Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Smart Agriculture Based on Big Data and Internet of Things...
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Smart Agriculture Based on Big Data and Internet of Things (IoT)

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 January 2025) | Viewed by 2390

Special Issue Editors

Prof. Dr. Jacek Przybył

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Poznań University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: modern agricultural equipment; use of agricultural machinery; digital-smart agriculture; engineering of crop production processes; postharvest technologies and process engineering; sustainable agriculture; biosystems engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Dawid Wojcieszak

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental Engineering and Mechanical Engineering, Poznan University of Life Sciences, Poznan, Poland
Interests: modern agricultural equipment; use of agricultural machinery; postharvest technologies and process engineering; biomass energy; biosystems engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Madueño Luna

E-Mail Website
Guest Editor
Department of Aerospace Engineering and Fluid Mechanics, University of Seville, 41092 Seville, Spain
Interests: artificial intelligence and machine learning applications; computer vision in horticulture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable agriculture supports the conservation of natural resources, halts biodiversity loss and reduces greenhouse gas emissions. Sustainable agriculture is a combination of the best conventional technologies and practices offering precision farming concepts and digital technologies. The aim of using digitalization is to optimize and increase the quality of production, reduce human labor, reduce industrial inputs and reduce environmental pressures. Today, in the era of Agriculture 4.0, the latest digital technology tools are currently being employed—namely, the Internet of Things (IoT), Big Data analytics, artificial intelligence, machine learning, satellite technologies, cloud computing, etc. New technologies are one of the key tools to enable the development of sustainable agriculture. As such, one of the most important technologies is the Internet of Things (IoT). This concept of connecting devices and collecting and processing the Big Data received from them allows for the continuous creation of streams of interconnected data as well as the creation of new information.

Smart agriculture emphasizes information and communication technologies in machines, equipment and sensors. This not only allows for high-tech farm monitoring and the automation of processes but also provides a possibility for remotely controlling both processes and work. Currently, these solutions are being used, among others, to monitor changes in soil characteristics, climatic factors and humidity.

Innovative digital technologies, i.e., satellite technologies, cloud computing or artificial intelligence, are expected to contribute to agricultural development, greater production efficiency and resource savings, as well as promote food security and reduce climate change. Digitization and the implementation of new technologies seem to be a natural path for agricultural development. However, the application of new technologies also raises certain concerns and poses new challenges for farmers.

Therefore, this Special Issue aims to review a wide range of theoretical and experimental research related to agricultural production processes implemented with innovative digital technologies, the possibilities of their application and the assessment of their effectiveness, as well as the anticipated challenges that arise when combining innovative technologies with conventional agricultural activities.

This Special Issue welcomes all types of articles and is intended for a broad and multidisciplinary audience.

Prof. Dr. Jacek Przybył
Dr. Dawid Wojcieszak
Dr. Antonio Madueño Luna
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. 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

  • sustainable agriculture
  • smart agriculture
  • data storage and processing
  • Internet of Things (IoT)
  • big data analytics
  • artificial intelligence
  • machine learning
  • cloud computing
  • satellite technologies
  • predictive models
  • remote control of processes and works
  • automation and autonomy
  • field management
  • reduction of fertilizer and pesticide use
  • reduction of energy inputs
  • increased crop production
  • increase in production efficiency
  • reduction of climate change

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 2745 KiB  
Article
Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
by Ersin Elbasi, Ahmet E. Topcu, Elda Cina, Aymen I. Zreikat, Ahmed Shdefat, Chamseddine Zaki and Wiem Abdelbaki
Appl. Sci. 2024, 14(22), 10507; https://doi.org/10.3390/app142210507 - 14 Nov 2024
Viewed by 1114
Abstract
In botany and agriculture, classifying leaves is a crucial process that yields vital information for studies on biodiversity, ecological studies, and the identification of plant species. The Cope Leaf Dataset offers a comprehensive collection of leaf images from various plant species, enabling the [...] Read more.
In botany and agriculture, classifying leaves is a crucial process that yields vital information for studies on biodiversity, ecological studies, and the identification of plant species. The Cope Leaf Dataset offers a comprehensive collection of leaf images from various plant species, enabling the development and evaluation of advanced classification algorithms. This study presents a robust methodology for classifying leaf images within the Cope Leaf Dataset by enhancing the feature extraction and selection process. Cope Leaf Dataset has 99 classes and 64 features with 1584 records. Features are extracted based on the margin, texture, and shape of the leaves. It is challenging to classify a large number of labels because of class imbalance, feature complexity, overfitting, and label noise. Our approach combines advanced feature selection techniques with robust preprocessing methods, including normalization, imputation, and noise reduction. By systematically integrating these techniques, we aim to reduce dimensionality, eliminate irrelevant or redundant features, and improve data quality. Increasing accuracy in classification, especially when dealing with large datasets and many classes, involves a combination of data preprocessing, model selection, regularization techniques, and fine-tuning. The results indicate that the Multilayer Perception algorithm gives 89.48%, the Naïve Bayes Classifier gives 89.63%, Convolutional Neural Networks has 88.72%, and the Hoeffding Tree algorithm gives 89.92% accuracy for the classification of 99 label plant leaf classification problems. Full article
(This article belongs to the Special Issue Smart Agriculture Based on Big Data and Internet of Things (IoT))
►▼ Show Figures

Figure 1

15 pages, 3552 KiB  
Article
Effect of Tomato Juice and Different Drying Methods on Selected Properties of Courgette
by Magdalena Kręcisz, Bogdan Stępień and Karol Pikor
Appl. Sci. 2024, 14(16), 7105; https://doi.org/10.3390/app14167105 - 13 Aug 2024
Viewed by 821
Abstract
The purpose of this study was to determine the impact of vacuum impregnation on selected physical properties of courgettes, the drying process, and kinetics of the drying process. Vacuum impregnation was used as a pretreatment in the conducted research. The drying process was [...] Read more.
The purpose of this study was to determine the impact of vacuum impregnation on selected physical properties of courgettes, the drying process, and kinetics of the drying process. Vacuum impregnation was used as a pretreatment in the conducted research. The drying process was carried out using three techniques (convection drying, freeze drying, and vacuum drying). In the presented work, selected properties of courgettes, i.e., water activity, dry weight, density, VGI, shrinkage, and color were investigated, and the best model describing the kinetics of the drying process was selected. As a result of the study, it was found that the pretreated courgette was characterized by increased dry matter (0.44% to 4.08%) and density content (15.52% to 33.78%) and reduced or increasing water activity (−5.08 to 38.62%) depending on the drying method. The process also resulted in reduced drying shrinkage (−2.13% to −6.97%). Tomato juice was used as an impregnating solution, resulting in an increase in red intensity (8.44) and a decrease in the L* color index (80.16 to 58.00 for the fresh courgette). Dries with the most favorable properties were obtained using the freeze-drying method. The best model of the drying process kinetics was the logistic model. Full article
(This article belongs to the Special Issue Smart Agriculture Based on Big Data and Internet of Things (IoT))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop