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Applied Sciences
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Spatial Analysis of Agricultural Data
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Spatial Analysis of Agricultural Data

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 4387

Special Issue Editor

Prof. Dr. Antonio López-Quílez

E-Mail Website
Guest Editor
Department of Statistics and Operational Research, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Spain
Interests: spatial statistics; bayesian statistics; environmental statistics; biostatistics; epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the 21st century, the global population is expected to grow to 10 billion. The question of how to increase agricultural production to achieve food security and feed a growing population is one of the greatest challenges facing humanity. This needs to be addressed while maintaining sustainable agricultural systems and simultaneously facing challenges related to climate, resources and weather events. Automation with new technologies, sensors, yield monitors, internet of things (IoT) and drones and robots, as well as the use of GIS methods, artificial intelligence (AI), highly structured mathematical models and Big Data statistical techniques, serves as the basis for a global “Digital Twin”. This conceptualization will contribute to the development of site-specific conservation and management practices that will increase the income and global sustainability of agricultural systems. The spatial analysis of agricultural data is a key element in this context.

Satellite and aerial images, sensors and yield monitors provide information about production variability at macro and micro scales, with a great amount of agricultural data to be processed, represented, modeled and understood. Spatiotemporal models seem to offer additional benefits beyond the classical, spatially explicit modeling. Hierarchical models can deal with complex interactions by specifying parameters that change on several levels via the introduction of random effects.

The spread of transboundary plant pests and diseases caused by fungi, bacteria or viruses has increased significantly in recent years. These threats are causing significant losses and impacting food security. In essence, they spread by human-migrated movement and are windborne or vector-borne. A wide range of environmental, climatic and socioeconomic factors underlie their spatial patterns. In addition, factors such as changes in climate, habits or land use intervene and complicate the understanding of these processes.

This Special Issue is intended for a wide and multidisciplinary audience and presents some of the most recent advances and novel approaches in the spatial analysis of agricultural data.

Prof. Dr. Antonio López-Quílez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precision agriculture
  • ICT applications
  • Internet of Things (IoT)
  • GIS applications
  • remote sensing
  • spatial statistics
  • geospatial artificial intelligence
  • clustering
  • spatial prediction

Published Papers (2 papers)

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Research

15 pages, 790 KiB  
Article
Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields
by Nora C. Monsalve and Antonio López-Quílez
Appl. Sci. 2022, 12(18), 9005; https://doi.org/10.3390/app12189005 - 08 Sep 2022
Viewed by 874
Abstract
In this paper, we propose a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The methodological approach involves a Gaussian field (GF) affected by a spatial process represented by an approximation to a Gaussian Markov [...] Read more.
In this paper, we propose a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The methodological approach involves a Gaussian field (GF) affected by a spatial process represented by an approximation to a Gaussian Markov random field (GMRF). This modeling allows the building of maps with prediction probabilities regarding the presence of a disease in plants using Bayesian kriging. The advantage of this modeling is its computational benefit when compared with known spatial hierarchical models and with the Bayesian inference based on Markov chain Monte Carlo (MCMC) methods. Inference through the use of the integrated nested Laplace approximation (INLA) with the stochastic partial differential equation (SPDE) approach facilitates the handling of large datasets in excellent computation times. Our approach allows the evaluation of different sampling strategies, from which we obtain inferences and prediction maps with similar behaviour to those obtained when we consider all subjects in the study population. The analysis of the different sampling strategies allows us to recognize the relevance of spatial components in the studied phenomenon. We demonstrate how Bayesian kriging can incorporate sources of uncertainty associated with the prediction parameters, which leads to more realistic and accurate estimation of the uncertainty. We illustrate the methodology with samplings of Citrus macrophylla affected by the tristeza virus (CTV) grown in a nursery. Full article
(This article belongs to the Special Issue Spatial Analysis of Agricultural Data)
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20 pages, 9750 KiB  
Article
Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions
by Rui Zhang, Zhanzhong Tang, Dong Luo, Hongxia Luo, Shucheng You and Tao Zhang
Appl. Sci. 2021, 11(15), 6923; https://doi.org/10.3390/app11156923 - 28 Jul 2021
Cited by 8 | Viewed by 1990
Abstract
The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, [...] Read more.
The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained. Full article
(This article belongs to the Special Issue Spatial Analysis of Agricultural Data)
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