<p>Sensor networks let a farmer keep their eye on multiple locations in an agricultural field simultaneously but can be expensive to install, maintain and analyse. Furthermore, sensors often suffer from gaps in the recording process which leads to missing data points or what are essentially ‘blind spots’ in the network structure. To cater for missing values, effective methods for data imputation are essential. In this paper, we use graphs to impute these missing values within sensor networks using a technique called graph signal processing (GSP) applied to soil moisture recordings. Using this method, we simulate network conditions involving missing sensors or inconsistently collected data. This enables farmers to reliably estimate the sensor readings that would have been obtained, thereby increasing the fault tolerance of their agricultural sensor networks. In this work, we are specifically interested in the relative accuracy of data imputation between several graph construction techniques within the GSP framework, both geometric, i.e., dependent on the geographical coordinates, and data-driven techniques, e.g., correlations between the sensor readings. We evaluated seven graph construction techniques, also comparing with a simple mean imputation baseline, for creating edges. By masking sensor values, we identify how accurately sensor values can be inferred. This is done by gradually masking sensors from the network with 1000 random sensor combinations per mask size and then imputing these “missing” sensors. For our experiments, we make use of the Cook Agronomy Farm (CAF) dataset for GSP imputation that contains soil moisture data recorded with 42 sensors. At almost at every timestamp not even once all moisture sensors recorded the data simultaneously, showcasing the value of correct data imputation in these sparse sensor networks. Our results indicate that data-driven graphs, that connect nodes (e.g., sensors) based on the underlying sensor recordings, tend to capture the relationships between sensors most accurately, where the data-driven Gaussian kernel graph (a signal similarity approach) consistently outperforms other graphs on average with 15% improvement across all experiments. Furthermore, compared to a simple baseline, error reduces between 50 and 70% depending on the underlying data. This suggests that the Gaussian kernel graph can function as a solid enhancement in applying GSP when sensors networks are either prone to faults or sparsely placed. Additional analysis showed that the interplay between graph density, signal smoothness and structural connectivity should be balanced for optimal performance.</p>