Internet of things sensors and support vector machine integrated intelligent irrigation system for agriculture industry
Kumar G.K. Bangare M.L. Bangare P.M. Kumar C.R. Raj R. Arias-Gonzáles J.L. Omarov B. Mia M.S.
December 2024Springer Nature
Discover Sustainability
2024#5Issue 1
Because there is more demand for freshwater around the world and the world’s population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world’s current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier.
Accuracy , Fresh Water Usage , Humidity sensors , Intelligent irrigation system , IoT , K-means SVM , Soil temperature sensor , Temperature Sensors
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Velagapudi Ramakrishna Siddartha Engineering College, Vijayawada, India
Department of Information Technology, Savitribai Phule Pune University Smt. Kashibai Navale College of Engineering, Pune, India
Department of E&TC, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, India
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Deemed to Be University, Telangana, Hyderabad, 500075, India
Economics, Haryana, Kurukshetra, India
University of British Columbia, Vancouver, Canada
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh
Velagapudi Ramakrishna Siddartha Engineering College
Department of Information Technology
Department of E&TC
Department of Computer Science and Engineering
Economics
University of British Columbia
Al-Farabi Kazakh National University
Department of Computer Science and Engineering
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