Comparison of Machine Learning Regression Models for Prediction of Soil Moisture with the use of Internet of Things Irrigation System Data

Nesnelerin İnterneti (IoT) teknolojisi, sistemlerin insanlardan bağımsız olarak kontrol edilmesine ve yönetilmesine olanak tanır. Nesnelerin interneti tabanlı tarım uygulamaları, dünya nüfusunun giderek artmasıyla tarımda gıda tüketimi ve su kıtlığı sorunlarına çözüm olarak yaygınlaşmıştır. Toprak nemi, tarımsal üretim ve hidrolojik döngüler için önemli bir faktördür ve tarımsal uygulamaların geliştirilmesinde toprak neminin tahmin edilmesi gerekmektedir. Bu çalışmada Arduino Uno kartına bağlı Esp8266 Wifi modülü, nem ve sıcaklık, toprak nemi, yağmur ve ultraviyole sensörlerinden oluşan IoT tabanlı bir sulama sistemi prototipi sunulmuştur. Daha sonra prototip sistemi kullanılarak 55 gün boyunca yarım saatlik periyotlarla belirlenen pilot alandan veriler toplanır ve ThingSpeak ile bulut üzerinden kaydedilir. Toplanan veriler kullanılarak çoklu doğrusal, polinomal , destek vektörü, karar ağacı ve rastgele orman regresyonu gibi farklı makine öğrenimi regresyon modelleri uygulanarak toprak nem değeri tahmin edilir. Elde edilen sonuçlar, bu algoritmaların başarısını incelemek için belirlilik katsayısı ve ortalama kare hatası kriterlerine göre karşılaştırılır. Rastgele orman regresyon modeli toprak nem tahmini için diğer makine öğrenmesi algoritmalarından daha üstün bulunmuştur

Anahtar Kelimeler:

Toprak Nem Tahmini, Makine Öğrenmesi Regresyon Modelleri, Nesnelerin İnterneti, ThingSpeak

Comparison of Machine Learning Regression Models for Prediction of Soil Moisture with the use of Internet of Things Irrigation System Data

Internet of Things (IoT) technology allows the control and management of systems independent of humans. Internet of things based agriculture applications have become widespread as a solution to the problems of food consumption and water scarcity in agriculture as the World population has increased gradually. Soil moisture is an important factor for agriculture production and hydrological cycles and the prediction of soil moisture is required in developing agricultural practices. In this study, an IoT-based irrigation system prototype is presented which consist of Esp8266 Wifi module, humidity and temperature, soil moisture, rain and ultraviolet sensors connected to the Arduino Uno board. Then, using the prototype system, data are collected from the pilot area determined in half-hour periods for 55 days and saved over the cloud with ThingSpeak. The soil moisture value is estimated by applying different machine learning regression models such as multiple linear, polynomial, support vector, decision tree and random forest regression using the collected data. The results obtained are compared according to the coefficient of determination and mean square error criteria to examine the success of these algorithms. The random forest regression model has found to be superior to other machine learning algorithms for soil moisture estimation.

Keywords:

Soil Moisture Prediction, Machine Learning Regression Models, Internet of Things, ThingSpeak,

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