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A Non-Invasive Revolution in Soil and Root Water Content Monitoring by Integrating Deep Learning and GPR
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Author(s): Mohammed Kahlaoui (National School of Applied Sciences, Morocco), Aboulkacem Karkri (National School of Applied Sciences, Morocco)and Mohammed Anisse Moutaouekkil (National School of Applied Sciences, Morocco)
Copyright: 2026
Pages: 30
Source title:
Computational Intelligence and Optimization Methods for Sustainable Water Management
Source Author(s)/Editor(s): Yassine Ezaier (Hassan II University, Casablanca, Morocco), Rajae Gaamouche (Moroccan School of Engineering Sciences, Rabat, Morocco)and Mohamed Lahby (Hassan II University, Casablanca, Morocco)
DOI: 10.4018/979-8-3373-2700-6.ch010
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Abstract
This chapter introduces a technique that combines simulations using Ground Penetrating Radar (GPR), advanced processing of signals and deep learning to determine water content in plant roots as well as nearby soil. Using dielectric mixing models, computer programs were used to create synthetic radargrams of root-soil interactions under various moisture levels. Radargrams were improved by using methods such as Singular Value Decomposition and Inverse Amplitude Decay. Convolutional Neural Networks (CNNs), including InceptionV3, MobileNetV2 and ResNet50V2, were adapted for using in classifying both root and soil water content. InceptionV3 got the top results among tested models for estimating plant roots. For easier soil moisture classification, MobileNetV2 was able to run quickly and efficiently. ResNet50V2 managed to perform with similar accuracy and level of stability for both types of tasks.
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