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Agriculture
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Big Data Application in Agriculture
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Big Data Application in Agriculture

A special issue of Agriculture (ISSN 2077-0472).

Deadline for manuscript submissions: closed (31 July 2016) | Viewed by 36167

Special Issue Editors

Dr. Ritaban Dutta

E-Mail Website
Guest Editor
CSIRO DATA61, Hobart, TAS 7001, Australia
Interests: big data analytics; digital agriculture; cloud computing for agriculture; agricultural decision support system; spatial analysis; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Ritaban Dutta

E-Mail Website
Guest Editor
CSIRO Data61, Hobart, Tasmania 7001, Australia
Interests: big data analytics; remote sensing; machine learning; precision agriculture; digital agronomy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The motivation behind this Agriculture Special Issue is to bring “Big Data Solutions for Agriculture” to identify the key challenges that are faced by big data analysts trying to solve problems for agriculture communities, discuss potential solutions, and identify the opportunities emerging from cross-domain interactions among agriculture experts, hydrologists, dairy experts, aquaculture experts, and big data analytics experts. Therefore, we expect to gain from the domain experts an explanation of how they can apply big data analytics, semantic web standards, machine learning techniques, and linked data standards into their scientific research via high impact publications in this Special Issue. We believe that this Special Issue will be a unique opportunity to highlight one of the main themes of next generation digital agriculture, making agricultural decision through the big data computational lens.

More specifically, the topics of interest include, but are not limited to:

  • All aspects of big data platform technology
  • Architecture for big data innovation
  • Big data innovation in agriculture
  • Performance evaluation of big data principles;
  • Environmental big data integration;
  • Smart farm and its application in big data;
  • Environmental big data;
  • Big data in agricultural disaster management;
  • Agricultural multilingual taxonomies and glossaries;
  • Cloud based decision support system for plant diseases;
  • Environmental big data and knowledge management;
  • Problem with big data

Dr. Ritaban Dutta
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. Agriculture is an international peer-reviewed open access monthly 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 2600 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

  • Big Data Analytics
  • Digital Agriculture
  • Cloud Computing for Agriculture
  • Agricultural Decision Support System
  • Spatial Analysis
  • Precision Agriculture
  • Decision Support Systems
  • Machine Learning in Agriculture
  • Remote Sensing.

Published Papers (5 papers)

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Research

2747 KiB  
Communication
Development and Testing of a Device to Increase the Level of Automation of a Conventional Milking Parlor through Vocal Commands
by Mauro Zaninelli
Agriculture 2017, 7(1), 3; https://doi.org/10.3390/agriculture7010003 - 12 Jan 2017
Cited by 2 | Viewed by 5397
Abstract
A portable wireless device with a “vocal commands” feature for activating the mechanical milking phase in conventional milking parlors was developed and tested to increase the level of automation in the milking procedures. The device was tested in the laboratory and in a [...] Read more.
A portable wireless device with a “vocal commands” feature for activating the mechanical milking phase in conventional milking parlors was developed and tested to increase the level of automation in the milking procedures. The device was tested in the laboratory and in a milking parlor. Four professional milkers participated in the experiment. Before the start of the tests, a set of acoustic models with speaker-dependent commands defined for the project was acquired for each milker using a dedicated “milker training procedure”. Two experimental sessions were performed by each milker, with one session in the laboratory and a subsequent session in the milking parlor. The device performance was evaluated based on the accuracy demonstrated in the vocal command recognition task and rated using the word recognition rate (WRR). The data were expressed as %WRR and grouped based on the different cases evaluated. Mixed effects logistic regression modeling was used to evaluate the association between the %WRR and explanatory variables. The results indicated significant effects due to the location where the tests were performed. Higher values of the %WRR were found for tests performed in the laboratory, whereas lower values were found for tests performed in the milking parlor (due to the presence of background noise). Nevertheless, the general performance level achieved by the device was sufficient for increasing the automation level of conventional milking parlors. Full article
(This article belongs to the Special Issue Big Data Application in Agriculture)
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3856 KiB  
Article
Combination of Fuzzy Logic and Analytical Hierarchy Process Techniques to Assess Potassium Saturation Percentage of Some Calcareous Soils (Case Study: Fars Province, Southern Iran)
by Marzieh Mokarram and Mahdi Najafi-Ghiri
Agriculture 2016, 6(4), 59; https://doi.org/10.3390/agriculture6040059 - 06 Dec 2016
Cited by 4 | Viewed by 5986
Abstract
This research was carried out to evaluate the capability of a combined fuzzy logic-based approach and analytical hierarchy process (AHP) for potassium saturation percentage (KSP) estimation in some calcareous soils of southern Iran. Based on a reconnaissance soil survey, 52 soil series were [...] Read more.
This research was carried out to evaluate the capability of a combined fuzzy logic-based approach and analytical hierarchy process (AHP) for potassium saturation percentage (KSP) estimation in some calcareous soils of southern Iran. Based on a reconnaissance soil survey, 52 soil series were selected and different physical and chemical properties were determined. Five soil parameters including clay, cation exchange capacity, calcium carbonate equivalent, electrical conductivity, and organic carbon were chosen for analysis. Mapping was developed with the kriging method for each parameter. Different fuzzy membership functions were employed and weights for all parameters were calculated according to AHP. Finally, KSP classes were provided for each land unit. Results indicated that about 60% of the studied area is classified as having moderate to high KSP content (>3%) and 40% of had low or very low KSP content (<3%). Then 15 sample points were used for determination of the accuracy of the fuzzy method. Results showed that the fuzzy and AHP methods have a high accuracy for KSP estimation in the studied soils. Further development of the fuzzy and AHP methods would be worthwhile for improving the accuracy of KSP analysis. Full article
(This article belongs to the Special Issue Big Data Application in Agriculture)
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2350 KiB  
Article
Frequency Domain Probe Design for High Frequency Sensing of Soil Moisture
by Mathew G. Pelletier, Robert C. Schwartz, Greg A. Holt, John D. Wanjura and Timothy R. Green
Agriculture 2016, 6(4), 60; https://doi.org/10.3390/agriculture6040060 - 11 Nov 2016
Cited by 14 | Viewed by 5959
Abstract
Accurate moisture sensing is an important need for many research programs as well as in control of industrial processes. This paper describes the development of a high accuracy frequency domain sensing probe for use in obtaining dielectric measurements of materials suitable for work [...] Read more.
Accurate moisture sensing is an important need for many research programs as well as in control of industrial processes. This paper describes the development of a high accuracy frequency domain sensing probe for use in obtaining dielectric measurements of materials suitable for work ranging from 300 MHz to 1 GHz. The probe was developed to accommodate a wide range of permittivity’s ranging from εr = 2.5 to elevated permittivity’s as high as εr = 40. The design provides a well-matched interface between the soil and the interconnecting cables. A key advantage of the frequency domain approach is that a change of salt concentration has a significantly reduced effect on ε′, versus the traditional time-domain reflectometry, TDR, measured apparent permittivity, Ka. Full article
(This article belongs to the Special Issue Big Data Application in Agriculture)
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978 KiB  
Article
Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique
by Jaafar Abdulridha, Reza Ehsani and Ana De Castro
Agriculture 2016, 6(4), 56; https://doi.org/10.3390/agriculture6040056 - 27 Oct 2016
Cited by 45 | Viewed by 9801
Abstract
Laurel wilt (Lw) is a fatal disease. It is a vascular pathogen and is considered a major threat to the avocado industry in Florida. Many of the symptoms of Lw resemble those that are caused by other diseases or stress factors. In this [...] Read more.
Laurel wilt (Lw) is a fatal disease. It is a vascular pathogen and is considered a major threat to the avocado industry in Florida. Many of the symptoms of Lw resemble those that are caused by other diseases or stress factors. In this study, the best wavelengths with which to discriminate plants affected by Lw from stress factors were determined and classified. Visible-near infrared (400–950 nm) spectral data from healthy trees and those with Lw, Phytophthora, or salinity damage were collected using a handheld spectroradiometer. The total number of wavelengths was averaged in two ranges: 10 nm and 40 nm. Three classification methods, stepwise discriminant (STEPDISC) analysis, multilayer perceptron (MLP), and radial basis function (RBF), were applied in the early stage of Lw infestation. The classification results obtained for MLP, with percent accuracy of classification as high as 98% were better than STEPDISC and RBF. The MLP neural network selected certain wavelengths that were crucial for correctly classifying healthy trees from those with stress trees. The results showed that there were sufficient spectral differences between laurel wilt, healthy trees, and trees that have other diseases; therefore, a remote sensing technique could diagnose Lw in the early stage of infestation. Full article
(This article belongs to the Special Issue Big Data Application in Agriculture)
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1671 KiB  
Article
Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran)
by Saeid Hamzeh, Marzieh Mokarram, Azadeh Haratian, Harm Bartholomeus, Arend Ligtenberg and Arnold K. Bregt
Agriculture 2016, 6(4), 52; https://doi.org/10.3390/agriculture6040052 - 10 Oct 2016
Cited by 17 | Viewed by 7029
Abstract
Land suitability classification is important in planning and managing sustainable land use. Most approaches to land suitability analysis combine a large number of land and soil parameters, and are time-consuming and costly. In this study, a potentially useful technique (combined feature selection and [...] Read more.
Land suitability classification is important in planning and managing sustainable land use. Most approaches to land suitability analysis combine a large number of land and soil parameters, and are time-consuming and costly. In this study, a potentially useful technique (combined feature selection and fuzzy-AHP method) to increase the efficiency of land suitability analysis was presented. To this end, three different feature selection algorithms—random search, best search and genetic methods—were used to determine the most effective parameters for land suitability classification for the cultivation of barely in the Shavur Plain, southwest Iran. Next, land suitability classes were calculated for all methods by using the fuzzy-AHP approach. Salinity (electrical conductivity (EC)), alkalinity (exchangeable sodium percentage (ESP)), wetness and soil texture were selected using the random search method. Gypsum, EC, ESP, and soil texture were selected using both the best search and genetic methods. The result shows a strong agreement between the standard fuzzy-AHP methods and methods presented in this study. The values of Kappa coefficients were 0.82, 0.79 and 0.79 for the random search, best search and genetic methods, respectively, compared with the standard fuzzy-AHP method. Our results indicate that EC, ESP, soil texture and wetness are the most effective features for evaluating land suitability classification for the cultivation of barely in the study area, and uses of these parameters, together with their appropriate weights as obtained from fuzzy-AHP, can perform good results for land suitability classification. So, the combined feature selection presented and the fuzzy-AHP approach has the potential to save time and money for land suitability classification. Full article
(This article belongs to the Special Issue Big Data Application in Agriculture)
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