A Decision Support System for Irrigation Management: Analysis and Implementation of Different Learning Techniques

Automatic irrigation scheduling systems are highly demanded in the agricultural sector due to their ability to both save water and manage deficit irrigation strategies. Elaborating a functional and efficient automatic irrigation system is a very complex task due to the high number of factors that the technician considers when managing irrigation in an optimal way. Automatic learning systems propose an alternative to traditional irrigation management by means of the automatic elaboration of predictions based on the learning of an agronomist (DSS). The aim of this paper is the study of several learning techniques in order to determine the goodness and error relative to expert decision. Nine orchards were tested during 2018 using linear regression (LR), random forest regression (RFR), and support vector regression (SVR) methods as engines of the irrigation decision support system (IDSS) proposed. The results obtained by the learning methods in three of these orchards have been compared with the decisions made by the agronomist over an entire year. The prediction model errors determined the best fitting regression model. The results obtained lead to the conclusion that these methods are valid engines to develop automatic irrigation scheduling systems.

[1]  Hamlyn G. Jones,et al.  Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling , 1999 .

[2]  Jessica K. Hodgins,et al.  Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease , 2019, Electronics.

[3]  Yu Feng,et al.  Evaluation of artificial intelligence models for actual crop evapotranspiration modeling in mulched and non-mulched maize croplands , 2018, Comput. Electron. Agric..

[4]  Alexis Tsoukiàs,et al.  A system dynamics model for supporting decision-makers in irrigation water management. , 2018, Journal of environmental management.

[5]  Michael D. Dukes,et al.  Irrigation scheduling performance by evapotranspiration-based controllers , 2010 .

[6]  Michael D. Dukes,et al.  Landscape irrigation by evapotranspiration-based irrigation controllers under dry conditions in Southwest Florida. , 2009 .

[7]  Yanjun Shen,et al.  Web-based irrigation decision support system with limited inputs for farmers , 2018, Agricultural Water Management.

[8]  H. Navarro-Hellín,et al.  A wireless sensors architecture for efficient irrigation water management , 2015 .

[9]  Manuel Jiménez Buendía,et al.  Design and Calibration of a Low-Cost SDI-12 Soil Moisture Sensor , 2019, Sensors.

[10]  Ozgur Kisi,et al.  Modeling reference evapotranspiration using three different heuristic regression approaches , 2016 .

[11]  L. Bacci,et al.  An integrated method for irrigation scheduling of potted plants , 2008 .

[12]  Ningbo Cui,et al.  Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. , 2017 .

[13]  Konstantinos X. Soulis,et al.  Performance evaluation of a recently developed soil water content, dielectric permittivity, and bulk electrical conductivity electromagnetic sensor , 2019, Agricultural Water Management.

[14]  Darko Pevec,et al.  AgroDSS: A decision support system for agriculture and farming , 2019, Comput. Electron. Agric..

[15]  GaniAbdullah,et al.  The rise of "big data" on cloud computing , 2015 .

[16]  Gilad Ravid,et al.  Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge , 2017, Precision Agriculture.

[17]  A. Thakur,et al.  Automatic drip irrigation scheduling effects on yield and water productivity of banana , 2019, Scientia Horticulturae.

[18]  S. Idso,et al.  Canopy temperature as a crop water stress indicator , 1981 .

[19]  J. Alarcón,et al.  Strategies for drought resistance in leaves of two almond cultivars , 1996 .

[20]  Rafael Domingo,et al.  Remote management of deficit irrigation in almond trees based on maximum daily trunk shrinkage. Water relations and yield , 2013 .

[21]  Roque Torres-Sánchez,et al.  Soil and plant water indicators for deficit irrigation management of field-grown sweet cherry trees , 2018, Agricultural Water Management.

[22]  H. Khachatryan,et al.  Investigating Homeowners’ Preferences for Smart Irrigation Technology Features , 2019, Water.

[23]  Jesús Martínez del Rincón,et al.  A decision support system for managing irrigation in agriculture , 2016, Comput. Electron. Agric..

[24]  Qin Zhang,et al.  Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold , 2015, Comput. Electron. Agric..

[25]  Michael D. Dukes,et al.  Field Comparison of Tensiometer and Granular Matrix Sensor Automatic Drip Irrigation on Tomato , 2005 .

[26]  Stefano Marsili-Libelli,et al.  A Fuzzy Decision Support System for irrigation and water conservation in agriculture , 2015, Environ. Model. Softw..

[27]  A. Torrecillas,et al.  Water relations, growth and yield of Fino lemon trees under regulated deficit irrigation , 1996, Irrigation Science.

[28]  Miguel Ángel Porta-Gándara,et al.  Automated Irrigation System Using a Wireless Sensor Network and GPRS Module , 2014, IEEE Transactions on Instrumentation and Measurement.

[29]  Jordi Marsal,et al.  A general algorithm for automated scheduling of drip irrigation in tree crops , 2012 .

[30]  Joe D. Luck,et al.  Soil water content monitoring for irrigation management:A geostatistical analysis , 2017 .

[31]  David C. Rose,et al.  Decision support tools for agriculture: Towards effective design and delivery , 2016 .

[32]  C. Rama Krishna,et al.  An IoT based smart irrigation management system using Machine learning and open source technologies , 2018, Computers and Electronics in Agriculture.

[33]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[34]  José Manuel Moreno,et al.  Assessing the Crop-Water Status in Almond (Prunus dulcis Mill.) Trees via Thermal Imaging Camera Connected to Smartphone , 2018, Sensors.

[35]  María Romero,et al.  Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management , 2018, Comput. Electron. Agric..

[36]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[37]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[38]  Rafaela Cáceres,et al.  Adaptation of an Automatic Irrigation-control Tray System for Outdoor Nurseries , 2007 .

[39]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[40]  M. J. Molina,et al.  Laboratory and field assessment of the capacitance sensors Decagon 10HS and 5TE for estimating the water content of irrigated soils , 2014 .

[41]  Jan W. Hopmans,et al.  Frequency, electrical conductivity and temperature analysis of a low-cost capacitance soil moisture sensor , 2008 .

[42]  Neha K. Nawandar,et al.  IoT based low cost and intelligent module for smart irrigation system , 2019, Comput. Electron. Agric..

[43]  Eduardo A. Holzapfel,et al.  Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data , 2019, Water.

[44]  J. E. Campbell,et al.  Dielectric properties and influence of conductivity in soils at one to fifty megahertz , 1990 .

[45]  Haijun Yan,et al.  A real-time fuzzy decision support system for alfalfa irrigation , 2019, Comput. Electron. Agric..

[46]  Subimal Ghosh,et al.  SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output , 2010 .

[47]  Mladen Todorovic,et al.  Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data , 2020 .

[48]  N. K. Tyagi,et al.  Design and development of an auto irrigation system , 1997 .