Abstract
The design and implementation of an inference method to schedule irrigation tasks in agriculture, based on the concept of rational agent is explained in this paper. Through the use of a raspberry-pi and xbee devices, the agent interacts with its environment acquiring soil moisture, soil temperature, luminosity, air temperature and rain data. Membership functions and a Mamdani inference methodology were implemented on the raspberry-pi to define irrigation time. Furthermore solenoid valves were used as actuators to apply the amount of water prescribed on the field. The inference system uses luminosity and ambient temperature data to define periods with high levels of evapotranspiration and soil moisture sensors to determine the Volumetric Water Content (VWC). As result of this implementation, temperature membership functions were defined, including the annual averages of minimum (9 \(^\circ \)C) and maximum (20 \(^\circ \)C) temperatures, according with weather data obtained in Nobsa, Boyacá, Colombia. Values between 0 and 2500 lx, were defined in the universe of discourse of brightness and soil moisture membership was defined using field capacity and permanent wilting points. The system developed allows to maintain the VWC in the soil near to the field capacity value, wherewith, soil moisture is maintained at the optimum level necessary for the correct development of a crop.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bendre, M., Thool, R., Thool, V.: Big data in precision agriculture: weather forecasting for future farming. In: 2015 1st International Conference on Proceedings of the Next Generation Computing Technologies (NGCT), pp. 744–750. IEEE (2015)
Mrinmayi, G., Bhagyashri, D., Atul, V.: A smart irrigation system for agriculture based on wireless sensors. Int. J. Innov. Res. Sci. Eng. Technol. 5, 6893–6899 (2016)
Döll, P.: Impact of climate change and variability on irrigation requirements: a global perspective. Clim. Change 54(3), 269–293 (2002)
Zhemukhov, R.S., Zhemukhova, M.M.: System of mathematical models to manage water and land resources at the regional level in case of anthropogenous climate changes taking into account economic indicators and ecological consequences. In: IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS), pp. 256–261. IEEE (2016)
Riediger, J., Breckling, B., Svoboda, N., Schröder, W.: Modelling regional variability of irrigation requirements due to climate change in Northern Germany. Sci. Total. Environ. 541, 329–340 (2016)
Isern, D., Abelló, S., Moreno, A.: Development of a multi-agent system simulation platform for irrigation scheduling with case studies for garden irrigation. Comput. Electron. Agric. 87, 1–13 (2012)
Smith, R.: Review of precision irrigation technologies and their applications. University of Southern Queensland, Technical report (2011)
Nautiyal, M., Grabow, G.L., Miller, G.L., Human, R.L.: Evaluation of two smart irrigation technologies in Cary, North Carolina. In: Proceedings of the Conference: 2010 Pittsburgh, Pennsylvania, 20 June–23 June, 2010, vol. 1. American Society of Agricultural and Biological Engineers (2010)
Lehnert, M.: Factors aecting soil temperature as limits of spatial interpretation and simulation of soil temperature. Acta Universitatis Palackianae Olomucensis–Geographica 45(1), 5–21 (2014)
Datta, S., Taghvaeian, S., Stivers, J.: Understanding soil water content and thresholds for irrigation management. Oklahoma Cooperative Extension Service BAE-1537. Division of Agricultural Sciences and Natural Resources, Oklahoma State University (2017)
Mweso, E., de Bruin, S.: Evaluating the importance of soil moisture availability, as a land quality, on selected rainfed crops in Serowe area, Botswana. ITC (2003)
Mihailovic, B., Cvijanovic, D., Milojevic, I., Filipovic, M.: The role of irrigation in development of agriculture in srem district1. Ekonomika Poljoprivrede 61(4), 989 (2014)
Chavan, C., Karande, P.: Wireless monitoring of soil moisture, temperature & humidity using zigbee in agriculture. Int. J. Eng. Trends 11(10), 493–497 (2014)
Touati, F., Al-Hitmi, M., Benhmed, K., Tabish, R.: fuzzy logic based irrigation system enhanced with wireless data logging applied to the state of Qatar. Comput. Electron. Agric. 98, 233–241 (2013)
Sui, R., Fisher, D.K., Barnes, E.M.: Soil moisture and plant canopy temperature sensing for irrigation application in cotton. J. Agric. Sci. 4(12), 93 (2012)
Majone, B., Viani, F., Filippi, E., Bellin, A., Massa, A., Toller, G., Robol, F., Salucci, M.: Wireless sensor network deployment for monitoring soil moisture dynamics at the field scale. Procedia Environ. Sci. 19, 426–435 (2013)
Terzis, A., Musaloiu-E, R., Cogan, J., Szlavecz, K., Szalay, A., Gray, J., Ozer, S., Liang, C.J., Gupchup, J., Burns, R.: Wireless sensor networks for soil science. Int. J. Sensor Netw. 7(1–2), 53–70 (2010)
Grashey-Jansen, S.: Optimizing irrigation efficiency through the consideration of soil hydrological properties–examples and simulation approaches. Erdkunde, pp. 33–48 (2014)
Hendrawan, Y., Murase, H.: Neural-intelligent water drops algorithm to select relevant textural features for developing precision irrigation system using machine vision. Comput. Electron. Agric. 77(2), 214–228 (2011)
Merot, A., Bergez, J.E.: Irrigate: a dynamic integrated model combining a knowledge-based model and mechanistic biophysical models for border irrigation management. Environ. Model. Softw. 25(4), 421–432 (2010)
Rodriguez-Ortega, W., Martinez, V., Rivero, R., Camara-Zapata, J., Mestre, T., Garcia-Sanchez, F.: Use of a smart irrigation system to study the eects of irrigation management on the agronomic and physiological responses of tomato plants grown under different temperatures regimes. Agric. Water Manag. 183, 158–168 (2017)
Chen, Z., Liu, G.: Application of artificial intelligence technology in water resources planning of river basin. In: 2010 International Conference of Information Science and Management Engineering (ISME), vol. 1, pp. 322–325. IEEE (2010)
Bustos, J., Ricardo, J.: Inteligencia artificial en el sector agropecuario. Seminario de Investigación I. Universidad Nacional de Colombia, Colombia (2005)
Kaur, K.: Machine learning: applications in Indian agriculture. Int. J. Adv. Res. Comput. Commun. Eng. 5(4), 342–344 (2016). https://doi.org/10.17148/IJARCCE.2016.5487
Rafea, A., Hassen, H., Hazman, M.: Automatic knowledge acquisition tool for irrigation and fertilization expert systems. Expert Syst. Appl. 24(1), 49–57 (2003)
Zhang, Q., Wu, C.H., Tilt, K.: Application of fuzzy logic in an irrigation control system. In: Proceedings of the IEEE International Conference on Industrial Technology, ICIT 1996, pp. 593–597. IEEE (1996)
Zanetti, S., Sousa, E., Oliveira, V., Almeida, F., Bernardo, S.: Estimating evapotranspiration using artificial neural network and minimum climatological data. J. Irrig. Drain. Eng. 133(2), 83–89 (2007)
Li, X., Yeh, A.: Multitemporal SAR images for monitoring cultivation systems using case-based reasoning. Remote. Sens. Environ. 90(4), 524–534 (2004)
Fedra, K.: Models, GIS, and expert systems: integrated water resources models. In: Proceedings of the International Conference on Applications of Geographic Information Systems in Hydrology and Water Resources Management, Vienna, vol. 211, pp. 297–308. IAHS Press (1994)
Castelletti, A., Soncini-Sessa, R.: Bayesian networks and participatory modelling in water resource management. Environ. Model. Softw. 22(8), 1075–1088 (2007)
Dessalegne, T., Nicklow, J.W.: Artificial life algorithm for management of multi-reservoir river systems. Water Resour. Manag. 26(5), 1125–1141 (2012)
Ranjithan, S.R.: Role of evolutionary computation in environmental and water resources systems analysis (2005)
Wardlaw, R., Bhaktikul, K.: Application of genetic algorithms for irrigation water scheduling. Irrig. Drain. 53(4), 397–414 (2004)
Reddy, M.J., Kumar, D.N.: Evolving strategies for crop planning and operation of irrigation reservoir system using multi-objective differential evolution. Irrig. Sci. 26(2), 177–190 (2008)
Pant, M., Thangaraj, R., Rani, D., Abraham, A., Srivastava, D.K.: Estimation of optimal crop plan using nature inspired metaheuristics. World J. Model. Simul. 6(2), 97–109 (2010)
Khan, M.A., Islam, M.Z., Hafeez, M.: Evaluating the performance of several data mining methods for predicting irrigation water requirement. In: Proceedings of the Tenth Australasian Data Mining Conference, vol. 134, pp. 199–207. Australian Computer Society, Inc. (2012)
Le Bars, M., Attonaty, J.M., Pinson, S.: An agent-based simulation for water sharing between different users. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, pp. 211–212. ACM (2002)
Romero, R., Muriel, J., García, I., de la Peña, D.M.: Research on automatic irrigation control: state of the art and recent results. Agric. Water Manag. 114, 59–66 (2012)
Boutraa, T., Akhkha, A., Alshuaibi, A., Atta, R.: Evaluation of the effectiveness of an automated irrigation system using wheat crops. Agric. Biol. J. N. Am. 2(1), 80–88 (2011)
O’Shaughnessy, S., Evett, S.R.: Canopy temperature based system effectively schedules and controls center pivot irrigation of cotton. Agric. Water Manag. 97(9), 1310–1316 (2010)
Romero, R., Muriel, J., Garcia, I.: Automatic irrigation system in almonds and walnuts trees based on sap flow measurements. In: VII International Workshop on Sap Flow, vol. 846, pp. 135–142 (2008)
Vicente, R.R.: Hydraulic modelling and control of the soil-plant-atmosphere continuum in woody crops. (doctoral dissertation). (2011). http://hdl.handle.net/10261/96715
Xiang, X.: Design of fuzzy drip irrigation control system based on zigbee wireless sensor network. In: International Conference on Computer and Computing Technologies in Agriculture, pp. 495–501. Springer (2010)
Salazar, R., Rangel, J.C., Pinzón, C., Rodríguez, A.: Irrigation system through intelligent agents implemented with arduino technology. Adv. Distrib. Comput. Artif. Intell. J. (ADCAIJ) 2(3), 29–36 (2013)
Benayache, Z., Besançon, G., Georges, D.: A new nonlinear control methodology for irrigation canals based on a delayed input model. In: IFAC Proceedings Volumes, vol. 41, no. 2, pp. 2544–2549 (2008)
Capraro, F., Patino, D., Tosetti, S., Schugurensky, C.: Neural network-based irrigation control for precision agriculture. In: IEEE International Conference on Networking, Sensing and Control, ICNSC 2008, pp. 357–362. IEEE (2008)
Tsang, S., Jim, C.: Applying artificial intelligence modeling to optimize green roof irrigation. Energy Build. 127, 360–369 (2016)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education Limited, Malaysia (2016)
Warner, J., Sexauer, J., scikit-fuzzy, twmeggs, Unnikrishnan, A., Castelõ, G., Batista, F., Badger, T.G., Mishra, H.: Jdwarner/scikit-fuzzy: Scikit-fuzzy 0.3.1 (2017). https://doi.org/10.5281/zenodo.1002946
Osroosh, Y., Peters, R.T., Campbell, C.S., Zhang, Q.: Comparison of irrigation automation algorithms for drip-irrigated apple trees. Comput. Electron. Agric. 128, 87–99 (2016)
Acknowledgment
We would like to thank the Research and Extension Department of Universidad Nacional de Colombia, Bogotá, for funding the institutional project entitled: Modeling Based on Agents for Precision Agriculture Applications in the National Call for Projects for research strengthening, Creation and Innovation of Universidad Nacional de Colombia 2016–2018. Pedro-F Cardenas expresses its gratitude to Colciencias for the doctorate study abroad scholarship 2007. Andres-F Jimenez expresses its gratitude to Crop, Soil and Environmental Sciences Department, Precision Agriculture, Auburn, Alabama, the Boyacá government for the doctorate scholarship - 2015 and also to the Universidad de los Llanos. We express our gratitude to the engineer Angel Rafael López Corredor.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jimenez, A.F., Herrera, E.F., Ortiz, B.V., Ruiz, A., Cardenas, P.F. (2019). Inference System for Irrigation Scheduling with an Intelligent Agent. In: Corrales, J., Angelov, P., Iglesias, J. (eds) Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change II. AACC 2018. Advances in Intelligent Systems and Computing, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-030-04447-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-04447-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04446-6
Online ISBN: 978-3-030-04447-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)