Irrigation pivot-center connected at low cost for the reduction of crop water requirements

Irrigation, particularly pivot-center, is widely used around the world to fill the need of crop watering. This method of irrigation has a low efficiency compared to other methods of irrigation such as drip systems and generally they use water without consider the real need of plants. In this paper we propose an automation system based on the Internet of Things (IoT), Geographic Information System (GIS) and quasi real-time in the cloud of water requirements to improve the efficiency of water use. Indeed, each segment of the pivot-center moves at a different speed compared to others; thus, must be individually controlled to optimize the yield of irrigation. Moreover, it necessary to integrate factors such as stage of crops' development, heterogeneity of soil, runoff, drainage, soil components, nutrients and moisture content. In this paper we develop a complete system integrating sensors, GIS, Internet of Things and cloud computing. This approach allows to automate fine-grained the consumption of water without decreasing the yield. In addition to that, the collect of data and the soil moisture measurement will allow to adapt coefficient of evapotranspiration to local weather without having to resort to lysimetric measures. The proposed architecture allows to store and treat real-time, time series data and low-priority data such as 3D images used in digital phenotyping field which are treated with batch processing.

[1]  Lei Zhang,et al.  Study on the Detection and Warning System of Rice Disease Based on the GIS and IOT in Jilin Province , 2014, CCTA.

[2]  Partha Pratim Ray,et al.  A survey of IoT cloud platforms , 2016 .

[3]  H. F. Blaney,et al.  Determining Water Requirements in Irrigated Areas From Climatological and Irrigation Data , 2017 .

[4]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[5]  S. Evett,et al.  Crop evapotranspiration calculation using infrared thermometers aboard center pivots , 2017 .

[6]  Z. Osmolski Estimating Potential Evapotranspiration in Arid Environments , 1981 .

[7]  Paul D. Colaizzi,et al.  Assessing Application Uniformity of a Variable Rate Irrigation System in a Windy Location , 2013 .

[8]  R. E. Yoder,et al.  Evaluation Of Methods For Estimating Daily Reference Crop Evapotranspiration At A Site In The Humid Southeast United States , 2005 .

[9]  Kamran Davary,et al.  Deriving data mining and regression based water-salinity production functions for spring wheat (Triticum aestivum) , 2014 .

[10]  H. D. Bruin,et al.  The Priestley-Taylor Evaporation Model Applied to a Large, Shallow Lake in the Netherlands , 1979 .

[11]  M Smith,et al.  [CROPWAT: a computer program for irrigation planning and management]. [Spanish] , 1992 .

[12]  Thomas Bartzanas,et al.  Internet of Things in agriculture, recent advances and future challenges , 2017 .

[13]  Bin Chen,et al.  A precision agriculture management system based on Internet of Things and WebGIS , 2013, 2013 21st International Conference on Geoinformatics.

[14]  A. A. Alazba,et al.  Simulation of water distribution under surface dripper using artificial neural networks , 2017, Comput. Electron. Agric..

[15]  Olivier Debauche,et al.  Web-based cattle behavior service for researchers based on the smartphone inertial central , 2017, FNC/MobiSPC.

[16]  Caige Sun,et al.  The Application and Forecast of Geospatial Information Technology in Agriculture Internet of Things , 2012, 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering.

[17]  F. Anctil,et al.  A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada , 2010 .

[18]  R. Allen,et al.  Evapotranspiration and Irrigation Water Requirements , 1990 .

[19]  Sidi Ahmed Mahmoudi,et al.  Cloud architecture for digital phenotyping and automation , 2017, 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech).

[20]  Paul D. Ayers,et al.  Studying uniform and variable rate center pivot irrigation strategies with the aid of site-specific water production functions , 2016, Comput. Electron. Agric..

[21]  R. López-Urrea,et al.  Testing evapotranspiration equations using lysimeter observations in a semiarid climate , 2006 .

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

[23]  Xiaoyin Liu,et al.  Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement , 2017 .

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

[25]  Sidi Ahmed Mahmoudi,et al.  Multi-CPU/Multi-GPU Based Framework for Multimedia Processing , 2015, CIIA.

[26]  H. R. Haise,et al.  Estimating evapotranspiration from solar radiation , 1963 .

[27]  Suat Irmak,et al.  Validation of Valiantzas Reference Evapotranspiration Equation under Different Climatic Conditions , 2017 .

[28]  H. L. Penman,et al.  Vegetation and hydrology , 1963 .

[29]  P. Gavilán,et al.  Reference Evapotranspiration Estimation in a Highly Advective Semiarid Environment , 2005 .

[30]  L. Pan,et al.  Analysis of soil water availability by integrating spatial and temporal sensor-based data , 2013, Precision Agriculture.

[31]  Sidi Ahmed Mahmoudi,et al.  Multi-GPU based event detection and localization using high definition videos , 2014, 2014 International Conference on Multimedia Computing and Systems (ICMCS).

[32]  Olivier Debauche,et al.  Web Monitoring of Bee Health for Researchers and Beekeepers Based on the Internet of Things , 2018, ANT/SEIT.

[33]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[34]  Enrique Playán,et al.  Effect of the start–stop cycle of center-pivot towers on irrigation performance: Experiments and simulations , 2015 .

[35]  Olivier Debauche,et al.  Climwat 2.0 & Cropwat 8.0 , 2012 .

[36]  Zailin Huo,et al.  Simulation for response of crop yield to soil moisture and salinity with artificial neural network , 2011 .

[37]  Suat Irmak,et al.  Autonomous precision agriculture through integration of wireless underground sensor networks with center pivot irrigation systems , 2013, Ad Hoc Networks.