Emprego de Simulações Computacionais em Problemas Envolvendo Agricultura: Um Estudo de Mapeamento Sistemático

  • Dienefer Fialho dos Santos UNIPAMPA
  • Fábio Paulo Basso UNIPAMPA
  • Marcelo Caggiani Luizelli UNIPAMPA
  • Saimon Martins Cabrera UNIPAMPA

Resumo


O avanço das tecnologias de redes de sensores sem fio (RSSF) permite monitorar ambientes de forma assistida por software. Entre os vários contextos de emprego, a agricultura inteligente desempenha um papel importante na sociedade, caracterizando um potencial para a realização de estudos aplicados. No entanto, os custos associados aos equipamentos em cenários envolvendo diversos sensores dificultam o exercício da aprendizagem baseada em problemas. Estudos voltados para simulações computacionais têm o potencial de explorar o tema sem tais custos, portanto, um potencial de exploração em disciplinas de aprendizagem baseadas em problemas. A literatura da área carece de uma caracterização do uso de simulações computacionais para problemas envolvendo agricultura. Este artigo apresenta os resultados de um estudo de mapeamento sistemático, proporcionando essa caracterização por meio de 35 estudos analisados.

Referências

Agrawal, H., Dhall, R., Iyer, K. S. S., and Chetlapalli, V. (2019). An improved energy ecient system for iot enabled precision agriculture.

Bandur, D., Jaksíc, B., Bandur, M., and Jovíc, S. (2019). An analysis of energy eciency in wireless sensor networks (wsns) applied in smart agriculture. Computers and Electronics in Agriculture, 156:500–507.

Bayrakdar, M. E. (2020). Employing sensor network based opportunistic spectrum utilization for agricultural monitoring. Sustainable Computing: Informatics and Systems, 27:100404.

Bhanu, B. B., Husain, M. A., and Mirza, M. A. (2020). A high throughput oering iot system for agriculture applications.

Castellanos, G., Deruyck, M., Martens, L., and Joseph, W. (2020). System assessment of wusn using nb-iot uav-aided networks in potato crops. IEEE Access, 8:56823–56836.

Chen, Y., Chanet, J.-P., Hou, K.-M., Shi, H., and Sousa, G. (2015). A scalable context-aware objective function (scaof) of routing protocol for agricultural low-power and lossy networks (rpal). Sensors, 15:34.

de Souza e Luis Dourado, S. C. (2015). Aprendizagem baseada em problemas (abp): Um método de aprendizagem inovador para o ensino educativo. HOLOS, 5(0):182–200.

Dhall, R. and Agrawal, H. (2018). An improved energy ecient duty cycling algorithm for iot based precision agriculture.

Faid, A., Sadik, M., and Sabir, E. (2020). Iot-based low cost architecIn 2020 International Wireless Communications and Mobile ture for smart farming. Computing (IWCMC), pages 1296–1302. IEEE.

Ibrahim, H., Mostafa, N., Halawa, H., Elsalamouny, M., Daoud, R., Amer, H., Shaarawi, A., Khattab, A., and Elsayed, H. (2018). A high availability networked control system architecture for precision agriculture. 2018 International Conference on Computer and Applications (ICCA), pages 457–460.

Iqbal, R. and Butt, T. (2020). Safe farming as a service of blockchain-based supply chain management for improved transparency.

Jain, J. K. (2020). A coherent approach for dynamic cluster-based routing and coverage hole detection and recovery in bi-layered wsn-iot. Wireless Personal Communications, 114.

Jiang, X., Yi, W., Chen, Y., and He, H. (2018). Energy Ecient Smart Irrigation System Based on 6LoWPAN: 4th International Conference, ICCCS 2018, Haikou, China, June 8-10, 2018, Revised Selected Papers, Part V, pages 308–319.

Khan, F. and Kumar, D. (2019). Ambient crop eld monitoring for improving context based agricultural by mobile sink in wsn. Journal of Ambient Intelligence and Humanized Computing.

Khan, S., Pathan, A.-S. K., and Alrajeh, N. A. (2016). Wireless Sensor Networks: Current Status and Future Trends. CRC Press, Inc., Boca Raton, FL, USA, 1st edition.

Lavanya, G., Rani, C., and GaneshKumar, P. (2020). An automated low cost iot based fertilizer intimation system for smart agriculture. Sustainable Computing: Informatics and Systems, 28:100300.

Linsner, S., Varma, R., and Reuter, C. (2019). Vulnerability assessment in the smart farming infrastructure through cyberattacks. In Meyer-Aurich, A., Gandorfer, M., Barta, N., Gronauer, A., Kantelhardt, J., and Floto, H., editors, 39. GIL-Jahrestagung, Digitalisierung für landwirtschaftliche Betriebe in kleinstrukturierten Regionen ein Widerspruch in sich?, pages 119–124, Bonn. Gesellschaft für Informatik e.V. Iot sensor-based smart agricultural system.

Mahalakshmi, J., Kuppusamy, K., Kaleeswari, C., and Maheswari, P. (2020). In Subramanian, B., Chen, S.-S., and Reddy, K. R., editors, Emerging Technologies for Agriculture and Environment, pages 39–52, Singapore. Springer Singapore.

Mukherjee, A., Misra, S., Sukrutha, A., and Raghuwanshi, N. S. (2020). Distributed aerial processing for iot-based edge uav swarms in smart farming. Computer Networks, 167:107038.

Musaazi, K., Bulega, T., and Lubega, S. (2014). Energy ecient data caching in wireless sensor networks: A case of precision agriculture. pages 154– 163.

Nandhini, A., Rajendran, H., Sankararajan, R., and Indumathi, K. (2017). Web enabled plant disease detection system for agricultural applications using wmsn. Wireless Personal Communications.

Nicolae, M., Popescu, D., Merezeanu, D., and Ichim, L. (2019). Large scale wireless sensor networks based on xed nodes and mobile robots in precision agriculture. In Aspragathos, N. A., Koustoumpardis, P. N., and Moulianitis, V. C., editors, Advances in Service and Industrial Robotics, pages 236–244, Cham. Springer International Publishing.

Ojha, T., Misra, S., and Raghuwanshi, N. S. (2017). Sensing-cloud: Leveraging the benets for agricultural applications. Computers and electronics in agriculture, 135:96–107.

Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studies in software engineering. Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, 17.

Petersen, K., Vakkalanka, S., and Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1–18.

Shamshiri, R. R., Bojic, I., van Henten, E., Balasundram, S. K., Dworak, V., Sultan, M., and Weltzien, C. (2020). Model-based evaluation of greenhouse microclimate using iot-sensor data fusion for energy ecient crop production. Journal of Cleaner Production, 263:121303.

Shinghal, D., Srivastava, N., et al. (2017). Wireless sensor networks in agriculture: for potato farming. Neelam, Wireless Sensor Networks in Agriculture: For Potato Farming (September 22, 2017).

Shipu, X., Yunsheng, W., Yong, L., Weixiong, R., Mingzhou, M., Jingyin, Z., and Chenxi, Z. (2018). Research on wsn routing algorithm for vegetable greenhouse. pages 37–42.

Stewart, J., Stewart, R., and Kennedy, S. (2017). Dynamic iot management system using k-means machine learning for precision agriculture applications. In Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing, ICC ’17, New York, NY, USA. Association for Computing Machinery.

Tanaka, K., Nishigaki, M., Sode, M., and Mizuno, T. (2018). Low delay data gathering method for rice cultivation management system: Iot specialized outdoor communication procedure. pages 139–143.

Trotta, A., Di Felice, M., Perilli, L., Scarselli, E. F., and Cinotti, T. S. (2020). Bee-drones: Ultra low-power monitoring systems based on unmanned aerial vehicles and wake-up radio ground sensors. Computer Networks, 180:107425.

Uddin, M. A., Ayaz, M., Aggoune, E. M., Mansour, A., and Jeune, D. L. (2019). Aordable broad agile farming system for rural and remote area. IEEE Access, 7:127098–127116.

Uddin, M. A., Mansour, A., Jeune, D. L., and Aggoune, E. H. M. (2017). Agriculture internet of things: Ag-iot. In 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), pages 1–6.

Uddin, M. A., Mansour, A., Jeune, D. L., Ayaz, M., and Aggoune, E.-H. M. (2018). Uav-assisted dynamic clustering of wireless sensor networks for crop health monitoring. Sensors (Basel, Switzerland), 18(2).

Wang, K. I.-K., Wu, S., Ivoghlian, A., Salcic, Z., Austin, A., and Zhou, X. (2019). Lws: A lorawan wireless underground sensor network simulator for agriculture applications. In 2019 IEEE (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 475–482.

Wu, H., Li, Q., Zhu, H., Han, X., Li, Y., and Yang, B. (2020a). Directional sensor placement in vegetable greenhouse for maximizing target coverage without occlusion. Wireless Networks, 26.

Wu, S., Austin, A. C., Ivoghlian, A., Bisht, A., Kevin, I., and Wang, K. (2020b). Long range wide area network for agricultural wireless underground sensor networks. Journal of Ambient Intelligence and Humanized Computing, pages 1–17.

Yassine, S., Fatima, L., et al. (2019). Dynamic cluster head selection method for wireless sensor network for agricultural application of internet of things based fuzzy c-means clustering algorithm. In 2019 7th Mediterranean Congress of Telecommunications (CMT), pages 1–9. IEEE.

Zhang, M., Xiong, S., and Wang, L. (2019). Sensor-cloud based precision sprinkler irrigation management system.
Publicado
27/09/2021
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SANTOS, Dienefer Fialho dos; BASSO, Fábio Paulo; LUIZELLI, Marcelo Caggiani; CABRERA, Saimon Martins. Emprego de Simulações Computacionais em Problemas Envolvendo Agricultura: Um Estudo de Mapeamento Sistemático. In: WORKSHOP EM MODELAGEM E SIMULAÇÃO DE SISTEMAS INTENSIVOS EM SOFTWARE (MSSIS), 3. , 2021, Joinville. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 20-29. DOI: https://doi.org/10.5753/mssis.2021.17256.