  Soil moisture estimation is an important task to enable precision agriculture
in creating optimal plans for irrigation, fertilization, and harvest. It is
common to utilize statistical and machine learning models to estimate soil
moisture from traditional data sources such as weather forecasts, soil
properties, and crop properties. However, there is a growing interest in
utilizing aerial and geospatial imagery to estimate soil moisture. Although
these images capture high-resolution crop details, they are expensive to curate
and challenging to interpret. Imagine, an AI-enhanced software tool that
predicts soil moisture using visual cues captured by smartphones and
statistical data given by weather forecasts. This work is a first step towards
that goal of developing a multi-modal approach for soil moisture estimation. In
particular, we curate a dataset consisting of real-world images taken from
ground stations and their corresponding weather data. We also propose MIS-ME -
Meteorological & Image based Soil Moisture Estimator, a multi-modal framework
for soil moisture estimation. Our extensive analysis shows that MIS-ME achieves
a MAPE of 10.79%, outperforming traditional unimodal approaches with a
reduction of 2.6% in MAPE for meteorological data and 1.5% in MAPE for image
data, highlighting the effectiveness of tailored multi-modal approaches.
