Visualisation of Big Data in Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 10426

Special Issue Editor


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Guest Editor
University of West Bohemia, Plan4all association, Czech Republic
Interests: Geographic Information System (GIS); multidimensional data modelling and visualization; climatic and Earth Observation data analysis for agricultural applications

Special Issue Information

Dear Colleagues,

It is my pleasure to announce the opening of a new Special Issue in the journal Applied Science entitled “Visualisation of Big Data in Agriculture”. The Issue will focus on leveraging various contemporary existing as well as emerging data sources in both crop and livestock production. Innovative data processing and analysis methods are of particular interest, in addition to the final presentation and visualisation of the synthesised information. 

As contemporary data science usually handles various types of data from which the final information is acquired, there is particular interest in manuscripts processing, analysing, and visualising the outputs of several kinds of data sources together, with a definite impact on agriculture. 

This Issue expects manuscripts that describe the utilisation of Earth Observation data (both multi and hyperspectral), climatic data, in-situ sensor data (connected to IoT), crowdsourced data, linked data, etc. together with traditional geographic data, stressing the multidimensionality of the data (e.g., the third and temporal dimensions of the data).

Next, each manuscript should describe an application of the data analysis/visualisation in agricultural processes and evaluate its impact, for example, on the optimisation of production or mitigation of environmental impact. 

As the Issue looks for innovative methods of applying big data in agriculture, fundamental aspects of big data must be addressed (volume, variety, veracity) in the manuscripts.

I hope you find the Issue topic interesting, and I look forward to your research contributions.

Dr. Karel Jedlička
Guest Editor

Manuscript Submission Information

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Keywords

  • big data
  • multidimensional data
  • data analysis
  • data visualization
  • data-driven agriculture
  • precision farming
  • autonomous farming

Published Papers (3 papers)

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Research

24 pages, 5640 KiB  
Article
Calculation of Agro-Climatic Factors from Global Climatic Data
by Karel Jedlička, Jiří Valeš, Pavel Hájek, Michal Kepka and Martin Pitoňák
Appl. Sci. 2021, 11(3), 1245; https://doi.org/10.3390/app11031245 - 29 Jan 2021
Cited by 5 | Viewed by 3362
Abstract
This manuscript aims to create large-scale calculations of agro-climatic factors from global climatic data with high granularity-climatic ERA5-Land dataset from the Copernicus Climate Change Service in particular. First, we analyze existing approaches used for agro-climatic factor calculation and formulate a frame for our [...] Read more.
This manuscript aims to create large-scale calculations of agro-climatic factors from global climatic data with high granularity-climatic ERA5-Land dataset from the Copernicus Climate Change Service in particular. First, we analyze existing approaches used for agro-climatic factor calculation and formulate a frame for our calculations. Then we describe the design of our methods for calculation and visualization of certain agro-climatic factors. We then run two case studies. Firstly, the case study of Kojčice validates the uncertainty of input data by in-situ sensors. Then, the case study of the Pilsen region presents certain agro-climatic factors calculated for a representative point of the area and visualizes their time-variability in graphs. Maps represent a spatial distribution of the chosen factors for the Pilsen region. The calculated agro-climatic factors are frost dates, frost-free periods, growing degree units, heat stress units, number of growing days, number of optimal growing days, dates of fall nitrogen application, precipitation, evapotranspiration, and runoff sums together as water balance and solar radiation. The algorithms are usable anywhere in the world, especially in temperate and subtropical zones. Full article
(This article belongs to the Special Issue Visualisation of Big Data in Agriculture)
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21 pages, 12173 KiB  
Article
Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery
by Tomáš Řezník, Petr Kubíček, Lukáš Herman, Tomáš Pavelka, Šimon Leitgeb, Martina Klocová and Filip Leitner
Appl. Sci. 2020, 10(17), 6132; https://doi.org/10.3390/app10176132 - 03 Sep 2020
Cited by 2 | Viewed by 3296
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Visualisation of Big Data in Agriculture)
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22 pages, 2367 KiB  
Article
Climate-Smart Agro-Hydrological Model for a Large Scale Rice Irrigation Scheme in Malaysia
by Habibu Ismail, Md Rowshon Kamal, Ahmad Fikri bin Abdullah and Mohd Syazwan Faisal bin Mohd
Appl. Sci. 2020, 10(11), 3906; https://doi.org/10.3390/app10113906 - 04 Jun 2020
Cited by 8 | Viewed by 2928
Abstract
Agro-hydrological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung [...] Read more.
Agro-hydrological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung Karang Rice Irrigation Scheme in Malaysia has yet to model agro-hydrological systems for effective water distribution under climate change impacts. A climate-smart agro-hydrological model was developed using Excel-based Visual Basic for Applications (VBA) for adaptive irrigation and wise water resource management towards water security under new climate change realities. Daily climate variables for baseline (1976–2005) and future (2010–2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway scenarios (RCP4.5, RCP6.0, and RCP8.5). The projected available water for supply to the scheme would noticeably decrease during the dry season. The water demand in the scheme will differ greatly during the months in future dry seasons, and the increase in effective rainfall during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. In order to improve water distribution, simulated flows from the model could be incorporated with appropriate cropping patterns. Full article
(This article belongs to the Special Issue Visualisation of Big Data in Agriculture)
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