn Variety Identification. Comput. Electron. Agric. 2010, 71, S48–S53. DOI: 10.1016/j.compag.2009.09.003.

 Web of Science ®Google Scholar

Daskalov, P.; Kirilova, E.; Georgieva, T. Performance of an Automatic Inspection System for Classification of Fusarium Moniliforme Damaged Corn Seeds by Image Analysis. MATEC Web of Conferences. 210, 02014. EDP Sciences, 2018.

 Google Scholar

Qiu, G.; Lü, E.; Wang, N.; Lu, H.; Wang, F.; Zeng, F. Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis. Appl. Sci. 2019, 9(8), 1530. DOI: 10.3390/app9081530.

 Google Scholar

Li, X.; Dai, B.; Sun, H.; Li, W. Corn Classification System Based on Computer Vision. Symmetry. 2019, 11(4), 591. DOI: 10.3390/sym11040591.

 Google Scholar

Zhang, L.; Liu, Z.; Ren, T.; Liu, D.; Ma, Z.; Tong, L.; Zhang, C.; Zhou, T.; Zhang, X.; Li, S. Identification of Seed Maize Fields with High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier. Remote Sens.. 2020, 12(3), 362. DOI: 10.3390/rs12030362.

 Web of Science ®Google Scholar

Zhang, J.; Dai, L.; Cheng, F. Classification of Frozen Corn Seeds Using Hyperspectral VIS/NIR Reflectance Imaging. Molecules. 2019, 24(1), 149. DOI: 10.3390/molecules24010149.

 Web of Science ®Google Scholar