A Novel Cleaning Method for Yield Data Collected by Sensors: A Case Study on Winter Cereals

Winter cereals yield tracking is a common practice since decision support systems can greatly benefit from the integration of these data. However, scientific literature highlights that many systematic errors occur during yield data collection. An efficient and easy to automatize protocol to clean collected field data is still missing despite its development is essential to integrate this useful tool in a smart-farming platform. This paper focuses on the development of a yield data cleaning procedure, easy to industrialize and performant in different contexts. This method is based on both empirical cleaning steps and statistical analysis on the “moving windows”. The developed cleaning procedure enabled the mixing of data coming from different combine harvesters and considered yield data measurements from the farmers to strengthen the results. In order to create readable and complete maps, an interpolation method concludes the procedure. The developed method is applied on a case study on real farm data.