A novel Observer-based Architecture for Water Management in Large-Scale (Hazelnut) Orchards

Abstract Water management is an important aspect in modern agriculture. Irrigation systems are becoming more and more complex, trying to minimize the water consumption while ensuring the necessities of the plants. A fundamental requirement to define efficient irrigation policies is to be able to estimate the water status of the plants and of the soil. In this context, precision agriculture addresses this problem by using the latest technological advancements. In particular, most of the works in the literature aim to develop highly accurate estimations under the assumption of the availability of a dense network of sensors. Although this assumption may be adequate for intensive farming (e.g. greenhouses), it becomes quite unrealistic in the context of large-scale scenarios. In this work, we propose a novel observer-based architecture for the water management of large-scale (hazelnut) orchards which relies on a network of sparsely deployed soil moisture sensors along with a weather station and on remote sensing measurements carried out by drones with a pre-defined periodicity. The contribution is twofold: i) First a novel model of the water dynamics in an hazelnut orchard is proposed, which includes the water dynamics in the soil and in the plants, and ii) then, on the basis of this model and of the available measurements, the use of a Kalman filter with intermittent observations is proposed, taking also into account the availability of the weather station measurements. The effectiveness of the proposed solution is validated through simulation.

[1]  Thomas Udelhoven,et al.  Water stress detection in potato plants using leaf temperature, emissivity, and reflectance , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Zhifang Zhou,et al.  Dynamics of Soil Water Evaporation during Soil Drying: Laboratory Experiment and Numerical Analysis , 2013, TheScientificWorldJournal.

[3]  Renata Wachowiak-Smolíkova,et al.  Visual analytics and remote sensing imagery to support community-based research for precision agriculture in emerging areas , 2017, Comput. Electron. Agric..

[4]  Roberto Confalonieri,et al.  Development and evaluation of new modelling solutions to simulate hazelnut (Corylus avellana L.) growth and development , 2016 .

[5]  Tahar Boutraa,et al.  Evaluation of the effectiveness of an automated irrigation system using wheat crops , 2011 .

[6]  Kathy Steppe,et al.  A step towards new irrigation scheduling strategies using plant-based measurements and mathematical modelling , 2008, Irrigation Science.

[7]  Manuel Bustillos,et al.  Model Predictive Control for Closed-Loop Irrigation , 2014 .

[8]  Abd Ali Naseri,et al.  A satellite based crop water stress index for irrigation scheduling in sugarcane fields , 2017 .

[9]  Marco Bittelli,et al.  Measuring Soil Water Content: A Review , 2011 .

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

[11]  M. Srbinovska,et al.  Environmental parameters monitoring in precision agriculture using wireless sensor networks , 2015 .

[12]  M. S. Goodchild,et al.  A Method for Precision Closed-loop Irrigation Using a Modified PID Control Algorithm , 2015 .

[13]  Selçuk Özmen Quantification of Leaf Water Potential, Stomatal Conductance and Photosynthetically Active Radiation in Rainfed Hazelnut , 2016, Erwerbs-Obstbau.

[14]  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..

[15]  M. Bethune,et al.  Understanding and predicting deep percolation under surface irrigation , 2008 .

[16]  Bruno Sinopoli,et al.  Kalman filtering with intermittent observations , 2004, IEEE Transactions on Automatic Control.

[17]  Ruhollah Taghizadeh-Mehrjardi,et al.  Estimation of infiltration rate and deep percolation water using feed-forward neural networks in Gorgan Province , 2014 .

[18]  Basil Baldwin,et al.  VARIATIONS IN FLOWERING, GROWTH AND YIELD OF HAZELNUT CULTIVARS AND GROWERS’ SELECTIONS IN AUSTRALIA , 2001 .

[19]  Shakib Shahidian,et al.  Irrigation management with remote sensing: Evaluating irrigation requirement for maize under Mediterranean climate condition , 2017 .

[20]  Andrea J. Goldsmith,et al.  LQG Control for MIMO Systems Over Multiple Erasure Channels With Perfect Acknowledgment , 2012, IEEE Transactions on Automatic Control.

[21]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[22]  Manuel Perez-Ruiz,et al.  Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards , 2017 .

[23]  H. Jones Irrigation scheduling: advantages and pitfalls of plant-based methods. , 2004, Journal of experimental botany.

[24]  Andrew K. Skidmore,et al.  Changes in thermal infrared spectra of plants caused by temperature and water stress : powerpoint , 2015 .

[25]  Dilini Delgoda,et al.  Model Predictive Control for Real-Time Irrigation Scheduling , 2013 .

[26]  Xinjian Xiang Design of Fuzzy Drip Irrigation Control System Based on ZigBee Wireless Sensor Network , 2010, CCTA.

[27]  Daniel A. Keim,et al.  Visual Analytics , 2009, Encyclopedia of Database Systems.

[28]  L. Quebrajo,et al.  Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet , 2018 .

[29]  Cristos Xiloyannis,et al.  Irrigation in Mediterranean Fruit Tree Orchards , 2012 .

[30]  Theofanis Gemtos,et al.  Precision Agriculture Application in Fruit Crops: Experience in Handpicked Fruits , 2013 .

[31]  J. Tous,et al.  CULTURAL PRACTICES AND COSTS IN HAZELNUT PRODUCTION , 1994 .

[32]  L. G. Santesteban,et al.  High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard , 2017 .

[33]  Kathy Steppe,et al.  Stored water use and transpiration in Scots pine: a modeling analysis with ANAFORE. , 2007, Tree physiology.

[34]  C. Rosenzweig,et al.  Improved Ground Hydrology Calculations for Global Climate Models (GCMs): Soil Water Movement and Evapotranspiration , 1988 .