COMO O USO DA INTELIGÊNCIA ARTIFICIAL TEM CONTRIBUÍDO COM A CULTURA DO AMENDOIM?

Diego Cezar Dalpian; Oswaldo Lazaro Mendes

doi:10.31510/infa.v19i2.1478

Autores

Diego Cezar Dalpian Faculdade de Tecnologia de Taquaritinga (Fatec) – Taquaritinga – SP – Brasil https://orcid.org/0000-0001-8715-3293
Oswaldo Lazaro Mendes Faculdade de Tecnologia de Taquaritinga (Fatec) – Taquaritinga – SP – Brasil https://orcid.org/0000-0002-0761-2670

DOI:

https://doi.org/10.31510/infa.v19i2.1478

Palavras-chave:

Aprendizado de máquina, E-agriculture, Sensoriamento remoto, Gestão estratégica

Resumo

O amendoim é um importante alimento que contribui para a segurança alimentar mundial. Esse estudo bibliométrico exploratório usou a base SCOPUS como consulta e buscou mostrar quais estudos, com o uso da inteligência artificial, foram feitos nos últimos dez anos na cultura do amendoim. O resultado mostrou que são poucos os estudos com esse tema e que a aplicação da inteligência artificial aos dados dos sensores de gerenciamento e de tomada de decisão, podem contribuir com os produtores e gestores na assertividade da sua administração, podendo portanto, agregar valor aos produtos, aumentar a lucratividade, a produtividade, e a sustentabilidade dos negócios agrícolas, por meio da compreensão do conjunto de dados.

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Referências

AGHILESH K, AJAY KUMAR, SMRITI AGARWAL, MANOJ CHANDRA GARG & HIMANSHU JOSHI (2021) Use of artificial intelligence to optimize textile wastewater biosorption using agricultural waste. Environmental Technology, DOI: 10.1080/09593330.2021.1961874. DOI: https://doi.org/10.1080/09593330.2021.1961874

ALMEIDA, O. B. et al.Blockchain in Agriculture: A Systematic Literature Review. In: 2018, Cham. Conferência Internacional sobre Tecnologias e Inovação. Cham: CITI 2018, 2018. p. 44-56. DOI: https://doi.org/10.1007/978-3-319-67283-0. DOI: https://doi.org/10.1007/978-3-030-00940-3_4

AN R, PEREZ-CRUET J, WANG J. We got nuts! use deep neural networks to classify images of common edible nuts. Nutrition and Health, 2022, DOI: 10.1177/02601060221113928. DOI: https://doi.org/10.1177/02601060221113928

BORBA, M. DA C.; RAMOS, J. S.; RAMBORGER, B. M.; MACHADO, J.A. Gestão no meio agrícola com o apoio da Inteligência Artificial: uma análise da digitalização da agricultura. Rev Agro Amb, v. 15, n. 3, e9337, 2022 - e-ISSN 2176-9168. DOI: https://doi.org/10.17765/2176-9168.2022v15n3e9337

BRONSON, K.; KNEZEVIC, I. Big Data in food and agriculture. Big Data & Society, v. 3, n. 1, p. 2053951716648174, 2016. DOI: https://doi.org/10.1177/2053951716648174

CONAB - Companhia Nacional de Abastecimento (2022). Acompanhamento da safra brasileira de grãos – Safra 2021/2022. 8º levantamento.101 Disponível em: https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos. Acesso em: 25/09/2022.

CUNHA, J. B. DE A.; NUNES, I. A; GAVA, C. A. T.; SANTOS, R. C. DOS; MARTINS, L. M. V; FERNANDES JUNIOR, P. Diversidade cultural de bactérias isoladas de nódulos de amendoim (Arachis hypogaea L.) cultivados em solos do Nordeste do Brasil. I.In: CONGRESSO BRASILEIRO DE CIÊNCIAS DO SOLO, 34., 2013. Florianópolis. Anais... Viçosa, MG: Sociedade Brasileira de Ciência do Solo, 2013.

DZOTSI, K. A.; BASSO B.; JONES, J.W. Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT, Ecological Modelling, Volume 260, 2013, Pages 62-76, ISSN 0304-3800, https://doi.org/10.1016/j.ecolmodel.2013.03.017. DOI: https://doi.org/10.1016/j.ecolmodel.2013.03.017

EVANS, K. J.; TERHORST, A.; KANG, B. H. From Data to Decisions: Helping Crop Producers Build Their Actionable Knowledge. Critical Reviews in Plant Sciences, London, v. 36, n. 2, p. 71-88, 2017. DOI: https://doi.org/10.1080/07352689.2017.1336047. DOI: https://doi.org/10.1080/07352689.2017.1336047

FENG, Z. Constructing rural e-commerce logistics model based on ant colony algorithm and artificial intelligence method. Soft Computing, v. 8, 2019. DOI: https://doi.org/10.1007/s00500-019-04046-8. DOI: https://doi.org/10.1007/s00500-019-04046-8

HARFOUCHE AL, JACOBSON DA, KAINER D, ROMERO JC, HARFOUCHE AH, SCARASCIA MUGNOZZA G, MOSHELION M, TUSKAN GA, KEURENTJES JJB, ALTMAN A. Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence. Trends Biotechnol. 2019 Nov;37(11):1217-1235. doi: 10.1016/j.tibtech.2019.05.007. Epub 2019 Jun 21. PMID: 31235329. DOI: https://doi.org/10.1016/j.tibtech.2019.05.007

HARFOUCHE, AL; JACOBSON, DA; KAINER, D.; ROMERO, JC; HARFOUCHE, AH; MUGNOZZA, GS; MOSHELION, M.; TUSKAN, GA; KEURENTJES, JJ; ALTMAN, A. Accelerating the improvement of climate-resilient plants by applying state-of-the-art artificial intelligence. Biotechnology Trends. 2019, 37, 1217-1235. DOI: https://doi.org/10.1016/j.tibtech.2019.05.007

HASHIMOTO, Y. et al.Intelligent systems for agriculture in Japan. IEEE Control Systems Magazine, Washington, v. 21, n. 5, p. 71-85, 2001. DOI: https://doi.org/10.1109/37.954520. Herbicide sprayer robot for corn fields. In: 2013, Tehran. 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM). Tehran: IEEE, 2013. p. 468-473.

HUTSON, M. AI Glossary: Artificial intelligence, in so many words. Science, New York, v. 357, n. 6346, p. 19-19, 2017. DOI: https://doi.org/10.1126/science.357.6346.19. DOI: https://doi.org/10.1126/science.357.6346.19

KADIYALA, M.D.M.; NEDUMARAN, S.; CHUKKA S. PIARA SINGH, MOHAMMAD A. IRSHAD, BANTILAN, M.C.S. An integrated crop model and GIS decision support system for assisting agronomic decision making under climate change. Science of The Total Environment, Volumes 521–522, 2015, Pages 123-134, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2015.03.097. DOI: https://doi.org/10.1016/j.scitotenv.2015.03.097

KAPLAN, A.; HAENLEIN, M. Siri. Who’s thefairest in the land? On the interpretations, illustrations, andimplications of artificial intelligence. Business Horizons, Amsterdam, v. 62, n. 1, p. 1525, 2019. DOI: https://doi.org/10.1016/j.bushor.2018.08.004. DOI: https://doi.org/10.1016/j.bushor.2018.08.004

KARGAR, A. H. B.; SHIRZADIFAR, A. M. Automatic weed detection system and smart

KUMAR, A. V. S. P.; BHRAMARAMBA, R. Adapting mining into agriculture sector with machine learning techniques. International Journal of Control and Automation, Seul, v. 10, n. 7, p. 13-22, 2017. DOI: https://doi.org/10.14257/ijca.2017.10.7.02. DOI: https://doi.org/10.14257/ijca.2017.10.7.02

LEE, R.-Y. , HSU, C.-H. , SHIU, Y.-S. Applying machine learning algorithms and WorldView-2 satellite imagery to classify crop types. Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings (2015).

LI, D.; YANG, H. State-of-the-art Review for Internet of Things in Agriculture. Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, Pequim, v. 49, n. 1, p. 1-20, 2018. DOI: https://doi.org/10.6041/j.issn.10001298.2018.01.001.

LI, P. , DU, Z. , CHANG, C. , XU, G. , XU, CC. Efficient Catalytic Conversion of Waste Peanut Shells into Liquid Biofuel: An Artificial Intelligence Approach. Energia e Combustíveis, 2020 34 (2), 1791-1801 DOI: 10.1021/acs.energyfuels.9b03433. DOI: https://doi.org/10.1021/acs.energyfuels.9b03433

LIU, M. 2020. Based on the Network and Information Technology, the Actual Performance Evaluation System of Heavy Metal Removal Biomass Materials in Water Was Developed. Journal of Physics: Conference Series 1574 (2020) 012096 IOP Publishing DOI:10.1088/1742-6596/1574/1/012096. DOI: https://doi.org/10.1088/1742-6596/1574/1/012096

PERINI, A.; SUSI, A. Developing a decision support system for integrated production in agriculture. Environmental Modellingand Software, v. 19, n. 9, p. 821-829, 2004. DOI: https://doi.org/10.1016/j.envsoft.2003.03.001. DOI: https://doi.org/10.1016/j.envsoft.2003.03.001

PETETIN, L. The COVID-19 crisis: an opportunity to integrate food democracy into post-pandemic food systems Eur. J. Risk Regul. (2020), pp. 1-11, 10.1017/err.2020.40. DOI: https://doi.org/10.1017/err.2020.40

PIVOTO, D. et al. Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, v. 5, n. 1, p. 21-32, 2018. DOI: https://doi.org/https://doi.org/10.1016/j.inpa.2017.12.002. DOI: https://doi.org/10.1016/j.inpa.2017.12.002

ROOPAEI, M.; RAD, P.; CHOO, K.-K. R. Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by ThermalImaging. IEEE Cloud Computing, New York, v. 4, n. 1,p. 10-15, 2017. DOI: https://doi.org/10.1109/mcc.2017.5. DOI: https://doi.org/10.1109/MCC.2017.5

SANTOS, A.F.; LACERDA, L.N.; ROSSI, C.; MORENO, L.D.A.; OLIVEIRA, M.F.; PILON, C.; SILVA, R.P.; VELLIDIS, G. Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks. Remote Sens. 2022, 14, 93. https://doi.org/10.3390/rs14010093. DOI: https://doi.org/10.3390/rs14010093

SEEBACHER, S.; SCHÜRITZ, R. Blockchain Technology as an Enabler of Service Systems: A Structured Literature Review. In: 2017, Geneva. International Conference on Exploring Services Science. Geneva: [s. n.], 2017. p. 12-23. DOI: https://doi.org/10.1007/978-3-319-56925-3_2. DOI: https://doi.org/10.1007/978-3-319-56925-3_2

SINGH, D.; CHAUDHARY, P.; TAUNK, J.; SINGH, C.K.; SINGH, D.; TOMAR, R.S.S.; ASKI, M.; KONJENGBAM, N.S.; RAJE, R.S.; SINGH, S.; SENGAR, R.S.; YADAV, R.K.; PAL, M. Fab Advances in Fabaceae for Abiotic Stress Resilience: From ‘Omics’ to Artificial Intelligence. Int. J. Mol. Sci. 2021, 22, 10535. https://doi.org/10.3390/ijms221910535. DOI: https://doi.org/10.3390/ijms221910535

TUCUNDUVA, S. Tabela de composição de alimentos. 4. Ed. São Paulo: Manole, 2013.

UNTARU, M.; ROTARESCU, V.; DORNEANU, L. Artificial neural networks for sustainable agribusiness: A case study of five energetic crops. Agrociencia, Cidade do México, v. 46, n. 5, p. 507-518, 2012.

WOLFERT, S. et al. Big Data in Smart Farming: a review. Agricultural Systems, London, v. 153, p. 6980, 2017. DOI: https://doi.org/https://doi.org/10.1016/j.agsy.2017.01.023. DOI: https://doi.org/10.1016/j.agsy.2017.01.023

YANG, M. , CHEN, Q. , KUTSANEDZIE, FYH , (...), GUO, Z. , OUYANG, Q. Portable spectroscopy system determination of acid value in peanut oil based on variables selection algorithms. Measurement, Volume 103, 2017, Pages 179-185, ISSN 0263 2241, https://doi.org/10.1016/j.measurement.2017.02.037. DOI: https://doi.org/10.1016/j.measurement.2017.02.037

YOSHIDA, K., ISHIKAWA, E., JOSHI, M., LECHAT, H., AYOUNI, F., BONNEFILLE, M. (2012). Quality Control and Rancidity Tendency of Nut Mix Using an Electronic Nose. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_21. DOI: https://doi.org/10.1007/978-3-642-27387-2_21