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A Machine Learning Approach for a Robust Irrigation Prediction via Regression and Feature Selection

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Advanced Information Networking and Applications (AINA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 449))


Smart irrigation has many advantages in optimizing resource usage (e.g., saving water, reducing energy consumption) and improving crop productivity. In this paper, we contribute to this field by proposing a robust and accurate machine learning-based approach that integrates feature selection techniques with several regression algorithms. To effectively determine the optimal quantity of water needed for a plant, Random Forest, Recursive Feature Elimination (RFE), and SelectKBest are used to assess the importance of the features. Different regression methods are established based on the set of effective features. The different models involved in this approach are trained and tested using a collected dataset about various crops such as tomatoes, grapes, and lemon and encompasses different features such as meteorological data, soil data, irrigation data, and crop data. The experiments demonstrated the performance of RF in analyzing the feature importance as well as the prediction of the optimal water quantity. The findings of feature selection highlight the importance level of the evapotranspiration, the depletion, and the deficit to maximize the model’s accuracy.

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The work is carried out in the frame of the PRECIMED project that is funded under the PRIMA Programme. PRIMA is an Art.185 initiative supported and co-funded under Horizon 2020, the European Union’s Programme for Research and Innovation. (project application number: 155331/I4/19.09.18).

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Correspondence to Emna Ben Abdallah .

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Abdallah, E.B., Grati, R., Fredj, M., Boukadi, K. (2022). A Machine Learning Approach for a Robust Irrigation Prediction via Regression and Feature Selection. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 449. Springer, Cham.

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