Outlier detection methods and sensor data fusion for precision agriculture

Abstract

Precision agriculture is a concept regarding the use of technology to increase production yield while preserving and optimizing resources. One of the means to achieve that goal is to use sensors to monitor crops and adjust the cultivation according to its needs. This paper compares different techniques for sensor data fusion and detection and removal of outliers from gathered data to improve sensors accuracy and to identify possible sensor malfunction. As a case study, we monitored an experimental crop of precocious dwarf cashew using soil moisture sensors. Combining generalized ESD method and a weighted outlier- robust Kalman filter generated the best result, leading to more accurate data.