ARTIFICIAL INTELLIGENCE, APPLIED IN AGRICULTURE
Keywords:
Artificial Intelligence, Agriculture, Agent-based Models, Cellular Automaton, Genetic Algorithms, Artificial Neural Network, Fuzzy LogicAbstract
The objective of this paper is to review how artificial intelligence (AI) tools have helped the agricultural sector. For this, a search process was carried out in the main scientific repositories. The investigations were then classified according to the Artificial Intelligence technique applied. At the end, it concludes, the great utility of AI tools in the agricultural sector, especially in determining the use of land, water and agricultural production.
References
Al-Amin, S., Berglund, E. Z., & Larson, K. (2014). Complex Adaptive System Framework to Simulate Adaptations of Human-Environmental Systems to Climate Change and Urbanization: The Verde River Basin. World Environmental and Water Resources Congress 2014: Water Without Borders - Proceedings of the 2014 World Environmental and Water Resources Congress.
Ali, M., Deo, R. C., Downs, N. J., & Maraseni, T. (2018). Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programing algorithm: A new hybrid copula-driven approach. Agricultural and Forest Meteorology, 263(January), 428–448. https://doi.org/10.1016/j.agrformet.2018.09.002
Arasteh, R., Ali Abbaspour, R., & Salmanmahiny, A. (2019). A modeling approach to path dependent and non-path dependent urban allocation in a rapidly growing region. Sustainable Cities and Society, 44(October 2018), 378–394. https://doi.org/10.1016/j.scs.2018.10.029
Awad, M. (2016). New mathematical models to estimate wheat Leaf Chlorophyll Content based on Artificial Neural Network and remote sensing data. 2016 IEEE International Multidisciplinary Conference on Engineering Technology, IMCET 2016, 86–91. https://doi.org/10.1109/IMCET.2016.7777432
Badaracco, N., Mariño, S., & Alfonzo, P. (2014). Modelización De La Asignación De Aulas Con Técnicas Simbólicas De La Ia Como Ayuda A La Toma De Decisiones. Revista Electrónica de Estudios Telemáticos, 13(2), 16–35.
Chen, X., Xv, J., Ye, K., Zhou, Y., You, J., & Jin, K. (2018). A Brief Discussion on the Applications of Artificial Intelligence in the Field of Valuation. Journal of Physics: Conference Series, 1069(1). https://doi.org/10.1088/1742-6596/1069/1/012010
Cisneros, A. M. (2013). Sistema De Inteligencia Artificial Como Soporte A La Toma De Decisiones Financieras En Las Sociedades De Corretaje. Revista del Centro de Investigación de Ciencias Administrativas y Gerenciales, 4(2), 54–73.
Dauby, J. P., & Upholzer, S. (2011). Exploring behavioral dynamics in systems of systems. Procedia Computer Science, 6, 34–39. https://doi.org/10.1016/j.procs.2011.08.009
Deep, S., & Saklani, A. (2014). Urban sprawl modeling using cellular automata. Egyptian Journal of Remote Sensing and Space Science, 17(2), 179–187. https://doi.org/10.1016/j.ejrs.2014.07.001
Etienne, M., Bourgeois, M., & Souchèreb, V. (2008). Participatory modelling of fire prevention and urbanisation in southern France: From coconstructing to playing with the model. Proc. iEMSs 4th Biennial Meeting - Int. Congress on Environmental Modelling and Software: Integrating Sciences and Information Technology for Environmental Assessment and Decision Making, iEMSs 2008, 2, 972–979.
Feng, Y. J., Liu, Y., & Han, Z. (2011). Land use simulation and landscape assessment by using genetic algorithm based on cellular automata under different sampling schemes. Chinese Journal of Applied Ecology, 22(4), 957–963.
Fowler, K. R., Jenkins, E. W., Ostrove, C., Chrispell, J. C., Farthing, M. W., & Parno, M. (2015). A decision making framework with MODFLOW-FMP2 via optimization: Determining trade-offs in crop selection. Environmental Modelling and Software, 69, 280–291. https://doi.org/10.1016/j.envsoft.2014.11.031
Fürst, C., Volk, M., Pietzsch, K., & Makeschin, F. (2010). Pimp your landscape: A tool for qualitative evaluation of the effects of regional planning measures on ecosystem services. Environmental Management, 46(6), 953–968. https://doi.org/10.1007/s00267- 010-9570-7
Gimona, A., & Polhill, J. G. (2011). Exploring robustness of biodiversity policy with a coupled meta community and agent-based model. Journal of Land Use Science, 6(2–3), 175–193. https://doi.org/10.1080/1747423X.2011.558601
Gimona, A., Polhill, J. G., & Davies, B. (2011). Sinks, sustainability, and conservation incentives. En J. Liu, V. Hull, A. T. Morzillo, & J. A. Wiens (Eds.), Sources, Sinks and Sustainability (pp. 155–178). https://doi.org/10.1017/CBO9780511842399.010
Guresen, E., & Kayakutlu, G. (2011). Definition of Artificial Neural Networks with comparison to other networks. Procedia Computer Science, 3, 426–433. https://doi.org/10.1016/j.procs.2010.12.071
Jaiswal, R. S., & Sarode, M. V. (2015). An Overview on Fuzzy Logic and Fuzzy Elements. International Research Journal of Computer Science, 3(2), 29–34. Recuperado de http://www.irjcs.com/volumes/vol2/iss3/05.MACS10088.pdf
Jang, S. H., Roh, J. H., Kim, W., Sherpa, T., Kim, J. H., & Park, J. B. (2011). A novel binary ant colony optimization: Application to the unit commitment problem of power systems. Journal of Electrical Engineering and Technology, 6(2), 174–181. https://doi.org/10.5370/JEET.2011.6.2.174
Larrañaga, J. M., Zulueta, E., Elizagarate, F., & Bernardo, J. A. (2011). Algoritmos Meméticos En Problemas De Investigación Operativa. Revista de Dirección y Administración de Empresas, (18), 189–208. Recuperado de https://www.ehu.eus/documents/2069587/2114295/18_12.pdf
Li, J. M., & Wang, M. F. (2010). Chaotic Genetic Algorithm-Based Forest Harvest Adjustment. Journal of Donghua University, 27(2).
Li, J., Rodriguez, D., & Tang, X. (2017). Effects of land lease policy on changes in land use, mechanization and agricultural pollution. Land Use Policy, 64(1), 405–413. https://doi.org/10.1016/j.landusepol.2017.03.008
López Rodríguez, A., & Silega Martínez, N. (2015). Ontología para la clasificación del riesgo de crédito en el Banco Nacional de Cuba Ontology to credit risk classification in the National Bank of Cuba. Serie Científica de la Universidad de las Ciencias Informáticas, 8(1), 17–31.
Ma, S., Wu, K., Lao, C., Zhong, Y., Zhang, T., & Huang, T. (2017). Establishment and application of iZone system for intelligently identifying preserved zones of permanent prime farmland. Transactions of the Chinese Society of Agricultural Engineering, 33(2), 276–282.
Man, K. F., Tang, K. S., & Kwong, S. (1996). Genetic algorithms: Concepts and applications. IEEE Transactions on Industrial Electronics, 43(5), 519–534. https://doi.org/10.1109/41.538609
Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, K., … Dewhurst, M. (2017). Un futuro que funciona: automatización, empleo y productividad (p. 27). p. 27.
Martin, B. (2007). Damage spreading and μ-sensitivity on cellular automata. Ergodic Theory and Dynamical Systems, 27(2), 545–565. https://doi.org/10.1017/S0143385706000782
Mehryar, S., Sliuzas, R., Schwarz, N., Sharifi, A., & van Maarseveen, M. (2019). From individual Fuzzy Cognitive Maps to Agent Based Models: Modeling multi-factorial and multi-stakeholder decision-making for water scarcity. Journal of Environmental Management, 250(September), 109482. https://doi.org/10.1016/j.jenvman.2019.109482
MeiFang, W., & JinMing, L. (2008). Adaptive genetic algorithm-based forest harvest adjustment. Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008, 1, 541–544. https://doi.org/10.1109/ISKE.2008.4730990
Nouiri, I., Yitayew, M., Maßmann, J., & Tarhouni, J. (2015). Multi-objective Optimization Tool for Integrated Groundwater Management. Water Resources Management, 29(14), 5353–5375. https://doi.org/10.1007/s11269-015-1122-8
Ochoa, A. J., Orellana, A., Sánchez, Y., & Dávila, F. (2014). Componente web para el análisis de información clínica usando la técnica de Minería de Datos por agrupamiento Web component for the analysis of clinical information using the technique of clustering data mining. Revista Cubana de Informática Médica, 6(1), 5–16.
Oliveira, A., & Nero, M. (2013). Application of fuzzy logic in prediction of fire in João Pessoa City - Brazil. Communications in Computer and Information Science, 399 PART I, 323–334. https://doi.org/10.1007/978-3-642-41908-9_33
Pandey, B. K., & Khare, D. (2017). Analyzing and modeling of a large river basin dynamics applying integrated cellular automata and Markov model. Environmental Earth Sciences, 76(22), 1–12. https://doi.org/10.1007/s12665-017-7133-4
Pavlidis, N. G., Parsopoulos, K. E., & Vrahatis, M. N. (2005). Computing Nash equilibria through computational intelligence methods. Journal of Computational and Applied Mathematics, 175(1 SPEC. ISS.), 113–136. https://doi.org/10.1016/j.cam.2004.06.005
Polhill, J. G., Gimona, A., & Gotts, N. M. (2010). Analysis of incentive schemes for biodiversity using a coupled agent-based model of land use change and species metacommunity model. Modelling for Environment’s Sake: Proceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010, 1, 809–816.
Polhill, J. G., Gimona, A., & Gotts, N. M. (2013). Nonlinearities in biodiversity incentive schemes: A study using an integrated agent-based and metacommunity model. Environmental Modelling and Software, 45, 74–91. https://doi.org/10.1016/j.envsoft.2012.11.011
Ramírez, R. A., Díaz, Y. V., & Fernández, Y. A. (2016). Jurimetría : Una opción para la sociedad . Jurimetrics : An choice for society . Serie Científica de la Universidad de las Ciencias Informáticas Vol., 9(4), 1–6.
Salvini, G., Ligtenberg, A., van Paassen, A., Bregt, A. K., Avitabile, V., & Herold, M. (2016). REDD+ and climate smart agriculture in landscapes: A case study in Vietnam using companion modelling. Journal of Environmental Management, 172, 58–70. https://doi.org/10.1016/j.jenvman.2015.11.060
Santacreu, L. J., Talavera, A., Aguasca, R., & Galván, B. J. (2015). Sistema experto para tomar decisiones de emergencias y seguridad ante meteorología adversa. Dyna Ingenieria E Industria, 90(5), 502–512.
Shaharum, N. S. N., Shafri, H. Z. M., Gambo, J., & Abidin, F. A. Z. (2018). Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms. Remote Sensing Applications: Society and Environment, 10(January), 24–35. https://doi.org/10.1016/j.rsase.2018.01.002
Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S., & Islam, T. (2015). Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information. Environmental Processes, 2(1), 61–78. https://doi.org/10.1007/s40710-015-0062-x
Su, X. F., Asseng, S., Campbell, P., Cook, F., Schilizzi, S., Nancarrow, B., … Brockman, H. (2005). A conceptual model for simulating farmer decisions and land use change. MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings, 156–161.
Suresh, K. R., & Mujumdar, P. P. (2004). A fuzzy risk approach for performance evaluation of an irrigation reservoir system. Agricultural Water Management, 69(3), 159– 177. https://doi.org/10.1016/j.agwat.2004.05.001
Tian, G., & Qiao, Z. (2014). Modeling urban expansion policy scenarios using an agentbased approach for Guangzhou Metropolitan Region of China. Ecology and Society, 19(3). https://doi.org/10.5751/ES-06909-190352
Torres, S., Lugo, J. A., Piñero, P. Y., Torres, K. M., Perdomo, A., Cuza, B., & Aldana, M. L. (2014). Técnicas formales y de inteligencia artificial para la gestión de recursos humanos en proyectos informáticos. Revista Cubana de Ciencias Informáticas, 8(3), 41– 52. Recuperado de http://scielo.sld.cu/pdf/rcci/v8n3/rcci04314.pdf
Universidad de Catalunya. (2010). Diseño y Aplicación de un Sistema de Información para Ejecutivos (Vol. 01).
Van Delden, H. (2009). Integration of socio-economic and bio-physical models to support sustainable development. 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation: Interfacing Modelling and Simulation with Mathematical and Computational Sciences, Proceedings, (July), 2457–2463.
Zeman, K. R., & Rodríguez, L. F. (2019). Quantifying farmer decision-making in an agent-based model. 2019 ASABE Annual International Meeting.
Zhang, W., & Huang, B. (2015). Soil erosion evaluation in a rapidly urbanizing city (Shenzhen, China) and implementation of spatial land-use optimization. Environmental Science and Pollution Research, 22(6), 4475–4490. https://doi.org/10.1007/s11356-014- 3454-y
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Sánchez Céspedes, Juan Manuel , Espinosa Romero, Ana Patricia, Rodríguez Miranda , Juan Pablo (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
