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


  • 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.

publication date

  • January 1, 2018