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Agronomy
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Big Data, Artificial Intelligence and Decision Support Systems in Sustainable...
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Big Data, Artificial Intelligence and Decision Support Systems in Sustainable Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (16 January 2023) | Viewed by 13959

Special Issue Editors

Prof. Dr. Gniewko Niedbała

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Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production
Special Issues, Collections and Topics in MDPI journals
Dr. Sebastian Kujawa

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Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With recent advances in information and communication technologies, big data are becoming the skeleton of many data-driven application domains, agriculture among them. Big data analytics, and therefore the decision support systems that can be derived from them, involve advanced learning and analysis techniques to deal with exceptionally large, diverse datasets collected from different sources with varying sizes. Analyzing big data is beyond the ability of traditional mining techniques. Precision and digital agriculture generates huge amounts of data using a variety of digital devices and other sources, ranging from mobiles, sensors, GIS, satellites to IoTs, financial, and other private agencies. Agricultural data analysis and mining are a particularly challenging duet to the high complexity of its datasets which are heterogeneous, very large, and collected with different objectives and qualitative attributes.

This Special Issue focuses on all the challenges of agricultural Big Data. We encourage researchers working in this big data analytics domain which is at the cross-boundaries between agriculture and computer science (Data Science) to share their research and their innovative techniques on how to deal with the challenges mentioned above. This Special Issue will certainly make significant contributions to the advancement of digital agriculture and data science.

Prof. Dr. Gniewko Niedbała
Dr. Sebastian Kujawa
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. Agronomy 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
  • artificial intelligence
  • machine learning
  • blockchain
  • agricultural decision support systems, ERP, FMIS
  • remote and proximal sensing
  • Internet of Things
  • cloud computing
  • monitoring and forecasting in crop and livestock production
  • precision and digital agriculture
  • sustainable agriculture
  • carbon farming

Published Papers (2 papers)

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17 pages, 4619 KiB  
Article
Applicability of Machine-Learned Regression Models to Estimate Internal Air Temperature and CO2 Concentration of a Pig House
by Uk-Hyeon Yeo, Seng-Kyoun Jo, Se-Han Kim, Dae-Heon Park, Deuk-Young Jeong, Se-Jun Park, Hakjong Shin and Rack-Woo Kim
Agronomy 2023, 13(2), 328; https://doi.org/10.3390/agronomy13020328 - 21 Jan 2023
Cited by 3 | Viewed by 1481
Abstract
Carbon dioxide (CO2) emissions from the livestock industry are expected to increase. A response strategy for CO2 emission regulations is required for pig production as this industry comprises a large proportion of the livestock industry and it is projected that [...] Read more.
Carbon dioxide (CO2) emissions from the livestock industry are expected to increase. A response strategy for CO2 emission regulations is required for pig production as this industry comprises a large proportion of the livestock industry and it is projected that per capita pork consumption will rise. A CO2 emission response strategy can be established by accurately measuring the CO2 concentrations in pig facilities. Here, we compared and evaluated the performance of three different machine learning (ML) models (ElasticNet, random forest regression (RFR), and support vector regression (SVR)) designed to predict CO2 concentration and internal air temperature (Ti) values in the pig house used to regulate a heating, ventilation, and air conditioning (HVAC) control system. For each ML model, the hyperparameter was optimised and the predictive accuracy was evaluated. The order of predictive accuracy for the ML models was ElasticNet < SVR < RFR. Hence, random forest regression provided superior prediction performance. Based on the test dataset, for Ti prediction by RFR, R2 ≥ 0.848 and the root mean square error (RMSE) and mean absolute error (MAE) were 0.235 °C and 0.160 °C, respectively, whilst for CO2 concentration prediction by RFR, R2 ≥ 0.885 and the RMSE and MAE were 64.39 ppm and ≤ 46.17 ppm, respectively. Full article
(This article belongs to the Special Issue Big Data, Artificial Intelligence and Decision Support Systems in Sustainable Agriculture)
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Review

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34 pages, 6382 KiB  
Review
Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review
by Ania Cravero, Sebastian Pardo, Samuel Sepúlveda and Lilia Muñoz
Agronomy 2022, 12(3), 748; https://doi.org/10.3390/agronomy12030748 - 21 Mar 2022
Cited by 54 | Viewed by 11361
Abstract
Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address problems such as farmers’ decision making, water management, soil management, crop management, and [...] Read more.
Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address problems such as farmers’ decision making, water management, soil management, crop management, and livestock management. Crop management includes yield prediction, disease detection, weed detection, crop quality, and species recognition. On the other hand, livestock management considers animal welfare and livestock production. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in agricultural Big Data. We conducted a systematic literature review applying the PRISMA protocol. This review includes 30 papers published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, machine learning techniques, and the leading technologies used. A significant challenge is the design of agricultural Big Data architectures due to the need to modify the set of technologies adapting the machine learning techniques as the volume of data increases. Full article
(This article belongs to the Special Issue Big Data, Artificial Intelligence and Decision Support Systems in Sustainable Agriculture)
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