Deployment and Verifications of the Spatial Filtering of Data Measured by Field Harvesters and Methods of Their Interpolation: Czech Cereal Fields between 2014 and 2018.

Řezník Řezník,Širůček Širůček,Pavelka Pavelka,Lukas Lukas,Leitgeb Leitgeb,Herman Herman

doi:10.3390/s19224879

Řezník Řezník, Širůček Širůček + Show 4 more

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https://doi.org/10.3390/s19224879

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Abstract

Yield mapping is a subject of research in (precision) agriculture and one of the primary concerns for farmers as it forms the basis of their income and has implications for subsidies and taxes. The presented approach involves deployment of field harvesters equipped with sensors that provide more detailed and spatially localized values than merely a sum of yields for the whole plot. The measurements from such sensors need to be filtered and subject to further processing, including interpolation, to facilitate follow-up interpretation. This paper aims to identify the relative differences between interpolations from (1) (field) measured data, (2) measured data that were globally filtered, and (3) measured data that were globally and locally filtered. All the measured data were obtained at a fully operational farm and are considered to represent a natural experiment. The revealed spatial patterns and recommendations regarding global and local filtering methods are presented at the end of the paper. Time investments into filtering techniques are also taken into account.

Highlights

IntroductionPrecision agriculture is defined as “management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production” [1]
Data measured by a cereal field harvester were used to analyze and evaluate the approaches of
Data measured by a cereal field harvester were used to analyze and evaluate the approaches of spatial filtering and interpolation

Summary

Introduction

Precision agriculture is defined as “management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production” [1]. The most tangible negative consequences of agricultural activities can be seen in the degradation of soil (e.g., erosion and the loss of organic content and/or biodiversity) and the pollution of (ground) waters by residues from fertilizers [2] as well as pesticides. In all such cases, precise spatial information is the key to the most efficient as well as sustainable usage of arable land. As stated by Aurenhammer [3], the concept of precision farming adds, among other things, the perspective of variable rate treatment

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