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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.
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A Noise Suppression of LSTM algorithm combined with Kalman filter for Agriculture Automation
An immense volume of data is produced by sensor devices in the fields of aquaponics, hydroponics, and soil-based food production, where these devices track various environmental factors. Data stream mining is the method of retrieving data from fast-sampled data sources that are constantly streaming. The accuracy of data obtained through data stream mining is largely determined by the algorithm utilized to filter out 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. They have been evaluated based on the mean squared error metric (MSE). The Long Short-Term Memory – LSTM filter (MSE: 0.000999943) performs better noise suppression than other traditional filters – Kalman (MSE: 0.0015982). We propose a new noise suppression filter – LSTM combined with Kalman (LSTM-KF). In LSTM-KF, the Kalman filter acts as an encoder and the LSTM becomes the decoder, resulting in a significantly lower MSE – 0.000080789592. The LSTM-KF is installed in our threshold-based aquaponics automation to maximize sustainable food production at minimum cost.
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- Award ID(s):
- 2131269
- PAR ID:
- 10546081
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9781450398329
- Page Range / eLocation ID:
- 230 to 234
- Subject(s) / Keyword(s):
- Machine Learning, Kalman Filter, Agriculture Automation
- Format(s):
- Medium: X
- Location:
- Stockholm Sweden
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3589883.3589918
