Desenvolvimento de software para a tomada de decisão no setor agropecuário: uma revisão sistemática da literatura
PDF

Palavras-chave

agricultura digital
desenvolvimento sustentável
design science research

Como Citar

Gonçalves, C. de B. Q., & Schlindwein, M. M. (2025). Desenvolvimento de software para a tomada de decisão no setor agropecuário: uma revisão sistemática da literatura. Multitemas, 30(75). https://doi.org/10.20435/multi.v30i75.4735

Resumo

O objetivo deste artigo é identificar as metodologias utilizadas para o desenvolvimento de software aplicadas em sistemas agropecuários, suas limitações e potencialidades. Para isso, realizamos uma revisão sistemática da literatura, na qual foram analisados 50 artigos publicados entre 2012 e 2023. Desse modo, este trabalho pode contribuir com pesquisas futuras sobre desenvolvimento de software na agropecuária. Verificamos que a maioria dos artigos não utilizou metodologias específicas para o desenvolvimento de software, limitando-se à linguagem de programação. Constatamos que a utilização de tecnologia é fundamental para a tomada de decisão, o aumento da produtividade, o melhoramento dos processos e a redução de custos. Entretanto, diversos desafios impossibilitam a sua propagação na agricultura familiar, como a falta de conectividade, falta de acesso à tecnologia e mão de obra qualificada para esse ambiente digital. Desse modo, existe uma lacuna de pesquisa para o desenvolvimento de software para os pequenos e médios produtores rurais.

https://doi.org/10.20435/multi.v30i75.4735
PDF

Referências

ALIANE, N.; MUÑOZ, C. Q. G.; SÁNCHEZ-SORIANO, J.. Web and MATLAB-based platform for UAV flight management and multispectral image processing. Sensors, [S. l.], v. 22, n. 11, p. 4243, 2022.

ALVES, R.; MATOS, P. A solution to prevent and minimize the consequences of accidents with farm tractors in the context of mountainous regions with low population density. Sensors, [S. l.], v. 23, n. 18, p. 7811, 2023.

ARAGO, N. et al. Smart dairy cattle farming and in-heat detection through the internet of things (IoT). International Journal of Integrated Engineering, [S. l.], v. 14, n. 1, p. 157-172, 2022.

ARSHAD, J. et al. Deployment of an intelligent and secure cattle health monitoring system. Egyptian Informatics Journal, [S. l.], v. 24, n. 2, p. 265-275, 2023.

BASKERVILLE, R.; BAIYERE, A.; GREGOR, S.; HEVNER, A. Design science research contributions: finding a balance between artifact and theory. Journal of the Association for Information Systems, [S. l.], v. 19, n. 5, p. 358-376, 2018.

BAYANO-TEJERO, S. et al. Machine to machine connections for integral management of the olive production. Computers and Electronics in Agriculture, [S. l.], v. 166, p. 104980, 2019.

BORGES, L. F.; BAZZI, C. L.; SOUZA, E. G.; MAGALHÃES, P. S. G.; MICHELON, G. K. Web software to create thematic maps for precision agriculture. Pesquisa agropecuária brasileira, [S. l.], v. 55, p. e00735, 2020.

CAICEDO-ORTIZ, J. G. et al. Monitoring system for agronomic variables based in WSN technology on cassava crops. Computers and Electronics in Agriculture, [S. l.], v. 145, p. 275-281, 2018.

CAÑADAS, J.; DEL ÁGUILA, I. M.; PALMA, J. Development of a web tool for action threshold evaluation in table grape pest management. Precision agriculture, [S. l.], v. 18, n. 6, p. 974-996, 2017.

CARLSON, B. R.; CARPENTER-BOGGS, L.; HIGGINS, S. S.; NELSON, R.; STOCKLE, C.; WEDDELL, J. Development of a web application for estimating carbon footprints of organic farms. Computers and Electronics in Agriculture, [S. l.], v. 142, p. 211-223, 2017.

CHANG, C. – H. et al. Operational forecasting inundation extents using REOF analysis (FIER) over lower Mekong and its potential economic impact on agriculture. Environmental Modelling & Software, [S. l.], v. 162, p. 105643, 2023.

CHEN, Y.; CHEN, M.; GUO, M.; WANG, J.; ZHENG, N. Pest recognition based on multi-image feature localization and adaptive filtering fusion. Frontiers in Plant Science, [S. l.], v. 14, p. 1282212, 2023.

COFAS, E. Zoosyst-computer system destinated for the analysis of the production potential of ruminant species. 2022.

DAI, D.; XAI, P.; ZHU, Z.; CHE, H. MTDL-EPDCLD: A multi-task deep-learning-based system for enhanced precision detection and diagnosis of corn leaf diseases. Plants, [S. l.], v. 12, n. 13, p. 2433, 2023.

DEBNATH, A. et al. A smartphone-based detection system for tomato leaf disease using efficientNetV2B2 and its explainability with artificial intelligence (AI). Sensors, [S. l.], v. 23, n. 21, p. 8685, 2023.

GINIGE, T.; RICHARDS, D.; GINIGE, A.; HITCHENS, M. Design for empowerment: Empowering Sri Lankan farmers through mobile-based information system. Communications of the Association for Information Systems, [S. l.], v. 46, p. 444-483, 2020.

GONZÁLEZ-ESQUIVA, J. M. GARCÍA-MATEOS, G.; HERNÁNDEZ-HERNÁNDEZ, J. L.; RUIZ-CANALES, A.; ESCARABAJAL-HENERAJOS, D.; MOLINA-MARTÍNEZ, J. M. Web application for analysis of digital photography in the estimation of irrigation requirements for lettuce crops. Agricultural water management, [S. l.], v. 183, p. 136-145, 2017.

HEVNER, A. R.; MARCH, S. T.; PARK, J.; RAM, S. Design science in information systems research. MIS quarterly, [S. l.], v. 28, n. 1, p. 75-105, mar. 2004.

HYUN, S. et al. Development of a mobile computing framework to aid decision-making on organic fertilizer management using a crop growth model. Computers and Electronics in Agriculture, [S. l.], v. 181, p. 105936, 2021.

ISLAM, M. et al. DeepCrop: deep learning-based crop disease prediction with web application. Journal of Agriculture and Food Research, [S. l.], v. 14, p. 100764, 2023.

JAIN, R. K. Experimental performance of smart IoT-enabled drip irrigation system using and controlled through web-based applications. Smart Agricultural Technology, [S. l.], v. 4, p. 100215, 2023.

JUHRIYANSYAH, D.; DWI, H.; FIRDAUS, A. Evaluation of peatland suitability for rice cultivation using matching method. Polish Journal of Environmental Studies, [S. l.], v. 30, n. 1, p. 2041-2047, 2021.

KAJORNKASIRAT, S.; RUANGSRI, J.; SUMAT, C.; INTARAMONTRI, P. Online analytics for shrimp farm management to control water quality parameters and growth performance. Sustainability, [S. l.], v. 13, n. 11, p. 1–11, 2021.

KUECHLER, B.; VAISHNAVI, V. On theory development in design science research: anatomy of a research project. European Journal of Information Systems, [S. l.], v. 17, n. 5, p. 489-504, 2008.

KUNDU, N. et al. Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning. Artificial intelligence in agriculture, [S. l.], v. 6, p. 276-291, 2022.

LACERDA, D. P.; DRESCH, A.; PROENÇA, A.; ANTUNES JUNIOR, J. A. V. Design Science Research: A research method to production engineering. Gestão & Produção, São Carlos, v. 20, n. 4, p. 741-761, 2013.

LEE, S.; CHOI, G.; PARK, H. – C.; Choi, C. Automatic classification service system for citrus pest recognition based on deep learning. Sensors, [S. l.], v. 22, n. 22, p. 8911, 2022.

LEKBANGPONG, N. et al. Precise automation and analysis of environmental factor effecting on growth of St. John’s wort. Ieee Access, [S. l.], v. 7, p. 112848-112858, 2019.

LIU, T.; CHEN, W.; WANG, Y.; WU, W.; SUN, C.; DING, J.; GUO, W. Rice and wheat grain counting method and software development based on Android system. Computers and Electronics in Agriculture, [S. l.], v. 141, p. 302-309, sept. 2017.

LI, Z.; QI, Z.; QIANJING, J.; NATHAN, S. An economic analysis software for evaluating best management practices to mitigate greenhouse gas emissions from cropland. Agricultural Systems, [S. l.], v. 186, p. 102950, 2021.

LIZZONI, L.; FEIDEN, A.; FEIDEN, A. PLAFIR: web application for rural financial planning. Biblios, [S. l.], v. 73, n. 73, p. 91-104, 2018.

MARCH, S. T.; SMITH, G. F. Design and natural science research on information technology. Decision support systems, [S. l.], v. 15, n. 4, p. 251–266, 1995.

MARKUS, M. L.; MAJCHRZAK, A.; GASSER, L. A design theory for systems that support emergent knowledge processes. MIS quarterly, [S. l.], v. 26, n. 3, p. 179-212, 2002.

MEDICI, M.; PEDERSEN, S. M.; CANAVARI, M.; ANKEN, T. A web-tool for calculating the economic performance of precision agriculture technology. Computers and Electronics in Agriculture, [S. l.], v. 181, n. June 2020, p. 105930, 2021.

MONTERO, C. S.; KAPINGA, A. F. Fortalecimento da pesquisa da ciência do design: integração da co-criação e do co-design. In: NIELSEN, P.; KIMARO, H. C. (Ed.). Information and communication technologies for development. Oslo: University of Oslo, 2019. p. 486-495

MORALES, D. A.; SÁNCHEZ-BRAVO, P.; LIPAN, L.; CANO-LAMADRID, M.; ISSA-ISSA, H.; CAMPO-GOMIS, F. J.; LLUCH, D. B. L. Designing of an enterprise resource planning for the optimal management of agricultural plots regarding quality and environmental requirements. Agronomy, [S. l.], v. 10, n. 9, p. 1-22, 2020.

MRÁZ, M. et al. Development of the web application by the information system for data processing and documentation on selected farm in agricultural production. Przeglad Elektrotechniczny, [S. l.], v. 96, n. 1, p. 218-221, 2020.

MUANGPRATHUB, J. BOONNAM., N.; KAJORNKASIRAT, S.; LEKBANGPONG, N.; WANICHSOMBAT, S.; NILLAOR, P. IoT and agriculture data analysis for smart farm. Computers and Electronics in Agriculture, [S. l.], v. 156, n. 9, p. 467-474, jan. 2019.

NDUNAGU, J. N. et al. Development of a Wireless sensor network and iot‐based smart irrigation system. Applied and Environmental Soil Science, [S. l.], v. 2022, n. 1, p. 7678570, 2022.

NI, X. et al. A deep learning-based web application for segmentation and quantification of blueberry internal bruising. Computers and Electronics in Agriculture, [S. l.], v. 201, p. 107200, 2022.

NOVA, N. A.; GONZÁLEZ, R. A. A financial inclusion app and USSD service for farmers in rural Colombia. Information Development, [S. l.], v. 42, n. 3, 2022.

NUNAMAKER JUNIOR, J. F.; CHEN, M.; PURDIN, T. D. M. Systems development in information systems research. Journal of management information systems, [S. l.], v. 7, n. 3, p. 89-106, 1990.

PEFFERS, K.; TUUNANEN, T.; ROTHENBERGER, M.; CHATTERJEE, S. A design science research methodology for information systems research. Journal of management information systems, [S. l.], v. 24, n. 3, p. 45-77, 2007.

PUN, T. B.; NEUPANE, A.; KOECH, R. A deep learning-based decision support tool for plant-parasitic nematode management. Journal of Imaging, [S. l.], v. 9, n. 11, p. 240, 2023.

PUTTINAOVARAT, S.; HORKAEW, P. A geospatial database management system for the collection of medicinal plants. Geospatial Health, [S. l.], v. 16, n. 2, 2021.

RAMOS, A.; FARIA, P. M.; FARIA, Á. Revisão sistemática de literatura: contributo para a inovação na investigação em Ciências da Educação. Revista Diálogo Educacional, Curitiba, v. 14, n. 41, p. 17-36, 2014.

ROSLIN, N. A. CHE’YA, N. N.; ROSLE, R.; ISMAIL, M. R. Smartphone application development for rice field management through aerial imagery and normalised difference vegetation index (Ndvi) analysis. Pertanika Journal of Science and Technology, [S. l.], v. 29, n. 2, p. 809-836, 2021.

ROTUNDO, J. L. et al. Development of a decision-making application for optimum soybean and maize fertilization strategies in Mato Grosso. Computers and Electronics in Agriculture, [S. l.], v. 193, feb. 2022.

SABAN, M. et al. A smart agricultural system based on PLC and a cloud computing web application using LoRa and LoRaWan. Sensors, [S. l.], v. 23, n. 5, p. 2725, 2023.

SAMPAIO, R.; MANCINI, M. Estudos de revisão sistemática : um guia para síntese. Revista Brasileira de Fisioterapia, São Carlos, v. 11, n. 1, p. 83–89, 2007.

SILVA, J. P.; NÄÄS, I. A.; ABE, J. M.; CORDEIRO, A. F. S. Classification of piglet (Sus Scrofa) stress conditions using vocalization pattern and applying paraconsistent logic Eτ. Computers and Electronics in Agriculture, [S. l.], v. 166, p. 105020, 2019.

SIMON, H. A. The Sciences of the Artificial. 3. ed. Cambridge: MIT Press, 1996.

SOLER-MÉNDEZ, M.; PARRAS-BURGOS, D.; BENOUNA-BENNOUNA, R.; MOLINA-MARTÍNEZ, J. M. Agroclimatic Evolution web application as a powerful solution for managing climate data. Scientific Reports, [S. l.], v. 12, n. 1, p. 6716, 2022.

TLHOBOGANG, B.; SETOTO, B. Developing a small-scale agriculture knowledge and information dissemination system: tankyu practice approach. In: INTERNATIONAL CONGRESS ON ADVANCED APPLIED INFORMATICS, 7., 2018, Yonago. Proceedings […]. Yonago: [S. n.], 2018. p. 244–249.

VENABLE, J.; PRIES-HEJE, J.; BASKERVILLE, R. FEDS: A framework for evaluation in design science research. European Journal of Information Systems, [S. l.], v. 25, n. 1, p. 77-89, 2016.

VINCENTDO, V.; SURANTHA, N. Nutrient film technique-based hydroponic monitoring and controlling system using ANFIS. Electronics, [S. l.], v. 12, n. 6, p. 1446, 2023.

VOURAKI, S.; SKOURTIS, I.; PSICHOS, K.; JONES, W.; DAVIS, C.; JOHNSON, M.; RUPÉREZ, L. R.; THEODORIDIS, A.; ARSENOS, G. A decision support system for economically sustainable sheep and goat farming. Animals, [S. l.], v. 10, n. 12, p. 1-18, 2020.

WALLS, J. G.; WIDMEYER, G. R.; EL SAWY, O. A. Building an information system design theory for vigilant EIS. Information systems research, [S. l.], v. 3, n. 1, p. 36-59, 1992.

WAN, X. – F.; ZHENG, T.; CUI, J.; ZHANG, F.; MA, Z. – Q.; YANG, Y. Near field communication-based agricultural management service systems for family farms. Sensors, [S. l.], v. 19, n. 20, p. 4406, 2019.

WANG, C.; TANG, Y.; AHMAD-AKHIA, M. F.; ABDUL-RAHMAN, H.; YAP, J. B. H. Cloud-Based system for sustainable stingless bee farm. Journal of Internet Technology, [S. l.], v. 23, n. 3, p. 539-551, 2022.

WANG, H. et al. PreCowKetosis: a shiny web application for predicting the risk of ketosis in dairy cows using prenatal indicators. Computers and Electronics in Agriculture, [S. l.], v. 206, p. 107697, 2023.

WECKESSER, F.; BECK, M.; HÜLSBERGEN, K. – J.; PEISL, S. A Digital Advisor Twin for Crop Nitrogen Management. Agriculture, [S. l.], v. 12, n. 2, 2022.

YANG, G.; LEI, L.; PING, G.; MO, L. A flexible decision support system for irrigation scheduling in an irrigation district in China. Agricultural water management, [S. l.], v. 179, p. 378-389, 2017.

YU, Z. et al. Ricemapengine: a google earth engine-based web application for fast paddy rice mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, [S. l.], v. 16, p. 7264-7275, jan. 2023.

ZHAO, Y.; SUN, C.; XU, X.; CHEN, J. RIC-Net: a plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism. Computers and Electronics in Agriculture, [S. l.], v. 193, p. 106644, 2022.

Creative Commons License
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.

Copyright (c) 2025 Claudia de Brito Quadros Gonçalves, Madalena Maria Schlindwein