An Intelligent System to Enhance the Productivity and Sustainability in Soybean Crop Enterprises
Authors: 
Douglas F. Pereira, Pedro H. Bugatti, 
Fabrício M. Lopes, André L. S. M. de Souza, Priscila T. M. SaitoAuthors Info & Claims
Article No.: 56, Pages 1 - 8
Abstract
This work presents an approach for the automatic processing and classification of the vigor of soybean seeds. The validation was performed through experimental evaluation using real datasets. By automating the seed analysis, it is possible a faster and more precise control, increasing the market competitiveness between producers and benefiting consumers with higher quality products and more appropriate and specific prices, according to the quality of the seeds. In addition, consequently, improvements related to sustainability and productivity can be obtained in the processes of planting, multiplication and/or commercialization of soybeans with better quality.
References
[1]
R. A. Batista, D. D. S. Bagatini, and R. Frozza. 2018. Classificação Automática de Códigos NCM Utilizando o Algoritmo Naive Bayes. iSys - Revista Brasileira de Sistemas de Informação 11, 2 (2018), 4--29.
[2]
C. D. R. Bittencourt, M. Ladeira, S. F. Silva, A. L. S. Bittencourt, and D. L. Borges. 2008. Sistema de Classificação Automática de Carcaças Bovinas. In Simpósio Brasileiro de Sistemas de Informação. SBC, 258--267.
[3]
Márcio de Campos Martins de Freitas. 2011. A cultura da soja no Brasil: O crescimento da produção brasileira e o surgimento de uma nova fronteira agrícola. Enciclopédia Biosfera 7, 12 (2011).
[4]
R.D.V. Epiphanio, A.R. Formaggio, B.F.T. Rudorff, E.E. Maeda, and A.J.B. Luiz. 2010. Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil. Pesquisa Agropecuária Brasileira 45, 1 (2010), 72--80.
[5]
A.W. Fitzgibbon and R.B. Fisher. 1995. A Buyer's Guide to Conic Fitting. British Machine Vision Conference 9, 51 (1995), 513--522.
[6]
J.B. França-Neto, F. C. Krzyzanowski, and N. P. da Costa. 1998. O teste de tetrazólio em sementes de soja. Embrapa-CNPSoja.
[7]
G.C. Guimarães, S.D.V.F. da Rosa, A.I. Freire, A.R. Chaves, and R.S.R. Pinto. 2011. Avaliação da qualidade fisiológica de sementes de café por meio de imagens digitalizadas. In Simpósio de Pesquisa dos Cafés do Brasil. Embrapa Café.
[8]
A.L. Hoffmaster, K Fujimura, M.B. McDonald, and M.A. Bennett. 2003. An automated system for vigor testing three-day-old soybean seedlings. Seed Science and Technology 31, 3 (2003), 701--713.
[9]
J.H. Kim and Y. Kim. 2018. Instagram user characteristics and the color of their photos: Colorfulness, color diversity, and color harmony. Information Processing & Management (2018), 1--12.
[10]
J. Marcos-Filho. 2015. Seed vigor testing: an overview of the past, present and future perspective. Scientia Agricola 72, 4 (2015), 363--374.
[11]
S.M. Mastelini, M.C. da Silva, A.P.A. da C. Barbon, andS. Barbon Jr. 2016. Marbling Grading Framework Applied on Meat Boutique Environment. In XII Brazilian Symposium on Information Systems. 542--549.
[12]
T. Ojala, M. Pietikäinen, and D. Harwood. 1996. A comparative study of texture measures with classification based on featured distributions. Pattern recognition 29, 1 (1996), 51--59.
[13]
D. F. Pereira, P. H. Bugatti, F. M. Lopes, A. L. S. M. Souza, and P. T. M. Saito. 2019. Contributing to agriculture by using soybean seed data from the tetrazolium test. Data Brief 23 (2019), 103652.
[14]
R. O. Stehling, M. A. Nascimento, and A. X. Falcão. 2002. A compact and efficient image retrieval approach based on border/interior pixel classification. In CIKM. 102--109.
[15]
M. A. Stricker and M. Orengo. 1995. Similarity of color images. In SPIE Storage and Retrieval for Image and Video Databases III, Vol. 2420. International Society for Optics and Photonics, 381--392.
[16]
M.J. Swain and D.H. Ballard. 1991. Color Indexing. IJCV 7, 1 (1991), 11--32.
[17]
R. D. Vieira, S. R. M. Bittencourt, and M. Panobianco. 2003. Seed vigor: an important component of seed quality in Brasil. Ista News Bulletin, Zürich 126 (2003), 21--22.
[18]
X. Yang, X. Shen, J. Long, and H. Chen. 2012. An Improved Median-based Otsu Image Thresholding Algorithm. AASRI Procedia 3 (2012), 468--473.
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Published In
May 2019
623 pages
ISBN:9781450372374
DOI:10.1145/3330204
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Published: 20 May 2019
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