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

TL;DR

This study explores machine learning methods, especially CNNs, combined with microwave imaging to accurately detect soil moisture near drip irrigation pipes, improving precision in agricultural water management.

Contribution

It introduces a novel approach integrating microwave imaging and ML algorithms, particularly CNNs, for local soil moisture detection around irrigation pipes, with emphasis on clutter reduction techniques.

Findings

CNN outperforms KNN in moisture estimation accuracy.

Clutter reduction improves imaging and classification results.

CNN with clutter reduction yields the best moisture detection performance.

Abstract

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.