Master Yield Monitoring 2025: Ace the Components, Calibrate with Confidence, and Decode Data!

Filtering data during yield data cleaning is an essential practice to ensure that the analysis is based on accurate and relevant information. Excluding points measured during equipment turns is a valid option because these points typically do not represent the true yield data of the crop. When machinery turns, it often does not accurately capture yield data due to overlaps or gaps that occur during the turning process, which can skew the results. As a result, by excluding these data points, the dataset becomes more reliable and reflective of actual yield performance across the cultivated area.

In contrast, including all data points, regardless of quality, would lead to a dataset that is filled with inaccuracies, making it challenging to draw meaningful conclusions. Deleting points collected from uncultivated areas might remove some irrelevant data but could also eliminate data from border areas where yield conditions might differ. Normalizing all data points to a zero baseline, while useful in some analyses, does not address the quality or contextual relevance of the data itself, which is a key aspect when cleaning yield data. Therefore, the option to exclude points measured during equipment turns enhances data quality significantly.