Soil Moisture a Posteriori Measurements Enhancement Using Ensemble Learning

This work aimed to assess the recalibration and accurate characterization of commonly used smart soil-moisture sensors using computational methods. The paper describes an ensemble learning algorithm that boosts the performance of potato root moisture estimation and increases the simple moisture sensors' performance. It was prepared using several month-long everyday actual outdoor data and validated on the separated part of that dataset. To obtain conclusive results, two different potato varieties were grown on 24 separate plots on two distinct soil profiles and, besides natural precipitation, several different watering strategies were applied, and the experiment was monitored during the whole season. The acquisitions on every plot were performed using simple moisture sensors and were supplemented with reference manual gravimetric measurements and meteorological data. Next, a group of machine learning algorithms was tested to extract the information from this measurements dataset. The study showed the possibility of decreasing the median moisture estimation error from 2.035% for the baseline model to 0.808%, which was achieved using the Extra Trees algorithm.

Automated summary

This study aimed to recalibrate and accurately characterize commonly used smart soil-moisture sensors using computational methods. It proposed an ensemble learning algorithm to improve potato root moisture estimation and the performance of simple moisture sensors. The results showed that the median moisture estimation error could be reduced from 2.035% for the baseline model to 0.808% using the Extra Trees algorithm.