Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Luque Reyes, J.R., et al.: Early prediction of winter cereals yield. In: Global IoT Summit (GIoTS). IEEE (2020)
Guidotti, D., Marchi, S., Antognelli, S., Cruciani, A.: Water management: agricolus tools integration. In: Global IoT Summit (GIoTS). IEEE (2019)
Luck, J.D., Fulton, J.D.: Best management practices for collecting accurate yield data and avoing errors during harvest. University of Nebraska Cooperative extension, Institute of Agriculture and Natural Resources (2004)
Adamchuck, V.I., Dobermann, A., Ping, J.: Listening to the story told by yield maps. University of Nebraska Cooperative extension, EC 04-704 (2004)
Sudduth, K.A., Drummond, S.T.: Yield editor: software for removing errors from crop yield maps. Agron. J. 99, 1471–1482 (2007)
Arslan, S., Colvin, T.S.: Grain yield mapping: yield sensing, yield reconstruction, and errors. Precis. Agric. 3, 135–154 (2002)
Kharel, T., et al.: Yield monitor data cleaning is essential for accurate corn grain/silage yield determination. Agron. J. 111, 509–516 (2018)
Blackmore, S.: The interpretation of trends from multiple yield maps. Comput. Electron. Agric. 26(1), 37–51 (2000)
Kleinjan, J., Clay, D.E., Carlson, C.G., Clay, S.A.: Developing productivity zones from multiple years of yield monitor data. Site-Specific Management Guidelines (SSMG) Series, International Plant Nutrition Institute (IPNI) (2002)
Spekken, M., Anselmi, A.A., Molin, J.P.: A simple method for filtering spatial data. In: 9th European Conference on Precision Agriculture, ECPA (2013)
Maldaner, L., Corrêdo, L., Rodrigues Tavares, T., Mendez, L.G., Duarte, C., Molin, J.: Identifying and filtering out outliers in spatial datasets. In: 14th International Conference on Precision Agriculture, Montreal, Quebec, Canada (2018)
Vega, A., Córdoba, M., Castro-Franco, M., Balzarini, M.: Protocol for automating error removal from yield maps. Precis. Agric. 20(5), 1030–1044 (2019). https://doi.org/10.1007/s11119-018-09632-8
Souza, E., Bazzi, C., Khosla, R., Uribe, O.M., Reich, R.M.: Interpolation type and data computation of crop yield maps is important for precision crop production. J. Plant Nutr. 39, 531–538 (2016)
Acknowledgements
We acknowledge Mr. Mauro Brunetti of the Foundation for Agricultural Education of Perugia for his very helpful and valuable support and collaboration in the data collection activities, and the University of Perugia to have supported the research activities as part of the PhD programme.
Funding
This research was developed within the framework of the project “RTK 2.0—Prototipizzazione di una rete RTK e di applicazioni tecnologiche innovative per l’automazione dei processi colturali e la gestione delle informazioni per l’agricoltura di precisione”—RDP 2014–2020 of Umbria—Meas. 16.1—App. 84250020256.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Natale, A., Antognelli, S., Ranieri, E., Cruciani, A., Boggia, A. (2020). A Novel Cleaning Method for Yield Data Collected by Sensors: A Case Study on Winter Cereals. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12253. Springer, Cham. https://doi.org/10.1007/978-3-030-58814-4_55
Download citation
DOI: https://doi.org/10.1007/978-3-030-58814-4_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58813-7
Online ISBN: 978-3-030-58814-4
eBook Packages: Computer ScienceComputer Science (R0)