  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.
