Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue.

[1]  W. Rawls,et al.  Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions , 2006 .

[2]  Mohammed Erritali,et al.  A comparative study of decision tree ID3 and C4.5 , 2014 .

[3]  Shahaboddin Shamshirband,et al.  Application of ANNs, ANFIS and RSM to estimating and optimizing the parameters that affect the yield and cost of biodiesel production , 2018 .

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

[5]  Determining evapotranspiration in an olive orchard in southwest Spain , 2012 .

[6]  Christopher M. U. Neale,et al.  AquaCrop-OS: An open source version of FAO’s crop water productivity model , 2017 .

[7]  Sheldon M. Ross.  Introduction to probability and statistics for engineers and scientists (2. ed.) , 1988 .

[8]  Jing-Shing Yao,et al.  Fuzzy decision making for medical diagnosis based on fuzzy number and compositional rule of inference , 2001, Fuzzy Sets Syst..

[9]  G. Fischer.  Transforming the global food system , 2018, Nature.

[10]  Ioannis K. Tsanis,et al.  Severe climate-induced water shortage and extremes in Crete , 2011 .

[11]  Ali Sadollah,et al.  Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System? , 2018, Fuzzy Logic Based in Optimization Methods and Control Systems and its Applications.

[12]  Ayca Altay,et al.  Fuzzy Decision Trees , 2016 .

[13]  Jon Atli Benediktsson,et al.  Automatic selection of molecular descriptors using random forest: Application to drug discovery , 2017, Expert Syst. Appl..

[14]  E. Stefanoudaki,et al.  Effect of drought stress on qualitative characteristics of olive oil of cv Koroneiki , 2001 .

[15]  F. Moreno,et al.  New approach for olive trees irrigation scheduling using trunk diameter sensors , 2010 .

[16]  Ioannis K. Tsanis,et al.  Temporal and spatial variation of annual rainfall on the island of Crete, Greece , 2003 .

[17]  Daniel Sánchez,et al.  Building a fuzzy logic information network and a decision-support system for olive cultivation in Andalusia. , 2008 .

[18]  Kwok-Wing Chau,et al.  A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources , 2019, IEEE Access.

[19]  Luis S. Pereira,et al.  Estimating crop coefficients from fraction of ground cover and height , 2009, Irrigation Science.

[20]  John W. Labadie,et al.  Computerized Decision Support Systems for Water Managers , 1986 .

[21]  D. Raes,et al.  AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles , 2009 .

[22]  Dirk Raes,et al.  Seasonal water production functions and yield response factors for maize and onion in Perkerra, Kenya , 2002 .

[23]  Md. Jalil Piran,et al.  Survey of computational intelligence as basis to big flood management: challenges, research directions and future work , 2018 .

[24]  Z. Huo,et al.  A decision-support system for cropland irrigation water management and agricultural non-point sources pollution control , 2014 .

[25]  Nicholas J. Car,et al.  USING decision models to enable better irrigation Decision Support Systems , 2018, Comput. Electron. Agric..

[26]  K. Chartzoulakis.  THE USE OF SALINE WATER FOR IRRIGATION OF OLIVES: EFFECTS ON GROWTH, PHYSIOLOGY, YIELD AND OIL QUALITY , 2011 .

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

[28]  Richard G. Allen,et al.  Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. , 2000 .

[29]  K. Mahmood,et al.  Comparative water-use efficiency of Sporobolus arabicus and Leptochloa fusca and its relation with carbon-isotope discrimination under semi-arid conditions , 2003, Plant and Soil.

[30]  L. S. Pereira,et al.  A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman-Monteith method , 2006 .

[31]  Jason H T Bates,et al.  Applying fuzzy logic to medical decision making in the intensive care unit. , 2003, American journal of respiratory and critical care medicine.

[32]  Adriana Bruggeman,et al.  Water pricing and irrigation across Europe: opportunities and constraints for adopting irrigation scheduling decision support systems , 2016 .

[33]  Thierry Marchant,et al.  Conditional expected utility criteria for decision making under ignorance or objective ambiguity , 2018, Journal of Mathematical Economics.

[34]  Luis S. Pereira,et al.  Model Validation, Crop Coefficients and Yield Response Factors for Maize Irrigation Scheduling based on Long-term Experiments , 2006 .

[35]  C. Parra-López,et al.  Farm-level multifunctionality associated with farming techniques in olive growing: An integrated modeling approach , 2014 .

[36]  I. Daliakopoulos,et al.  Tree Crown Detection on Multispectral VHR Satellite Imagery , 2009 .

[37]  Harun Artuner,et al.  Application of Decision Tree Algorithm for classification and identification of natural minerals using SEM-EDS , 2015, Comput. Geosci..

[38]  Roberto Genovesi,et al.  IRRINET: Large Scale DSS Application for On-farm Irrigation Scheduling , 2013 .

[39]  M. H. Ali,et al.  Increasing water productivity in crop production : A synthesis , 2008 .

[40]  Ahmet Istanbulluoglu,et al.  Effects of irrigation regimes on yield and water productivity of safflower (Carthamus tinctorius L.) under Mediterranean climatic conditions , 2009 .

[41]  I. Daliakopoulos,et al.  A Review of Soil-Improving Cropping Systems for Soil Salinization , 2019, Agronomy.

[42]  Antonio Díaz-Espejo,et al.  Soil moisture dynamics in a hedgerow olive orchard under well-watered and deficit irrigation regimes: Assessment, prediction and scenario analysis , 2016 .

[43]  Michele Perniola,et al.  Yield response factor to water (Ky) and water use efficiency of Carthamus tinctorius L. and Solanum melongena L , 2007 .

[44]  U. Sahin,et al.  DETERMINING WATER-YIELD RELATIONSHIP, WATER USE EFFICIENCY, SEASONAL CROP AND PAN COEFFICIENTS FOR ALFALFA IN A SEMIARID REGION WITH HIGH ALTITUDE , 2010 .

[45]  Stefan Siebert,et al.  Improvements in crop water productivity increase water sustainability and food security—a global analysis , 2013 .

[46]  Lei Xu,et al.  Yin-Yang firefly algorithm based on dimensionally Cauchy mutation , 2020, Expert Syst. Appl..

[47]  Luis S. Pereira,et al.  Performance assessment of the FAO AquaCrop model for soil water, soil evaporation, biomass and yield of soybeans in North China Plain , 2015 .

[48]  D. Jacob,et al.  Cross sectoral impacts on water availability at +2 °C and +3 °C for east Mediterranean island states: The case of Crete , 2016 .

[49]  Ioannis K. Tsanis,et al.  The effect of spatially distributed meteorological parameters on irrigation water demand assessment , 2003 .

[50]  José L. Chávez,et al.  Crop Evapotranspiration (ET) Estimation Models: A Review and Discussion of the Applicability and Limitations of ET Methods , 2015 .

[51]  M. Sánchez,et al.  Irrigation decision support based on leaf relative water content determination in olive grove using near infrared spectroscopy , 2019, Biosystems Engineering.

[52]  S. Keesstra,et al.  Soil Water Conservation: Dynamics and Impact , 2018, Water.

[53]  Michele Rinaldi,et al.  Chapter Six – Decision Support Systems to Manage Irrigation in Agriculture * , 2014 .

[54]  A. Koutroulis,et al.  Spatiotemporal Characteristics of Meteorological Drought for the Island of Crete , 2011 .

[55]  Jirka Šimůnek,et al.  Seasonal simulation of water, salinity and nitrate dynamics under drip irrigated mandarin (Citrus reticulata) and assessing management options for drainage and nitrate leaching , 2014 .

[56]  I. Daliakopoulos,et al.  Yield Response of Mediterranean Rangelands under a Changing Climate , 2017 .

[57]  B. McNeil,et al.  Decision analysis for public health: principles and illustrations. , 1984, Annual review of public health.

[58]  Benoît Duchemin,et al.  Improvement of FAO-56 method for olive orchards through sequential assimilation of thermal infrared-based estimates of ET , 2008 .

[59]  Sandro Bimonte,et al.  A new decision-support system for the historical analysis of integrated pest management activities on olive crops based on climatic data , 2018, Comput. Electron. Agric..

[60]  Faruk Gul,et al.  EXPECTED UNCERTAIN UTILITY THEORY , 2014 .

[61]  Kwok-wing Chau,et al.  Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids , 2018, Engineering Applications of Computational Fluid Mechanics.