Mahmoudi, Said ; Université de Mons > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Mahmoudi, Sidi ; Université de Mons > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Ferhat, Kaci
Language :
English
Title :
Wheat varieties identification based on a deep learning approach
Publication date :
01 March 2021
Journal title :
Journal of the Saudi Society of Agricultural Sciences
ISSN :
1658-077X
Publisher :
Elsevier
Volume :
20
Issue :
5
Pages :
281-289
Peer reviewed :
Peer Reviewed verified by ORBi
Research unit :
F114 - Informatique, Logiciel et Intelligence artificielle
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K., A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8, 2019, 292, 10.3390/electronics8030292.
Brahimi, M., Boukhalfa, K., Moussaoui, A., Deep Learning for Tomato Diseases: Classification and Symptoms Visualization. Appl. Artif. Intell. 31 (2017), 299–315, 10.1080/08839514.2017.1315516.
Delogu, Chiara, Validation of a new method: use of SDS-PAGE technique for the verification of Triticum and ×Triticosecale. ISTA News Bull., 147, 2013, 32.
Choudhary, R., Mahesh, S., Paliwal, J., Jayas, D.S., Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosyst. Eng. 102 (2009), 115–127, 10.1016/j.biosystemseng.2008.09.028.
Copeland, L.O., McDonald, M.B., 2001. Principles of Seed Science and Technology, Principles of seed science and technology. Springer US, Boston, MA. https://doi.org/10.1007/978-1-4615-1619-4.
Davies, E.R., 2012. Computer and Machine Vision, Computer and Machine Vision, Texts in Computer Science. Elsevier, London. https://doi.org/10.1016/C2010-0-66926-4.
Davies, E.R., The application of machine vision to food and agriculture: a review. Imaging Sci. J. 57 (2009), 197–217, 10.1179/174313109x454756.
Dolata, P., Reiner, J. 2018. Barley Variety Recognition with Viewpoint-aware Double-stream Convolutional Neural Networks. Proceedings of the 2018 Federated Conference on Computer Science and Information Systems. ACSIS, Vol. 15, pp. 101–105.
Du, C.J., Sun, D.W., Learning techniques used in computer vision for food quality evaluation: A review. J. Food Eng. 72 (2006), 39–55, 10.1016/j.jfoodeng.2004.11.017.
Ebrahimi, E., Mollazade, K., Babaei, S., Toward an automatic wheat purity measuring device: A machine vision-based neural networks-assisted imperialist competitive algorithm approach. Measurement 55 (2014), 196–205, 10.1016/j.measurement.2014.05.003.
Elias, S.G., Copeland, L.O., McDonald, M.B., Baalbaki, R.Z., Seed Testing: Principles and Practices. 2012, Michigan State University Press.
Fahimirad, S., Ghorbanpour, M., 2019. Omics Approaches in Developing Abiotic Stress Tolerance in Rice (Oryza sativa L.), in: Advances in Rice Research for Abiotic Stress Tolerance. Elsevier, pp. 767–779. https://doi.org/10.1016/B978-0-12-814332-2.00038-1.
Ferentinos, K.P., Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145 (2018), 311–318, 10.1016/j.compag.2018.01.009.
Howitt, C.A., Diane, M., Identification of Grain Variety and Quality Type. Wrigley, C., Ian, B., Diane, M., (eds.) Cereal Grains: Assessing and Managing Quality, 2017, Second Edition. Elsevier, 55–87, 10.1016/B978-0-08-100719-8.00004-8.
Kamilaris, A., Prenafeta-Boldú, F.X., 2018. Deep learning in agriculture: A survey. Comput. Electron. Agric. https://doi.org/10.1016/j.compag.2018.02.016.
Kaya, A., Keceli, A.S., Catal, C., Yalic, H.Y., Temucin, H., Tekinerdogan, B., Analysis of transfer learning for deep neural network based plant classification models. Comput. Electron. Agric. 158 (2019), 20–29, 10.1016/j.compag.2019.01.041.
Khoshroo, A., Arefi, A., 2014. Classification of Wheat Cultivars Using Image Processing and Artificial Neural Networks. 2, 17–22.
Kozłowski, M., Górecki, P., Szczypiński, P.M., Varietal classification of barley by convolutional neural networks. Biosyst. Eng. 184 (2019), 155–165, 10.1016/j.biosystemseng.2019.06.012.
Makridakis, S., Spiliotis, E., Assimakopoulos, V., Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLoS One 13 (2018), 1–26, 10.1371/journal.pone.0194889.
Meyer, D.J.L., Wiersema, J.H., 2016. AOSA Rules for Testing Seeds: Principles and procedures, AOSA Rules for Testing Seeds. Association of Official Seed Analysts.
OCDE, 2019. Oecd Seed Schemes for the Varietal Certification or the Control of Seed Moving in International Trade [WWW Document]. URL https://www.oecd.org/agriculture/seeds/documents/oecd-seed-schemes-rules-and-regulations.pdf.
Patrício, D.I., Rieder, R., Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Comput. Electron. Agric. 153 (2018), 69–81 https://doi.org/https://doi.org/10.1016/j.compag.2018.08.001.
Powell, A.A., What is seed quality and how to measure it. PROC. 2nd World Seed Conf. Responding to challenges a Chang. world role new plant Var. high Qual. seed Agric. FAO Headquarters, Rome, 2009, 142–149.
Pridmore, T., Imaging methods for phenotyping of plant traits. Kumar, J., Pratap, A., Kumar, S., (eds.) Phenomics in Crop Plants: Trends, Options and Limitations, 2015, Springer India, New Delhi, 61–74, 10.1007/978-81-322-2226-2_5.
Shen, Y., Zhou, H., Li, J., Jian, F., Jayas, D.S., Detection of stored-grain insects using deep learning. Comput. Electron. Agric. 145 (2018), 319–325, 10.1016/j.compag.2017.11.039.
Szczypiński, P.M., Klepaczko, A., Zapotoczny, P., Identifying barley varieties by computer vision. Comput. Electron. Agric. 110 (2015), 1–8, 10.1016/j.compag.2014.09.016.
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C., A Survey on Deep Transfer Learning. 2018, Springer Verlag, 270–279.
Thenmozhi, K., Srinivasulu Reddy, U., Crop pest classification based on deep convolutional neural network and transfer learning. Comput. Electron. Agric., 164, 2019, 104906, 10.1016/j.compag.2019.104906.
Too, E.C., Yujian, L., Njuki, S., Yingchun, L., A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161 (2019), 272–279, 10.1016/j.compag.2018.03.032.
Tripodi, P., Massa, D., Venezia, A., Cardi, T., Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges. Agronomy, 8, 2018, 57, 10.3390/agronomy8040057.
Ubbens, J.R., Stavness, I., Corrigendum: Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks. Front. Plant Sci., 8, 2017, 1190, 10.3389/fpls.2017.02245.
Vithu, P., Moses, J.A., Machine vision system for food grain quality evaluation: A review. Trends Food Sci. Technol. 56 (2016), 13–20, 10.1016/j.tifs.2016.07.011.
Wrigley, C., 2017a. Assessing and Managing Quality at all Stages of the Grain Chain, in: Cereal Grains. Elsevier, pp. 3–25. https://doi.org/10.1016/B978-0-08-100719-8.00001-2.
Wrigley, C., 2017b. Cereal-Grain Morphology and Composition, in: Cereal Grains. Elsevier, pp. 55–87. https://doi.org/10.1016/B978-0-08-100719-8.00004-8.