Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge

Jojoba Israel is a world-leading producer of Jojoba products, whose orchards are covered with sensors that collect soil moisture data for monitoring plant needs at real-time. Based on these data, the company’s agronomist defines a weekly irrigation plan. In addition, data on weather, irrigation, and yield are recorded from other sources (e.g. meteorological station and irrigation-plan records). However, so far, there has been no attempt to use the entire set of collected data to reveal insights and interesting relationships between different variables, such as soil, weather, irrigation characteristics, and resulting yield. By integrating and utilizing data from different sources, our research aims at using the collected data not only for monitoring and controlling the crop, but also for predicting irrigation recommendations. In particular, a dataset was constructed by integrating data collected over almost two years from 22 soil-sensors spread in four major plots (which are divided into 28 subplots and eight irrigation groups), from a meteorological station, and from actual irrigation records. Different regression and classification algorithms were applied on this dataset to develop models that were able to predict the weekly irrigation plan as recommended by the agronomist. The models were developed using eight different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Gradient Boosted Regression Trees, with 93% accuracy, and the best classification model was the Boosted Tree Classifier, with 95% accuracy (on the test-set). Data that were not contributing to the model prediction success rate were identified as well. The resulting model can significantly facilitate the agronomist’s irrigation planning process. In addition, the potential of applying machine learning on the company data for yield and disease prediction is discussed.

[1]  Bradley Lubben,et al.  Precision Agriculture Usage and Big Agriculture Data , 2015 .

[2]  D. Bochtis,et al.  Conceptual model of a future farm management information system , 2010 .

[3]  Haisheng Li,et al.  Bike-Sharing Prediction System , 2016, Edutainment.

[4]  Miguel Damas,et al.  HidroBus® system: fieldbus for integrated management of extensive areas of irrigated land , 2001, Microprocess. Microsystems.

[5]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[6]  P. Ruelle,et al.  Modelling irrigation scheduling to analyse water management at farm level, during water shortages , 2000 .

[7]  David Mease,et al.  Boosted Classification Trees and Class Probability/Quantile Estimation , 2007, J. Mach. Learn. Res..

[8]  Mac McKee,et al.  Recursive partitioning techniques for modeling irrigation behavior , 2013, Environ. Model. Softw..

[9]  Fabio Rodrigues de Miranda A Site-Specific Irrigation Control System , 2003 .

[10]  Ian J. Yule,et al.  Soil water status and water table depth modelling using electromagnetic surveys for precision irrigation scheduling , 2013 .

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

[12]  Allan Leck Jensen,et al.  Pl@nteInfo® : a web-based system for personalised decision support in crop management , 2000 .

[13]  C. M. Stripling,et al.  A Dynamic Variable Rate Irrigation Control System , 2017 .

[14]  H. Zou The Adaptive Lasso and Its Oracle Properties , 2006 .

[15]  Linda Lilburne,et al.  A prototype DSS to evaluate irrigation management plans , 1998 .

[16]  Iver Thysen,et al.  Online decision support for irrigation for farmers , 2006 .

[17]  Yunseop Kim,et al.  Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network , 2008, IEEE Transactions on Instrumentation and Measurement.

[18]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[19]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[20]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[21]  N. Zhang,et al.  Precision agriculture—a worldwide overview , 2002 .

[22]  Haruhiko Murase,et al.  A NEURAL NETWORK ESTIMATION TECHNIQUE FOR PLANT WATER STATUS USING THE TEXTURAL FEATURES OF PICTORIAL DATA OF PLANT CANOPY , 1995 .

[23]  Wei Peng,et al.  Machine learning approaches for soil classification in a multi-agent deficit irrigation control system , 2009, 2009 IEEE International Conference on Industrial Technology.

[24]  Calvin D. Perry,et al.  A soil moisture sensor-based variable rate irrigation scheduling system , 2013 .

[25]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[26]  Luciano Mateos,et al.  SIMIS: the FAO decision support system for irrigation scheme management , 2002 .

[27]  Yang Liu,et al.  The Research of Precision Irrigation Decision Support System Based on Genetic Algorithm , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[28]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .