Machine Learning-Driven Microwave Imaging for Soil Moisture Estimation near Leaky Pipe

Characterizing soil moisture (SM) around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated by two different microwave imaging techniques based on spatio-temporal measurements at various frequency bands. The study investigates the performance of K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN) algorithms for moisture classification based on these images, both before and after performing soil clutter reduction. We also explore the potentials of CNN and KNN for moisture estimation around the plant roots and in the presence of pebbles. The results demonstrate the more accurate moisture estimation using CNN when it is applied after clutter reduction considering back projection algorithm (BPA) as the imaging technique.

A Microwave Imaging System for Soil Moisture Estimation in Subsurface Drip Irrigation

The microwave imaging system(MIS) stands out among prominent imaging tools for capturing images of concealed obstacles. Leveraging its capability to penetrate through heterogeneous environments MIS has been widely used for subsurface imaging. Monitoring subsurface drip irrigation(SDI) as an efficient procedure in agricultural irrigation is essential to maintain the required moisture percentage for plant growth which is a novel MIS application. In this research, we implement a laboratory-scale MIS for SDI reflecting real-world conditions to evaluate leakage localization and quantification in a heterogeneous area. We extract a model to quantify the moisture content by exploiting an imaging approach that could be used in a scheduled SDI. We employ the subspace information of images formed by back projection and Born approximation algorithms for model parametrization and estimate the model parameters using a statistical curve fitting technique. We then compare the performance of these imaging techniques in the presence of environmental clutter such as plant roots and pebbles. The proposed approach can well contribute to efficient mechanistic subsurface irrigation for which the local moisture around the root is obtained noninvasively and remotely with less than 20% estimation error.