Automatic Visual Inspection of Grains Quality In Agroindustry 4.0

Robson Aparecido Gomes de Macedo; Wilson Dias Marques; Peterson Adriano Belan; Sidnei Alves Arujo

doi:10.5585/iji.v6i3.339

Autores

Robson Aparecido Gomes de Macedo Universidade Nove de Julho https://orcid.org/0000-0003-3550-7293
Wilson Dias Marques Universidade Nove de Julho https://orcid.org/0000-0001-5616-2672
Peterson Adriano Belan Universidade Nove de Julho https://orcid.org/0000-0001-9529-1637
Sidnei Alves Arujo Universidade Nove de Julho https://orcid.org/0000-0003-3970-5801

DOI:

https://doi.org/10.5585/iji.v6i3.339

Palavras-chave:

agroindustry 4.0, processing, automatic visual inspection, grains,

Resumo

With the advent of Industry 4.0, the use of new technologies, robotization and advanced manufacturing has been extended to the agricultural sector, with the aim of increasing productivity, reducing environmental impacts, increasing profits and improving the quality of products, giving rise to the terms Precision Agriculture, Agribusiness 4.0, Agriculture 4.0 and Agroindustry 4.0. If on the one hand much is being said about the adoption of new technologies in the stages of land preparation, planting and harvesting, on the other hand very little is said about the processing of agricultural products using, for example, automated systems for visual inspection of quality. This work aims to investigate the different approaches for automatic visual inspection of grains quality proposed in the last decade and present a discussion about how these approaches are inserted in the context of these new productive processes of modern agriculture, as well as the positive aspects and the limitations found for their uses.

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Biografia do Autor

Robson Aparecido Gomes de Macedo, Universidade Nove de Julho

PPGI

Referências

Aggarwal, A. K., & Mohan, R. (2010). Aspect ratio analysis using image processing for rice grain quality. International Journal of Food Engineering, 6(5). https://doi.org/10.2202/1556-3758.1788

Anami, B. S., & Savakar, D. G. (2010). Influence of light, distance and size on recognition and classification of food grains’ images. International Journal of Food Engineering, 6(2). https://doi.org/10.2202/1556-3758.1698

Araújo, S. A. De, Pessota, J. H., & Kim, H. Y. (2015). Beans quality inspection using correlation-based granulometry. Engineering Applications of Artificial Intelligence, 40, 84–94. https://doi.org/10.1016/j.engappai.2015.01.004

Araújo, S. A., Alves, W. A. L., Belan, P. A., & Anselmo, K. P. (2015). A Computer Vision System for Automatic Classification of Most Consumed Brazilian Beans. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9475, 45–53. https://doi.org/10.1007/978-3-319-27863-6

Belan, P. A., Araújo, S. A., & Alves, W. A. L. (2016). Image Analysis and Recognition. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9730, 801–809. https://doi.org/10.1007/978-3-319-41501-7

Belan, P. A., Araújo, S. A., & Santana, J. C. C. (2015). Um Sistema De Análise De Imagens Para Classificação Automática De Grãos De Feijão Brasileiro. CILAMCE - Ibero-Latin American Congress on Computational Methods in Engineering, 1–7.

Bernardi, A. C. D. C., Fragalle, E. P., Inamasu, R. Y., Sudeste, E. P., & Carlos, S. (2011). Inovação tecnológica em Agricultura de Precisão. Agricultura de Precisão - Um Novo Olhar, 297–302.

Bernardi, A. C. de C., & Inamasu, R. Y. (2014). Tendências Da Agricultura De Precisão No Brasil. Agricultura de Precisão: Resultados de Um Novo Olhar, (1), 559–577.

Bhat, S., Panat, S., & Arunachalam, N. (2017).

Classification of rice grain varieties arranged in scattered and heap fashion using image processing. In Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016) (Vol. 10341). https://doi.org/10.1117/12.2268802

Brito, C., Cirani, S., Azanha, M., & Dias De Moraes, F. (2010). Inovação na Indústria Sucroalcooleira Paulista: Os Determinantes da Adoção das Tecnologias de Agricultura de Precisão, 48(4), 543–565.

Laurent, B., Ousman, B., Dzudie, T., Carl, M. F. M., & Emmanuel, T. (2010). Digital camera images processing of hard-to-cook beans. Jornal of Engineering and Technology Research, 2(9), 177–188.

Ouyang, A., Gao, R., Liu, Y., & Dong, X. (2010). An Automatic Method for Identifying Different Variety of Rice Seeds Using Machine Vision Technology. Science And Technology, (Icnc), 84–88. Retrieved from http://ieeexplore.ieee.org.ez1.periodicos.capes.gov.br/stamp/stamp.jsp?tp=&arnumber=5583370

Parronchi, P. (2017). Os Pioneiros do desenvolvimento e a Nova Agricultura 4.0: desenvolvimento econômico a partir do campo? The Development Pioneers and the New Agriculture 4.0: economic development from the countryside? Universidade Federal Do ABC.

Potter, P., Valiente, J. M., & Andreu-García, G. (2015). Automatic Visual Inspection of Corn Kernels Using Principal Component Analysis, (February 2015).

Ramos, P. J., Prieto, F. A., Montoya, E. C., & Oliveros, C. E. (2017). Automatic fruit count on coffee branches using computer vision. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2017.03.010

Schwab, K. (2015). Navigating the fourth industrial revolution. Nature Nanotechnology, 10(12), 1005–1006. https://doi.org/10.1038/nnano.2015.286

Siddagangappa, M. R., & Kulkarni, A. H. (2014). Classification and Quality Analysis of Food Grains. IOSR Journal of Computer Engineering (IOSR-JCE), 16(4), 01-10.

Simões, Margareth;Soler, Lucianas.;Py, H. (2017). TECNOLOGIAS A SERVIÇO DA SUSTENTABILIDADE E DA AGRICULTURA. Boletim Informativo, 50–53.

Swati, & Chanana, R. (2014). Grain Counting Method Based on Machine Vision. International Journal of Advanced Technology in Engineering and Science, 02(08), 328–332.

Venora, G., Grillo, O., Ravalli, C., & Cremonini, R. (2009). Identification of Italian landraces of bean (Phaseolus vulgaris L.) using an image analysis system. Scientia Horticulturae, 121(4), 410–418.

https://doi.org/10.1016/j.scienta.2009.03.014

Zareiforoush, H., Minaei, S., Alizadeh, M. R., & Banakar, A. (2016). Qualitative classification of milled rice grains using computer vision and metaheuristic techniques. Journal of Food Science and Technology, 53(1), 118–131. https://doi.org/10.1007/s13197-015-1947-4