Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

Tomáš Řezník,Lukáš Herman,Šimon Leitgeb,Petr Kubíček,Tomáš Pavelka,Martina Klocová,Filip Leitner

doi:10.3390/app10176132

Abstract

Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations.

Highlights

The period of the past twenty years can be characterized by a shift from conventional agriculture through precision agriculture [1] towards the latest innovations in the form of data-driven agriculture [2].A crucial part of precision agriculture is estimating crop yield
Farm data on yield measurements were provided by the Rostěnice Farm in the Czech Republic
Agronomic practices, and yield measurements were provided for the purposes of this this paper

Summary

Introduction

The period of the past twenty years can be characterized by a shift from conventional agriculture through precision agriculture [1] towards the latest innovations in the form of data-driven agriculture [2].A crucial part of precision agriculture is estimating crop yield. Information on yield is important for two main reasons: it can be used to maximise economic profitability and to reduce the environmental burden caused by agricultural activities [3,4]. Both objectives need as much precise information on yield as possible. It has been defined, for example, by Auernhammer [5], that a yield in a plot is not homogeneous owing to variable soil conditions, weather, climate, and so on. Data from field harvesters represent the most detailed, as well as the most credible, source of yield information. Conventional approaches of reducing these biases through filtering the data were described, for example, in Blackmore [6], Arslan and Colvin [7], Lyle et al [8], Reznik et al [9], Vega et al [10], and Reznik et al [11]

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