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Agriculture
Special Issues
Data Science to Support Agricultural Diversification
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Data Science to Support Agricultural Diversification

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (25 April 2023) | Viewed by 4260

Special Issue Editors

Dr. Ebrahim Jahanshiri

E-Mail Website1 Website2
Guest Editor
Crops For the Future (CFF) UK CIC, National Institute of Agricultural Botany(NIAB), 93 Lawrence Weaver Road, Cambridge CB3 0LG, UK
Interests: data science; machine learning; data mining; geospatial analysis; open access; food systems
Prof. Dr. Sayed Azam-Ali

E-Mail Website1 Website2
Guest Editor
Crops For the Future (CFF) UK CIC, National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge CB3 0LG, UK
Interests: food systems; underutilised crops; ninth revolution

Special Issue Information

Dear Colleagues, 

Working with data and models is one of the primary skills of any environmental scientist and algorithms and databases that are developed for agriculture have had a great impact on developing evidence bases for production systems. Modelling routines, particularly those that are publicly available, are shaping a new discipline in agricultural sciences that looks at the science of diversification using the potential of forgotten, neglected and underutilised crops and cropping systems. Data science techniques, such as data and knowledge mining, databasing, modelling and simulations, are now being adapted to construct large datasets that can help with the development of locally relevant insights. In this Special Issue, we aim to collate research on using all aspects of data science that are relevant to the development of datasets, databases, crop simulation models and algorithms that can further enable decision making for growers and other stakeholders that are interested in the science of agricultural diversification. We particularly welcome efforts that are aimed at advancing the following aspects that are not well developed in relation to

  • Dataset development, data and knowledge acquisition techniques, database development and open data initiative
  • New models and algorithms that take advantage of big data and machine learning for the benefit of agriculture
  • Cases studies that utilise old datasets and models in a new way to further strengthen their impacts.

Dr. Ebrahim Jahanshiri
Prof. Dr. Sayed Azam-Ali
Guest Editors

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

  • data science
  • agricultural diversification
  • crop modelling
  • databases
  • machine learning
  • relational
  • NoSQL

Published Papers (3 papers)

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Research

19 pages, 3298 KiB  
Article
An Approach Based on Web Scraping and Denoising Encoders to Curate Food Security Datasets
by Fabián Santos and Nicole Acosta
Agriculture 2023, 13(5), 1015; https://doi.org/10.3390/agriculture13051015 - 06 May 2023
Cited by 1 | Viewed by 1861
Abstract
Ensuring food security requires the publication of data in a timely manner, but often this information is not properly documented and evaluated. Therefore, the combination of databases from multiple sources is a common practice to curate the data and corroborate the results; however, [...] Read more.
Ensuring food security requires the publication of data in a timely manner, but often this information is not properly documented and evaluated. Therefore, the combination of databases from multiple sources is a common practice to curate the data and corroborate the results; however, this also results in incomplete cases. These tasks are often labor-intensive since they require a case-wise review to obtain the requested and completed information. To address these problems, an approach based on Selenium web-scraping software and the multiple imputation denoising autoencoders (MIDAS) algorithm is presented for a case study in Ecuador. The objective was to produce a multidimensional database, free of data gaps, with 72 species of food crops based on the data from 3 different open data web databases. This methodology resulted in an analysis-ready dataset with 43 parameters describing plant traits, nutritional composition, and planted areas of food crops, whose imputed data obtained an R-square of 0.84 for a control numerical parameter selected for validation. This enriched dataset was later clustered with K-means to report unprecedented insights into food crops cultivated in Ecuador. The methodology is useful for users who need to collect and curate data from different sources in a semi-automatic fashion. Full article
(This article belongs to the Special Issue Data Science to Support Agricultural Diversification)
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29 pages, 6008 KiB  
Article
A Shortlisting Framework for Crop Diversification in the United Kingdom
by Ebrahim Jahanshiri, Sayed Azam-Ali, Peter J. Gregory and Eranga M. Wimalasiri
Agriculture 2023, 13(4), 787; https://doi.org/10.3390/agriculture13040787 - 29 Mar 2023
Viewed by 1655
Abstract
We present a systematic framework for nationwide crop suitability assessment within the UK to improve the resilience in cropping systems and nutrition security of the UK population. An initial suitability analysis was performed using data from 1842 crops at 2862 grid locations within [...] Read more.
We present a systematic framework for nationwide crop suitability assessment within the UK to improve the resilience in cropping systems and nutrition security of the UK population. An initial suitability analysis was performed using data from 1842 crops at 2862 grid locations within the UK, using climate (temperature and rainfall) and soil (pH, depth, and texture) data from the UK Met Office and British Geological Survey. In the second phase, additional qualitative and quantitative data are collected on 56 crops with the highest pedoclimatic suitability and coverage across the UK. An exercise was conducted on crops within each category using a systematic ranking methodology that shortlists crops with high value across a multitude of traits. Crops were ranked based on their nutritional value (macronutrients, vitamins, and minerals) and on adaptive (resistance to waterlogging/flood, frost, shade, pest, weed, and diseases and suitability in poor soils) and physiological traits (water-use efficiency and yield). Other characteristics such as the number of special uses, available germplasm through the number of institutions working on the crops, and production knowledge were considered in shortlisting. The shortlisted crops in each category are bulbous barley (cereal), colonial bentgrass (fodder), Russian wildrye (forage), sea buckthorn (fruit), blue lupin (legume), shoestring acacia (nut), ochrus vetch (vegetable), spear wattle (industrial), scallion (medicinal), and velvet bentgrass (ornamental/landscape). These crops were identified as suitable crops that can be adopted in the UK. We further discuss steps in mainstreaming these and other potential crops based on a systematic framework that takes into account local farming system issues, land suitability, and crop performance modelling at the field scale across the UK. Full article
(This article belongs to the Special Issue Data Science to Support Agricultural Diversification)
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28 pages, 2351 KiB  
Article
Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks
by Mario San Emeterio de la Parte, Sara Lana Serrano, Marta Muriel Elduayen and José-Fernán Martínez-Ortega
Agriculture 2023, 13(2), 360; https://doi.org/10.3390/agriculture13020360 - 01 Feb 2023
Cited by 1 | Viewed by 2145
Abstract
In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of [...] Read more.
In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy. Full article
(This article belongs to the Special Issue Data Science to Support Agricultural Diversification)
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