Decision-making in agriculture increasingly relies on quantitative analyses of large-scale data. This data is generated by sensors on farming machinery such as seeders, sprayers, and combine harvesters. Good decision-making requires valid data, yet automated collection often introduces systematic errors. Yield data is an example of useful data that is also especially error prone. Agricultural researchers have developed algorithms for identifying common problems and cleaning data, but the algorithms are implemented in GUI applications, most commonly as Excel macros. Often, they are one-off implementations. Although there are some advantages to this approach, it hinders the development of reproducible research in precision agriculture and introduces unnecessary costs. An R package called cydr was developed to facilitate reproducible research in precision agriculture, and to simplify cleaning of yield data by identifying and removing some common problems. cydr ensures accessibility for new users, while providing powerful functionality for experienced users.
For more information please visit the project website.