Inspection and classification of wheat quality using image processing

Main Article Content

Junsong Zhu
Jianrong Cai
Baosheng Sun
Yongjian Xu
Feng Lu
Haile Ma


BP neural network, image processing, inspect, single granulation guide groove, SVM, wheat


Wheat plays an important role in our daily life and industrial production. Several computer vision approaches have been proposed for classifying wheat quality, but there were some methods focusing on the problem of cohesive wheats while image processing. In this paper, we designed a single kernel guide groove to separate the cohesive wheats, which could simplify the algorithm of image processing and improve the accuracy rate of classification. For the method followed while recording the data, the image information must be converted into digital information, and the results are provided using appropriate image processing algorithms. Image preprocessing steps such as binarization, image enhancement, image segmentation, and morphological processing were used to reduce noise. For image segmentation, we proposed the following new segmentation methods: (1) extracting wheat region by converting image to H channel and (2) watershed algorithm based on Euclidean distance transformation. For the classification model, 22 features of 7 different qualities of wheat were inputted in the Back Propagation (BP) neural network and Support Vector Machine (SVM) model, and the overall correct classification rates were determined to be 91% and 97% for SVM and BP neural network, respectively. The BP neural network was more suitable for wheat appearance quality detection.


Download data is not yet available.
Abstract 176 | PDF Downloads 275 HTML Downloads 13 XML Downloads 4


Chen, S., Xiong, J., Guo, W., Bu, R., Zheng, Z., Chen, Y., Yang, Z., and Lin, R. 2019. Colored rice quality inspection system using machine vision. Journal of Cereal Science 88: 87–95. 10.1016/j.jcs.2019.05.010

Davies, E.R. 2009. The application of machine vision to food and agriculture: a review. Imaging Science Journal 57(4): 197–217. 10.1179/174313109X454756

Dhua, S., Kumar, K., Kumar, Y., Singh, L., and Sharanagat, V.S. 2021. Composition, characteristics and health promising prospects of black wheat: A review. Trends in Food Science and Technology 112: 780–794. 10.1016/j.tifs.2021.04.037

dos Santos, C.M., Escobedo, J.F., Teramoto, É.T., and da Silva, S.H.M.G. 2016. Assessment of ANN and SVM models for estimating-normal direct irradiation (Hb). Energy Conversion and Management 126: 826–836. 10.1016/j.enconman.2016.08.020

Femenias, A., Gatius, F., Ramos, A. J., Sanchis, V., and Marín, S. 2021. Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples. Food Chemistry 341: 128206. 10.1016/j.foodchem.2020.128206

Hamdi, A., Chan, Y.K., and Koo, V.C. 2021. A New Image Enhancement and Super Resolution technique for license plate recognition. Heliyon 7(11): e08341. 10.1016/j.heliyon.2021.e08341

Islabudeen, M., Vigneshwaran, P., Sindhu Madhuri, G., Muthu Kumar, B., Ragaventhiran, J., and Sharmila, G. 2021. Feature extraction of underwater images using principle component analysis with image registration. Materials Today: Proceedings. 10.1016/j.matpr.2021.03.341

Javanmardi, S., Miraei Ashtiani, S.H., Verbeek, F.J., and Martynenko, A. 2021. Computer-vision classification of corn seed varieties using deep convolutional neural network. Journal of Stored Products Research 92: 101800. 10.1016/j.jspr.2021.101800

Kaya, E., and Saritas, İ. 2019. Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features. Computers and Electronics in Agriculture 166: 105016. 10.1016/j.compag.2019.105016

Kiani, S., Minaei, S., and Ghasemi-Varnamkhasti, M. 2016. Fusion of artificial senses as a robust approach to food quality-assessment. Journal of Food Engineering 171: 230–239. 10.1016/j.jfoodeng.2015.10.007

Koklu, M., and Ozkan, I.A. 2020. Multiclass classification of dry beans using computer vision and machine learning techniques. Computers and Electronics in Agriculture 174: 105507. 10.1016/j.compag.2020.105507

Lei, B., and Fan, J. 2019. Image thresholding segmentation method based on minimum square rough entropy. Applied Soft Computing 84: 105687. 10.1016/j.asoc.2019.105687

Li, C., Zhang, X., Huang, Y., Tang, C., and Fatikow, S. 2020. A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision. Computers & Industrial Engineering 146: 106530. 10.1016/j.cie.2020.106530

Li, L., Chen, S., Deng, M., and Gao, Z. 2022. Optical techniques in non-destructive detection of wheat quality: A review. Grain & Oil Science and Technology 5(1): 44–57. 10.1016/j.gaost.2021.12.001

Li, Y., Otsubo, M., Kuwano, R., and Nadimi, S. 2021. Quantitative evaluation of surface roughness for granular materials using Gaussian filter method. Powder Technology 388: 251–260. 10.1016/j.powtec.2021.04.068

Lin, X., Xu, J.L., and Sun, D.W. 2020. Evaluating drying feature differences between ginger slices and splits during microwave-vacuum drying by hyperspectral imaging technique. Food Chemistry 332: 127407. 10.1016/j.foodchem.2020.127407

Liu, R., Li, Y., Wang, H., and Liu, J. 2021. A noisy multi--objective optimization algorithm based on mean and Wiener filters. Knowledge-Based Systems 228: 107215. 10.1016/j.knosys.2021.107215

Liu, Z., Zhang, R., Yang, C., Hu, B., Luo, X., Li, Y., and Dong, C. 2022. Research on moisture content detection method during green tea processing based on machine vision and near--infrared spectroscopy technology. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 271: 120921. 10.1016/j.saa.2022.120921

Ma, J., Li, Y., Du, K., Zheng, F., Zhang, L., Gong, Z., and Jiao, W. 2020. Segmenting ears of winter wheat at flowering stage using digital images and deep learning. Computers and Electronics in Agriculture 168: 105159. 10.1016/j.compag.2019.105159

Mahanti, N.K., Pandiselvam, R., Kothakota, A., Ishwarya, S.P., Chakraborty, S.K., Kumar, M., and Cozzolino, D. 2022. Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis. Trends in Food Science & Technology 120: 418–438. 10.1016/j.tifs.2021.12.021

Majumdar, S., and Jayas, D.S. 1999. Classification of Bulk Samples of Cereal Grains using Machine Vision. Journal of Agricultural Engineering Research 73(1): 35–47. 10.1006/jaer.1998.0388

Mohd Ali, M., Hashim, N., Aziz, S.A., and Lasekan, O. 2020. Emerging non-destructive thermal imaging technique coupled with chemometrics on quality and safety inspection in food and agriculture. Trends in Food Science & Technology 105: 176–185. 10.1016/j.tifs.2020.09.003

Ni, C., Wang, D., Vinson, R., Holmes, M., and Tao, Y. 2019. Automatic inspection machine for maize kernels based on deep convolutional neural networks. Biosystems Engineering 178: 131–144. 10.1016/j.biosystemseng.2018.11.010

Oliveira, M.M., Cerqueira, B.V., Barbon, S., and Barbin, D.F. 2021. Classification of fermented cocoa beans (cut test) using computer vision. Journal of Food Composition and Analysis 97: 103771. 10.1016/j.jfca.2020.103771

Pandiselvam, R., Mayookha, V.P., Kothakota, A., Ramesh, S.V., Thirumdas, R., and Juvvi, P. 2020. Biospeckle laser technique-A novel non-destructive approach for food quality and safety detection. Trends in Food Science & Technology 97: 1–13. 10.1016/j.tifs.2019.12.028

Qian, K., Bao, Y., Zhu, J., Wang, J., and Wei, Z. (2021). Development of a portable electronic nose based on a hybrid filter-wrapper method for identifying the Chinese dry-cured ham of different grades. Journal of Food Engineering 290: 110250. 10.1016/j.jfoodeng.2020.110250

Ravikanth, L., Singh, C.B., Jayas, D.S., and White, N.D.G. 2015. Classification of contaminants from wheat using near-infrared hyperspectral imaging. Biosystems Engineering 135: 73–86. 10.1016/j.biosystemseng.2015.04.007

Septiarini, A., Sunyoto, A., Hamdani, H., Kasim, A.A., Utaminingrum, F., and Hatta, H.R. 2021. Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features. Scientia Horticulturae 286: 110245. 10.1016/j.scienta.2021.110245

Shen, Y., Yin, Y., Li, B., Zhao, C., and Li, G. 2021. Detection of impurities in wheat using terahertz spectral imaging and convolutional neural networks. Computers and Electronics in Agriculture 181: 105931. 10.1016/j.compag.2020.105931

Singh, P., Bhandari, A.K., and Kumar, R. 2022. Naturalness-balance contrast enhancement using adaptive gamma with cumulative histogram and median filtering. Optik 251: 168251. 10.1016/j.ijleo.2021.168251

Srinivasa R.K., and Jaya, T. 2021. De-noising and enhancement of MRI medical images using Gaussian filter and histogram equalization. Materials Today: Proceedings. 10.1016/j.matpr.2021.03.144

Suresh K.P., Behera, H.S., Kumari, A.K., Nayak, J., and Naik, B. 2020. Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades. Computer Science Review 38: 100288. 10.1016/j.cosrev.2020.100288

Tetila, E.C., Machado, B.B., Astolfi, G., Belete, N.A.d.S., Amorim, W.P., Roel, A.R., and Pistori, H. 2020. Detection and classification of soybean pests using deep learning with UAV images. Computers and Electronics in Agriculture 179: 105836. 10.1016/j.compag.2020.105836

Vithu, P., and Moses, J.A. 2016. Machine vision system for food grain quality evaluation: A review. Trends in Food Science and Technology 56: 13–20. 10.1016/j.tifs.2016.07.011

Wang, A., Zhang, W., and Wei, X. 2019. A review on weed detection using ground-based machine vision and image processing techniques. Computers and Electronics in Agriculture 158: 226–240. 10.1016/j.compag.2019.02.005

Yugander, P., Tejaswini, C.H., Meenakshi, J., Kumar, K.S., Varma, B.V.N.S., and Jagannath, M. 2020. MR Image Enhancement using Adaptive Weighted Mean Filtering and Homomorphic Filtering. Procedia Computer Science 167: 677–685. 10.1016/j.procs.2020.03.334

Zhang, H., Tang, Z., Xie, Y., Gao, X., and Chen, Q. 2019. A watershed segmentation algorithm based on an optimal marker for bubble size measurement. Measurement 138: 182–193. 10.1016/j.measurement.2019.02.005