Dynamic Plant Water Use Measurement using Data from a Low-cost Weighing Lysimeter

Inexpensive, reliable ways to measure plant water use on short temporal scales are needed to improve irrigation efficiency. Apart from more expensive, accurate components, new data mining methods may be a way to extract more reliable data from a system with less precision. In this study, different time-averaging methods were applied to the data from a custom-built weighing lysimeter to determine the maximum amount of reliable water use information that could be determined. The relationship between data reliability and temporal resolution were examined based on error analyses, and a proposed dynamic time-stepping algorithm was used to optimize the temporal resolution and number of reliable data points obtained from the data set. The results given by the lysimeter system, and the data processing methods were verified using a precision laboratory balance as a reference. Separate month-long tests were performed, one with lettuce grown from seedlings, and another with small tomato and pepper plants. This paper aims to improve the quality and amount of information obtainable from a generic low-cost system with significant uncertainty.