  IoT systems have been facing increasingly sophisticated technical problems
due to the growing complexity of these systems and their fast deployment
practices. Consequently, IoT managers have to judiciously detect failures
(anomalies) in order to reduce their cyber risk and operational cost. While
there is a rich literature on anomaly detection in many IoT-based systems,
there is no existing work that documents the use of ML models for anomaly
detection in digital agriculture and in smart manufacturing systems. These two
application domains pose certain salient technical challenges. In agriculture
the data is often sparse, due to the vast areas of farms and the requirement to
keep the cost of monitoring low. Second, in both domains, there are multiple
types of sensors with varying capabilities and costs. The sensor data
characteristics change with the operating point of the environment or machines,
such as, the RPM of the motor. The inferencing and the anomaly detection
processes therefore have to be calibrated for the operating point.
  In this paper, we analyze data from sensors deployed in an agricultural farm
with data from seven different kinds of sensors, and from an advanced
manufacturing testbed with vibration sensors. We evaluate the performance of
ARIMA and LSTM models for predicting the time series of sensor data. Then,
considering the sparse data from one kind of sensor, we perform transfer
learning from a high data rate sensor. We then perform anomaly detection using
the predicted sensor data. Taken together, we show how in these two application
domains, predictive failure classification can be achieved, thus paving the way
for predictive maintenance.
