Development of a novel machine vision procedure for rapid and non-contact measurement of soil moisture content
| Autor: | Reza P. R. Hasanzadeh, Kaveh Mollazade, Fatemeh Rahimi-Ajdadi, Yousef Abbaspour-Gilandeh |
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| Rok vydání: | 2018 |
| Předmět: |
chemistry.chemical_classification
Adaptive neuro fuzzy inference system 010504 meteorology & atmospheric sciences Soil texture Machine vision Applied Mathematics Soil science 04 agricultural and veterinary sciences Stepwise regression Color space Condensed Matter Physics 01 natural sciences chemistry Linear regression 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science Organic matter Electrical and Electronic Engineering Instrumentation Water content 0105 earth and related environmental sciences |
| Zdroj: | Measurement. 121:179-189 |
| ISSN: | 0263-2241 |
| DOI: | 10.1016/j.measurement.2018.02.060 |
| Popis: | Soil moisture measurement is one of the essential management components to decrease water consumption and prevent water stresses in plants. In this study, a fast and non-contact method using machine vision and artificial intelligence was developed so as to make operators capable of having an estimate of soil moisture by taking only one image. Three soil textures along with three levels of added organic matter were applied. Mean comparison and the subsequent stepwise multiple regression were applied to find superior features from different color spaces. ANFIS and stepwise multiple regression were used to predict the soil moisture. Results indicated that the general model could predict the soil moisture with mean absolute error of less than 1.1%. This value reached to 0.3% for some sub-models belonging to the texture–organic matter group. Application of the present method is highly recommended for soil moisture measurement because of simple implementation and potential for online measurements. |
| Databáze: | OpenAIRE |
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