A drip irrigation prediction system in a greenhouse based on long short-term memory and connected objects

: pp. 524–533
Received: February 28, 2023
Accepted: April 27, 2023
Laboratory of Information Technology and Modeling, Hassan II University, Faculty of Sciences Ben M'Sik
Laboratory of Information Technology and Modeling, Hassan II University, Faculty of Sciences Ben M'Sik
Laboratory of Information Technology and Modeling, Hassan II University, Faculty of Sciences Ben M'Sik

Smart greenhouses use Internet of Things (IoT) technology to monitor and control various factors that affect plant growth, such as soil humidity, indoor humidity, soil temperature, rain sensor, illumination, and indoor temperature.  Sensors and actuators connected to an IoT network can collect data on these factors and use it to automate processes such as watering, heating, and ventilation.  This can help optimize growing conditions and improve crop yield.  To enable their vegetative growth and development, plants need the right amount of water at the right time.  The objective of this work is to strictly control the different factors that affect the growth of greenhouse crops.  Therefore, we need a non-linear prediction model to perform greenhouse crop irrigation prediction.  During operation, the system receives the input commands via sensors and then predicts the next watering run.  The irrigation is predicted using GRU, LSTM, and BLSTM and a comparison was made between the results of the three techniques, and the technique with the best result was selected.

  1. Cardoso J., Glória A., Sebastião P.  Improve Irrigation Timing Decision for Agriculture using Real-Time Data and Machine Learning.  2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI). 1–5 (2020).
  2. Jimenez A.-F., Ortiz B. V., Bondesan L., Morata G., Damianidis D.  Long Short-Term Memory Neural Network for irrigation management: a case study from Southern Alabama, USA.  Precision Agric.  22 (2), 475–492 (2021).
  3. Adeyemi O., Grove I., Peets S., Domun Y., Norton T.  Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling.  Sensors.  18 (10), 3408 (2018).
  4. Suzuki Y., Ibayashi H., Mineno H.  An SVM-based irrigation control system for home gardening.  2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE). 365–366 (2013).
  5. Ramya S., Swetha A. M., Doraipandian M.  IoT Framework for Smart Irrigation using Machine Learning Technique.  Journal of Computer Science.  16 (3), 355–363 (2020).
  6. Kumar A., Surendra A., Mohan H., Valliappan K. M., Kirthika N.  Internet of things based smart irrigation using regression algorithm.  2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). 1652–1657 (2017).
  7. Ramsundar B., Zadeh R. B.  TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning. O'Reilly Media (2018).
  8. Kapoor A.  Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems. Packt Publishing (2019).
Mathematical Modeling and Computing, Vol. 10, No. 2, pp. 524–533 (2023)