Computational Intelligence in Agriculture and Natural Resources

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Design, Modeling and Computing Methods".

Deadline for manuscript submissions: closed (30 October 2020) | Viewed by 10787

Special Issue Editors


E-Mail Website
Guest Editor
Department of Natural Resources Development and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Street, 11855 Athens, Greece
Interests: process control; computational intelligence; automation in agriculture; wireless sensor networks; microgrids’ management
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Agricultural Engineering, Institute of Soil and Water Resources, Hellenic Agricultural Organization “Demeter”, 61 Dimokratias Av., 13561 Athens, Greece
Interests: artificial intelligence in agriculture; deep learning; wireless sensor networks; controlled environment agriculture; hydroponics

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) encompasses a large part of artificial intelligence (AI) methodologies, which are mainly inspired by natural processes and systems, and are used to solve real-world problems to which the application of conventional methods can be problematic due to their high complexity and their inherent stochastic nature. A variety of such real-world problems exist in the areas of agriculture and natural resources. The processes involved are usually complex, with many uncertainties, and CI-based methodologies often constitute the only feasible approach to their modelling, design, and optimization.

The aim of this Special Issue is to gather and present recent works where CI methodologies are developed for solving complex problems in the fields of agriculture and natural resources. We invite you to contribute to this issue by submitting comprehensive reviews, case studies, or research articles that focus on scientific methods and technological tools, in order to provide an opportunity for learning the state-of-the-art and for discussion on future directions in computational intelligence in agriculture and natural resources fields. Papers selected for this Special Issue will be subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications.

Topics of interest include, but are not limited to, the following aspects of computational intelligence (CI):

  • CΙ-based decision support systems
  • CΙ tools in precision agriculture
  • CΙ techniques in agricultural robotics and automation equipment
  • CI in autonomous agricultural vehicles
  • CI paradigms in agricultural UAVs and drones
  • CI and agricultural knowledge-based systems
  • CI in agricultural optimization management
  • intelligent interfaces and human–machine interaction
  • CI in machine vision and image/signal processing
  • CI in machine learning, deep learning, and pattern recognition
  • neural networks, fuzzy systems, neuro-fuzzy systems
  • agents and multi-agent systems
  • heuristic and meta-heuristic algorithms
  • intelligent systems for animal feeding and management
  • CI in crop phenotyping and analysis
  • CI in protected horticulture
  • CI in remote sensing in agriculture and natural resources
  • CI in food engineering and cold chain logistics
  • CI in Big Data analysis and management
  • CI in agricultural data mining and information extraction
  • CI in smart sensors and Internet of Things
  • bio-informatics
  • CI in cloud computing
  • web intelligence and semantic web
  • CI in fault detection and diagnosis
  • CI in augmented and virtual reality
  • CI in water, irrigation, and drainage management
  • CI in the management and control of smart grids
  • CI in the management of renewable energy sources
  • CI in the management and control of energy mini- and micro-grids

Prof. Dr. Konstantinos G. Arvanitis
Dr. Dinos Ferentinos
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. Inventions 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 1800 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.

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

12 pages, 1829 KiB  
Article
Classification of Tree Species in the Process of Timber-Harvesting Operations Using Machine-Learning Methods
by Fedor Svoikin, Kirill Zhuk, Vladimir Svoikin, Sergey Ugryumov, Ivan Bacherikov, Daniela Veas Iniesta and Anatoly Ryapukhin
Inventions 2023, 8(2), 57; https://doi.org/10.3390/inventions8020057 - 22 Mar 2023
Cited by 2 | Viewed by 1324
Abstract
This article presents the constraining factors that limit the increase in the efficiency of logging production by modern multi-operation machines operating on the Scandinavian cut-to-length technology in the felling phase, namely the selection and registration of wood species. The factors for creating a [...] Read more.
This article presents the constraining factors that limit the increase in the efficiency of logging production by modern multi-operation machines operating on the Scandinavian cut-to-length technology in the felling phase, namely the selection and registration of wood species. The factors for creating a complete architecture of a fully connected neural network (NN) are given. The dependence of the prediction accuracy of a fully connected NN on a test sample on the size of the training dataset, and an image of the dependence of the prediction accuracy on the number of trees in the random forest method for image classification is shown. For a fully connected NN, a sufficient number of images and a test sample size were established for training, using tree-trunk breed-class labels as target values. A selected list of trees was given, with the size of the training sample of images presenting a problem for the classification of tree trunks using the random forest method. The aim was the discovery of the optimal number of trees necessary to achieve prediction accuracy. Full article
(This article belongs to the Special Issue Computational Intelligence in Agriculture and Natural Resources)
Show Figures

Figure 1

16 pages, 824 KiB  
Article
The Entropy Model for Sustainability Assessment in Industrial Ecosystems
by Tatyana Tolstykh, Nadezhda Shmeleva, Yulia Vertakova and Vladimir Plotnikov
Inventions 2020, 5(4), 54; https://doi.org/10.3390/inventions5040054 - 7 Nov 2020
Cited by 5 | Viewed by 2794
Abstract
The aim of this paper is to address the gap in the academic literature towards the development of methodological approaches to the industrial ecosystem sustainability assessment. This study was focused on the industrial ecosystems formed based on an entropy model and implementing the [...] Read more.
The aim of this paper is to address the gap in the academic literature towards the development of methodological approaches to the industrial ecosystem sustainability assessment. This study was focused on the industrial ecosystems formed based on an entropy model and implementing the principles of complex systems. This article systematizes the problem of applying the ecosystem approach to cross-industry interaction. A contribution to the literature was achieved by providing a systemic perspective on the sustainable industrial process. In this paper, we develop the methodological foundations to improve the understanding of integration processes’ influence on the industrial ecosystem potential. For a relevant analysis of industrial ecosystem potential, the existing patterns of system functioning were taken into account, including entropy equilibrium and the Harrington model. We illustrate our assumptions with an empirical case study of the National University of Science and Technology (NUST) “MISIS” ecosystem—“Green technologies for resource conservation” (Russia), with an assessment of ecosystem sustainability through the actors’ collaboration level. The propositions arising from this analysis provide information to help academics, policymakers, government, and individual enterprises with a more adequate understanding of the practical mechanisms and tools that help trigger the self-organization and sustainability of the industrial ecosystems. Full article
(This article belongs to the Special Issue Computational Intelligence in Agriculture and Natural Resources)
Show Figures

Figure 1

13 pages, 2006 KiB  
Article
Heat Pump Dryer Design Optimization Algorithm
by Bernardo Andrade, Ighor Amorim, Michel Silva, Larysa Savosh and Luís Frölén Ribeiro
Inventions 2019, 4(4), 63; https://doi.org/10.3390/inventions4040063 - 10 Oct 2019
Cited by 1 | Viewed by 5864
Abstract
Drying food involves complex physical atmospheric mechanisms with non-linear relations from the air-food interactions, and those relations are strongly dependent on the moisture contents and the type of food. Such dependence makes it complex to design suitable dryers dedicated to a single drying [...] Read more.
Drying food involves complex physical atmospheric mechanisms with non-linear relations from the air-food interactions, and those relations are strongly dependent on the moisture contents and the type of food. Such dependence makes it complex to design suitable dryers dedicated to a single drying process. To streamline the design of a novel compact food-drying machine, a heat pump dryer component design optimization algorithm was developed as a subprogram of a Computer Aided Engineering tool. The algorithm requires inputting food and air properties, the volume of the drying container, and the technical specifications of the heat pump off-the-shelf components. The heat required to dehumidify the food supplied by the heat exchange process from condenser to evaporator, and the compressor’s requirements (refrigerant mass flow rate and operating pressures) are then calculated. Compressors can then be selected based on the volume and type of food to be dried. The algorithm is shown via a flow chart to guide the user through three different stages: Changes in drying air properties, heat flow within dryer and product moisture content. Example results of how different compressors are selected for different types of produces and quantities (Agaricus blazei mushroom with three different moisture contents or fish from Thunnini tribe) conclude this article. Full article
(This article belongs to the Special Issue Computational Intelligence in Agriculture and Natural Resources)
Show Figures

Figure 1

Back to TopTop