Abstract: Palms trees (Phoenix dactylifera L.), Al Nakheel in Arabic are known to have cultural and economic importance to Gulf and Arabic-speaking countries. However, using the traditional method of cultivation, improper use, and depletion of water is perceived as the major challenge as farmers used almost two and a half times the required amount without considering numerous factors. This paper attempts to develop an implementation model of a water management system for Date Palm Trees using Case Based-Reasoning. The said model involves an IoT-based module comprised of NodeMCU, soil moisture, temperature, and humidity sensors that automate the settings of the water amount for the whole year based on palm age, temperature, air humidity, and soil moisture. CBR calculates the amount of water supplied to palm trees (based on initial knowledgebase cited from previous empirical studies) and stores it in a cloud-based database. These data and hardware status can be accessed using a mobile application. When the temperature or soil moisture sensor fails, data trends are retrieved from the database and processed using Linear Regression Analysis. The test results have shown that the proposed model helped in a significant decrease in water consumption compared to the traditional method.

Ferddie Quiroz Canlas, Moayad Al Falahi and Sarachandran Nair, “IoT based Date Palm Water Management System Using Case-Based Reasoning and Linear Regression for Trend Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 13(2), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130264

@article{Canlas2022,
title = {IoT based Date Palm Water Management System Using Case-Based Reasoning and Linear Regression for Trend Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130264},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130264},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {2},
author = {Ferddie Quiroz Canlas and Moayad Al Falahi and Sarachandran Nair}
}