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Enormous amounts of data are generated each day by sensor devices. In agriculture, these devices continuously monitor numerous environmental properties in the fields of aquaponics, hydroponics, and soil-based food production. Data stream mining is the process of extracting data from continuous, rapidly sampled data sources. The data accuracy that can be achieved in data stream mining is highly dependent on the algorithm chosen to suppress noise. For threshold-based automation, an actuator can be activated when the value of sensor data is above a permissible threshold. Noise from sensors may activate the actuator. Several statistical and machine learning-based noise- suppression algorithms have been proposed in the literature. The proposed LSTM (Long Short-Term Memory) filter performs better noise suppression than other traditional filters – Kalman and moving average filters. The LSTM filter is installed in our threshold-based aquaponics automation to maximize sustainable food production at minimum cost.