Classification of Wheat Rootstock and Their Hybrids According to Color Features by Machine Learning Algorithms

Year 2022, Volume: 10 Issue: 2, 39 - 48, 30.06.2022

Mesut Ersin Sönmez , Kadir Sabancı , Nevzat Aydın

https://doi.org/10.18100/ijamec.1098276

Cited By: 1

Abstract

Accurate classification of wheat varieties has a large economic market in the world is enabled both high income in the market and the development of new fertile hybrids for changing weather conditions due to global warming. In this study, instead of using the conventional classification method, we extracted color features of the 1400 durum wheat grain samples, consisting of Ahmetbugdayi, Cesare and their hybrids BC1F6 and BC2F5, by using image processing techniques. For the color features, every twelve channels of four different color spaces were used and square-shaped samples were taken from the center of all the grains in these channels of images. the averages of the channel pixels values were used as color features. Then six different machine learning algorithms were employed for the classification task. ANN, SVM and DT models achieved more than 0.99 accuracies. On the other hand, k-NN and RF model reached approximately 0.99 accuracies. According to our results, in addition to different wheat varieties, also sibling hybrid seeds can be classified with high accuracy according to their color characteristics by the methods we proposed.

Keywords

Wheat, Classification, Machine Learning Algorithms, Color Features

Thanks

This research was carried out within the scope of project number 02-D-19 supported by Karamanoglu Mehmetbey University Scientific Research Projects Coordinator.

References

• [1] FAO, “FAOSTAT,” 2020. https://www.fao.org/faostat/en/#data/QCL (accessed Dec. 05, 2021).
• [2] M. K. van Aalst, “The impacts of climate change on the risk of natural disasters,” Disasters, vol. 30, no. 1, pp. 5–18, Mar. 2006, doi: 10.1111/J.1467-9523.2006.00303.X/FORMAT/PDF.
• [3] N. Arunrat, S. Sereenonchai, W. Chaowiwat, and C. Wang, “Climate change impact on major crop yield and water footprint under CMIP6 climate projections in repeated drought and flood areas in Thailand,” Science of the Total Environment, vol. 807, Feb. 2022, doi: 10.1016/J.SCITOTENV.2021.150741.
• [4] S. Mehryar and S. Surminski, “National laws for enhancing flood resilience in the context of climate change: potential and shortcomings,” Climate Policy, vol. 21, no. 2, pp. 133–151, 2021, doi: 10.1080/14693062.2020.1808439/SUPPL_FILE/TCPO_A_1808439_SM1165.ZIP.
• [5] S. Zhang, S. Wang, L. Yuan, and X. Liu, “The impact of epidemics on agricultural production and forecast of COVID-19,” 2020, doi: 10.1108/CAER-04-2020-0055.
• [6] D. Bochtis, L. Benos, M. Lampridi, V. Marinoudi, S. Pearson, and C. G. Sørensen, “Agricultural Workforce Crisis in Light of the COVID-19 Pandemic,” Sustainability 2020, Vol. 12, Page 8212, vol. 12, no. 19, p. 8212, Oct. 2020, doi: 10.3390/SU12198212.
• [7] P. Bahadur Poudel et al., “COVID-19 and its Global Impact on Food and Agriculture,” J Biol Today’s World, vol. 9, no. 5, p. 221, 2020.
• [8] WHO, “The state of food security and nutrition in the world 2020: transforming food systems for affordable healthy diets,” 2020.
• [9] A. I. Olagunju, T. D. Oluwajuyitan, and S. I. Oyeleye, “Multigrain bread: dough rheology, quality characteristics, in vitro antioxidant and antidiabetic properties,” Journal of Food Measurement and Characterization, vol. 15, no. 2, pp. 1851–1864, 2021, doi: 10.1007/s11694-020-00670-3.
• [10] B. Svihus, Nutritive and Digestive Effects of Starch and Fiber in Whole Wheat. Elsevier, 2014. doi: 10.1016/B978-0-12-401716-0.00007-6.
• [11] P. R. Shewry and S. J. Hey, “The contribution of wheat to human diet and health,” Food and Energy Security, vol. 4, no. 3, pp. 178–202, 2015, doi: 10.1002/FES3.64.
• [12] FAO, “FAOSTAT,” 2020. https://www.fao.org/faostat/en/#data/QCL/visualize (accessed Dec. 29, 2021).
• [13] C. Miralbés, “Discrimination of European wheat varieties using near infrared reflectance spectroscopy,” Food Chemistry, vol. 106, no. 1, pp. 386–389, Jan. 2008, doi: 10.1016/J.FOODCHEM.2007.05.090.
• [14] R. Tkachuk and V. J. Metlish, “Wheat cultivar identification by high voltage gel electrophoresis.,” Annales de Technologie Agricole, vol. 29, no. 2, pp. 207–212, 1980.
• [15] K. G. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, “Machine Learning in Agriculture: A Review,” Sensors 2018, Vol. 18, Page 2674, vol. 18, no. 8, p. 2674, Aug. 2018, doi: 10.3390/S18082674.
• [16] K. Jha, A. Doshi, P. Patel, and M. Shah, “A comprehensive review on automation in agriculture using artificial intelligence,” Artificial Intelligence in Agriculture, vol. 2, pp. 1–12, Jun. 2019, doi: 10.1016/J.AIIA.2019.05.004.
• [17] D. I. Patrício and R. Rieder, “Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review,” Computers and Electronics in Agriculture, vol. 153, pp. 69–81, Oct. 2018, doi: 10.1016/J.COMPAG.2018.08.001.
• [18] R. V. Ronge and M. M. Sardeshmukh, “Comparative analysis of Indian wheat seed classification,” Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014, pp. 937–942, Nov. 2014, doi: 10.1109/ICACCI.2014.6968483.
• [19] K. Sabanci and M. Akkaya, “International Journal of Intelligent Systems and Applications in Engineering Classification of Different Wheat Varieties by Using Data Mining Algorithms,” IJISAE, vol. 4, no. 2, 2016, doi: 10.18201/ijisae.62843.
• [20] A. Kayabasi, “An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains,” International Journal of Intelligent Systems and Applications in Engineering, vol. 6, no. 1, pp. 85–91, Mar. 2018, doi: 10.18201/IJISAE.2018637936.
• [21] J. J. Martín-Gómez, A. Rewicz, K. Goriewa-Duba, M. Wiwart, Á. Tocino, and E. Cervantes, “Morphological Description and Classification of Wheat Kernels Based on Geometric Models,” Agronomy 2019, Vol. 9, Page 399, vol. 9, no. 7, p. 399, Jul. 2019, doi: 10.3390/AGRONOMY9070399.
• [22] K. Laabassi, M. A. Belarbi, S. Mahmoudi, S. A. Mahmoudi, and K. Ferhat, “Wheat varieties identification based on a deep learning approach,” Journal of the Saudi Society of Agricultural Sciences, vol. 20, no. 5, pp. 281–289, Jul. 2021, doi: 10.1016/J.JSSAS.2021.02.008.
• [23] T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, Jun. 2006, doi: 10.1016/J.PATREC.2005.10.010.
• [24] K. S. Narendra and K. Parthasarathy, “Neural Networks In Dynamical Systems,” in Intelligent Control and Adaptive Systems, 1990, vol. 1196, p. 230. doi: 10.1117/12.969922.
• [25] S. Haykin, J. Nie, and B. Currie, “Neural network-based receiver for wireless communications,” Electronics Letters, vol. 35, no. 3, pp. 203–205, 1999, doi: 10.1049/el:19990177.
• [26] S. R. Gunn, “Support Vector Machines for Classification and Regression,” 1998.
• [27] D. W. Aha, D. Kibler, M. K. Albert, and J. R. Quinian, “Instance-based learning algorithms,” Machine Learning 1991 6:1, vol. 6, no. 1, pp. 37–66, Jan. 1991, doi: 10.1007/BF00153759.
• [28] Y. Y. Song and Y. Lu, “Decision tree methods: applications for classification and prediction,” Shanghai Archives of Psychiatry, vol. 27, no. 2, p. 130, Apr. 2015, doi: 10.11919/J.ISSN.1002-0829.215044.
• [29] X. Mei et al., “A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat,” Remote Sensing 2019, Vol. 11, Page 920, vol. 11, no. 8, p. 920, Apr. 2019, doi: 10.3390/RS11080920.
• [30] L. Guo, N. Chehata, C. Mallet, and S. Boukir, “Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 1, pp. 56–66, Jan. 2011, doi: 10.1016/J.ISPRSJPRS.2010.08.007.
• [31] D. L. Naik and R. Kiran, “Naïve Bayes classifier, multivariate linear regression and experimental testing for classification and characterization of wheat straw based on mechanical properties,” Industrial Crops and Products, vol. 112, pp. 434–448, Feb. 2018, doi: 10.1016/J.INDCROP.2017.12.034.
• [32] D. Zhang et al., “Integration of spectroscopy and image for identifying fusarium damage in wheat kernels,” Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, vol. 236, p. 118344, 2020, doi: 10.1016/j.saa.2020.118344.
• [33] C. E. Metz, “Basic principles of ROC analysis,” Seminars in Nuclear Medicine, vol. 8, no. 4, pp. 283–298, Oct. 1978, doi: 10.1016/S0001-2998(78)80014-2.
• [34] K. Sabanci, A. Kayabasi, and A. Toktas, “Computer vision-based method for classification of wheat grains using artificial neural network,” Journal of the Science of Food and Agriculture, vol. 97, no. 8, pp. 2588–2593, Jun. 2017, doi: 10.1002/JSFA.8080.
• [35] Y. Cakir, M. Kirci, and E. O. Gunes, “Yield prediction of wheat in south-east region of Turkey by using artificial neural networks,” 2014 The 3rd International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2014, 2014, doi: 10.1109/Agro-Geoinformatics.2014.6910609.
• [36] L. Ravikanth, C. B. Singh, D. S. Jayas, and N. D. G. White, “Classification of contaminants from wheat using near-infrared hyperspectral imaging,” Biosystems Engineering, vol. 135, pp. 73–86, Jul. 2015, doi: 10.1016/J.BIOSYSTEMSENG.2015.04.007.