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Leveraging IoT Technologies for the Future Smart Agriculture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3222

Special Issue Editors


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Guest Editor
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; smart agriculture; smart cities; big stream; data
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Food and Drug, University of Parma, 43124 Parma, Italy
Interests: micropropagation; flower and fruit biology; frost damage; systematic pomology; evaluation of fruit quality; varietal selection
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Food and Drug, University of Parma, Parco Area delle Scienze 27/A, 43124 Parma, Italy
Interests: edible flowers; ornamental plants; postharvest; plant physiology; bioactive compounds; volatile organic compounds; in vitro tissue culture and plant propagation; nutraceuticals; functional food
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Parma, viale delle Scienze 181/A, 43124 Parma, Italy
Interests: food engineering; food packaging; industrial quality and safety; life cycle assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, a digital transformation has influenced several aspects of our society, and has also positively impacted agriculture, which is integrating innovative technologies and solutions, evolving towards the digital agriculture (or Agriculture 4.0) paradigm. Considering this context, the Internet of Things (IoT) technologies represent one of the key and enabling aspects for ensuring connectivity and thus allowing on field continuous real-time monitoring through heterogeneous wireless or wired sensors networks.

In this historical moment, with the indiscriminate use of pesticides, climate change, reduction of water supplies, depletion of resources, as well as loss of soil quality, which are already limiting the amount of food produced by the world’s farmlands, it is necessary to identify the best approaches to tackle these challenges. Furthermore, reliable detection, accurate identification, and proper quantification of pathogens, diseases and other factors affecting plants and the whole environment health are critical and need to be kept under control in order to reduce economic overheads, trade disruptions, and human health risks.

Agriculture needs to be enhanced by new technologies in order to make it sustainable in a smart way. Leveraging on the integration of these smart technologies in crops and plants managements, a plenitude of new platforms, services and analytics are emerging. Some examples are agriculture-specific Cyber Physical Systems (CPSs), Decision Support Systems (DSSs), Machine Learning-based and Artificial Intelligence-oriented predictive models, resource optimization and monitoring systems, certification layers, and more in general, the acquisition of a new knowledge from the field to regional or national scale.

This Special Issue thus encourages authors, from academia and industry to submit new research exploring developments, advancements and novel insights into the state of the art, the challenges and the approaches in the development of IoT infrastructures for enabling new services in the field of smart agriculture with reference to vegetables and plants management.

Some topics of interest include but are not limited to:

  • Smart agriculture and agriculture 4.0;
  • IoT sensors and sensors networks deployment for smart agriculture;
  • IoT technologies integration for agriculture 4.0;
  • IoT architectures and infrastructures for agriculture 4.0;
  • CPSs for smart agriculture;
  • Blockchain technologies applied to smart agriculture;
  • ML-based and AI-oriented technologies applied to for smart agriculture;
  • Cloud computing for smart agriculture;
  • Edge computing for smart agriculture;
  • Sustainable resources management and optimization in smart agriculture;
  • Farm services and oriented applications for vegetable agri-food systems;
  • Data-aware networking in smart agriculture;
  • Modeling and metrics for sensing in smart agriculture;
  • Implementation and prototypes of WSN agriculture showcases;
  • Data security by user authentication and data encryption;
  • Security by design philosophy in agriculture;
  • Cost-effective sensor development and deployment for smart agriculture.

Dr. Laura Belli
Dr. Luca Davoli
Prof. Dr. Tommaso Ganino
Dr. Ilaria Marchioni
Prof. Dr. Giuseppe Vignali
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. Sensors 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 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.

Published Papers (1 paper)

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Research

14 pages, 15343 KiB  
Article
Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers
by Sana Parez, Naqqash Dilshad, Norah Saleh Alghamdi, Turki M. Alanazi and Jong Weon Lee
Sensors 2023, 23(15), 6949; https://doi.org/10.3390/s23156949 - 4 Aug 2023
Cited by 17 | Viewed by 2846
Abstract
In order for a country’s economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming. [...] Read more.
In order for a country’s economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming. This leads to an agricultural management system in need of a method for automatically detecting disease at an early stage. As a consequence of dimensionality reduction, CNN-based models use pooling layers, which results in the loss of vital information, including the precise location of the most prominent features. In response to these challenges, we propose a fine-tuned technique, GreenViT, for detecting plant infections and diseases based on Vision Transformers (ViTs). Similar to word embedding, we divide the input image into smaller blocks or patches and feed these to the ViT sequentially. Our approach leverages the strengths of ViTs in order to overcome the problems associated with CNN-based models. Experiments on widely used benchmark datasets were conducted to evaluate the proposed GreenViT performance. Based on the obtained experimental outcomes, the proposed technique outperforms state-of-the-art (SOTA) CNN models for detecting plant diseases. Full article
(This article belongs to the Special Issue Leveraging IoT Technologies for the Future Smart Agriculture)
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