  Amidst changing climate, real-time soil moisture monitoring is vital for the
development of in-season decision support tools to help farmers manage weather
related risks. Precision Sustainable Agriculture (PSA) recently established a
real-time soil moisture monitoring network across the central, Midwest, and
eastern U.S., but field-scale sensor observations often come with data gaps and
anomalies. To maintain the data quality needed for development of decision
tools, a quality control system is necessary. The International Soil Moisture
Network (ISMN) introduced the Flagit module for anomaly detection in soil
moisture observations. However, under certain conditions, Flagit's quality
control approaches may underperform in identifying anomalies. Recently deep
learning methods have been successfully applied to detect anomalies in time
series data in various disciplines. However, their use in agriculture has not
been yet investigated. This study focuses on developing a Bi-directional Long
Short-Term Memory (LSTM) model, referred to as DeepQC, to identify anomalies in
soil moisture data. Manual flagged PSA observations were used for training,
validation, and testing the model, following an 80:10:10 split. The study then
compared the DeepQC and Flagit based estimates to assess their relative
performance. Flagit corrected flagged 95.5% of the corrected observations and
50.3% of the anomaly observations, indicating its limitations in identifying
anomalies. On the other hand, the DeepQC correctly flagged 99.7% of the correct
observations and 95.6% of the anomalies in significantly less time,
demonstrating its superiority over Flagit approach. Importantly, DeepQC's
performance remained consistent regardless of the number of anomalies. Given
the promising results obtained with the DeepQC, future studies will focus on
implementing this model on national and global soil moisture networks.
