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Thermal drift compensation of load cell reading using linear regression in weighing lysimeters

Author:

G. Abhiram

Massey University, Private Bag 11 222, Palmerston North 4442, NZ
About G.

Environmental Sciences, School of Agriculture & Environment

 

Department of Export Agriculture, Faculty of Animal Science and Export Agriculture, Uva Wellassa University, Badulla

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Abstract

The accuracy of load cell data is significantly influenced by thermal drift. Different techniques have been used to minimise thermal drift during the manufacturing of load cells and the data processing stage. However, a proper study for thermal drift compensation using a simple method is unavailable. In this study, therefore, simple linear regression models were developed between load cell readings and temperature changes at 5-, 30- and 60-minute time steps and models were tested against an independent data set to compensate thermal drift. The load cell reading and temperature changes showed a strong relationship with correlation coefficient (R2) values between 81-95 for both tested load cells (A7 & A8). The regression model was load cell-specific. The linear regression model predicted the load cell reading well and it was found that increasing the time step decreased the residual error. The predicted load cell values positively correlated with observed load cell values and R2 values were 80.3, 82.7, and 82.8, for 5-, 30- and 60-minute time steps, respectively. The correlation analysis depicts that the developed linear model generally underpredicted the load cell readings at all three-time steps. The developed linear regression model is useful for predicting load cell readings using temperature data which can be used to offset the thermal drift in load cell readings.
How to Cite: Abhiram, G., 2022. Thermal drift compensation of load cell reading using linear regression in weighing lysimeters. AGRIEAST: Journal of Agricultural Sciences, 16(2), pp.47–59. DOI: http://doi.org/10.4038/agrieast.v16i2.116
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Published on 21 Dec 2022.
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