Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Soil Moisture Detection Using Arduino Sensor and ANN Prediction
Smart irrigation systems are essential to detect the existing moisture content of soil, which regulates and controls the water supply to irrigation. The present study focuses on the on-board installation of soil moisture sensor with Arduino UNO platform to measure the moisture content of soil samples, which will facilitate in releasing of irrigation water. The present experimental study uses five uniform (poorly graded) soil samples of size d50 = 850, 600, 425, 300, and 150 µm and a non-uniform (well-graded) soil sample of d50 = 325 µm. A fourth order polynomial is fitted between the sensor reading and degree of saturation, which is related to second order polynomial between the degree of saturation and moisture content of the soil. The sensor readings are used to estimate the existing moisture content of the soil sample through the degree of saturation of the soil through these polynomials. A satisfactory similarity is found between degree of saturation and versus normalized sensor readings for all the cases of uniform and no uniform soil. Further, power equation is developed between the sensor reading and the moisture content of the soil with an R2 value of 0.96. In addition, three machine learning prediction models ANN, KNN, and SVM were employed and compared. It is found that artificial neural network predicted the moisture content better than other predictors having prediction accuracy with R2 = 0.981 for training and 0.985 for validation indicating as a good predictor as compared to KNN and SVM.
Soil Moisture Detection Using Arduino Sensor and ANN Prediction
Smart irrigation systems are essential to detect the existing moisture content of soil, which regulates and controls the water supply to irrigation. The present study focuses on the on-board installation of soil moisture sensor with Arduino UNO platform to measure the moisture content of soil samples, which will facilitate in releasing of irrigation water. The present experimental study uses five uniform (poorly graded) soil samples of size d50 = 850, 600, 425, 300, and 150 µm and a non-uniform (well-graded) soil sample of d50 = 325 µm. A fourth order polynomial is fitted between the sensor reading and degree of saturation, which is related to second order polynomial between the degree of saturation and moisture content of the soil. The sensor readings are used to estimate the existing moisture content of the soil sample through the degree of saturation of the soil through these polynomials. A satisfactory similarity is found between degree of saturation and versus normalized sensor readings for all the cases of uniform and no uniform soil. Further, power equation is developed between the sensor reading and the moisture content of the soil with an R2 value of 0.96. In addition, three machine learning prediction models ANN, KNN, and SVM were employed and compared. It is found that artificial neural network predicted the moisture content better than other predictors having prediction accuracy with R2 = 0.981 for training and 0.985 for validation indicating as a good predictor as compared to KNN and SVM.
Soil Moisture Detection Using Arduino Sensor and ANN Prediction
Lecture Notes in Civil Engineering
Sreekeshava, K. S. (Herausgeber:in) / Kolathayar, Sreevalsa (Herausgeber:in) / Vinod Chandra Menon, N. (Herausgeber:in) / Raikar, Rajkumar (Autor:in) / Katageri, Basavaraj (Autor:in) / Khanai, Rajashri (Autor:in) / Torse, Dattaprasad (Autor:in) / Mannikatti, Praveen (Autor:in)
International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development ; 2023
26.03.2024
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
| DOAJ | 2020
| TIBKAT | 1984
SOIL MOISTURE MEASUREMENT SENSOR AND CAPACITANCE TYPE MOISTURE METER
| Europäisches Patentamt | 2021
Smart Irrigation System Using Arduino
| IEEE | 2023