Smart irrigation system based on IoT and machine learning. (November 2022)

• Record Type: Journal Article
• Authors: Tace, Youness; Tabaa, Mohamed; Elfilali, Sanaa; Leghris, Cherkaoui; Bensag, Hassna; Renault, Eric
• Is Part Of: Energy reports. Volume 8(2022)Supplement 9
• Page Start: 1025
• Page End: 1036
• Publication Date: 2022-11
• Subjects: Smart irrigation -- Machine learning -- Internet of Things -- Agriculture 4.0 -- Web app; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.07.088
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Traditional agriculture has been the pillar of development on the planet for centuries. But with exponential population growth and increasing demand, farmers will need water to irrigate the land to meet this demand. Because of the scarcity of this resource, farmers need a solution that changes the way they operate. With the advent of new technologies, the notion of Agriculture 4.0 has become a reality to keep up with and meet the demand. With the addition of artificial intelligence and IoT through the collection and processing of agricultural data, decisions have become more and more precise to facilitate decision-making. This paper proposes an intelligent and flexible irrigation approach with low consumption and cost that can be deployed in different contexts. This approach is based on machine learning algorithms for smart agriculture. For this, we used a set of sensors (soil humidity, temperature, and rain) in an environment that ensures better plant growth for months, from which we collected data based on an acquisition map using the Node-RED platform and MongoDB. We used many different models based on the collected data: KNN, Logistic Regression, Neural Networks, SVM, and Naïve Bayes. The results showed that K-Nearest Neighbors is better with a recognition rate of 98.3% and a root mean square error (RMSE) of 0.12, compared to other models (LR, NN, SVM, NB). and towards the end, we provided a web application that brings together the various data emitted by the sensors as well as the prediction of our models to allow better visualization and supervision of our environment.