Advances in Agriculture 4.0

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 8391

Special Issue Editors


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Guest Editor
Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
Interests: communications and networking; Internet of Things; pervasive and physical computing; sensor networks; industrial informatics; location and context awareness; informatics in education
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
Interests: agricultural robots; biomimetic robots; control engineering; automation systems

E-Mail Website
Guest Editor
Department of Agriculture, Hellenic Mediterranean University, 71410 Heraklion, Greece
Interests: sustainable management of waste and natural resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The fourth agricultural revolution, known as Agriculture 4.0 in analogy to Industry 4.0, refers to the anticipated radical changes in agricultural production through the application of recent advances in Information Technology. Agriculture 4.0 is a term for the next big trends to impact this industry, including a greater focus on precision agriculture with the regular application of the Internet of Things (IoT), autonomous robots, big-data (from sensors, earth observation, logistics, etc.), and Artificial Intelligence (AI), to drive greater business efficiencies, make smarter planning decisions, and lower the environmental footprint. These technological innovations are expected to play a major role in addressing the four main challenges, identified in the 2018 report published by the World Government Summit, facing future agricultural production: rising populations, scarcity of natural resources, climate change, and food waste.

With Agriculture 4.0, the whole agri-food chain will become more competitive and profitable, through the extended use of smart networked sensors, automation systems, and information technology. Deeper insight and precise advice for plant requirements will empower farmers to minimize agricultural input (water, nutrients, plant protection, etc.) thus minimizing environmental and health hazards, but also to scale production further by reducing space requirements, growing periods, and logistic chains. Labor-intensive tasks will be undertaken by autonomous ground robots or unmanned aerial vehicles, equipped with machine vision systems and precision telemetry. Tighter specifications and traceability – made possible by data connectivity – will boost quality across the supply chain to meet the demands of local and international food markets and to increase consumer trust.

The goal of this Special Issue is to disseminate high-quality, state-of-the-art research papers that deal with challenging issues in Agriculture 4.0. We solicit original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal. Topics of interest include, but are not limited to, modeling and applied approaches, at various scales (from lab to field and global), including engineering aspects and agronomical impact of the following:

  • Advances in sensors technologies for Agriculture 4.0;
  • Advances in communications and networking for Agriculture 4.0;
  • Advances in autonomous robotic systems and unmanned aerial vehicles for Agriculture 4.0;
  • Blockchain technologies for Agriculture 4.0;
  • Food traceability and supply chain management for Agriculture 4.0;
  • Low-power and energy-efficient Agriculture 4.0;
  • Advances in machine vision for Agriculture 4.0;
  • Cloud/fog/edge/extreme edge computing for Agriculture 4.0;
  • Sensor and data fusion for Agriculture 4.0;
  • Advances in profiling and human–computer interfaces for Agriculture 4.0;
  • Machine learning and computational intelligence for Agriculture 4.0;
  • Decision support systems for Agriculture 4.0;
  • Data analytics for Agriculture 4.0.

Dr. Spyros Panagiotakis
Dr. Michael Sfakiotakis
Dr. Ioannis N. Daliakopoulos
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. Future Internet 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 1600 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

  • Agriculture 4.0
  • sensors
  • telemetry
  • Internet of Things
  • robotics
  • machine vision
  • big data
  • machine learning
  • AI
  • food traceability
  • supply chain
  • blockchain
  • networking
  • precision agriculture

Published Papers (3 papers)

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Research

12 pages, 4054 KiB  
Article
Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application
by Eduardo Assunção, Pedro D. Gaspar, Khadijeh Alibabaei, Maria P. Simões, Hugo Proença, Vasco N. G. J. Soares and João M. L. P. Caldeira
Future Internet 2022, 14(11), 323; https://doi.org/10.3390/fi14110323 - 8 Nov 2022
Cited by 7 | Viewed by 2564
Abstract
Within the scope of precision agriculture, many applications have been developed to support decision making and yield enhancement. Fruit detection has attracted considerable attention from researchers, and it can be used offline. In contrast, some applications, such as robot vision in orchards, require [...] Read more.
Within the scope of precision agriculture, many applications have been developed to support decision making and yield enhancement. Fruit detection has attracted considerable attention from researchers, and it can be used offline. In contrast, some applications, such as robot vision in orchards, require computer vision models to run on edge devices while performing inferences at high speed. In this area, most modern applications use an integrated graphics processing unit (GPU). In this work, we propose the use of a tensor processing unit (TPU) accelerator with a Raspberry Pi target device and the state-of-the-art, lightweight, and hardware-aware MobileDet detector model. Our contribution is the extension of the possibilities of using accelerators (the TPU) for edge devices in precision agriculture. The proposed method was evaluated using a novel dataset of peaches with three cultivars, which will be made available for further studies. The model achieved an average precision (AP) of 88.2% and a performance of 19.84 frames per second (FPS) at an image size of 640 × 480. The results obtained show that the TPU accelerator can be an excellent alternative for processing on the edge in precision agriculture. Full article
(This article belongs to the Special Issue Advances in Agriculture 4.0)
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20 pages, 4426 KiB  
Article
The Combined Use of UAV-Based RGB and DEM Images for the Detection and Delineation of Orange Tree Crowns with Mask R-CNN: An Approach of Labeling and Unified Framework
by Felipe Lucena, Fabio Marcelo Breunig and Hermann Kux
Future Internet 2022, 14(10), 275; https://doi.org/10.3390/fi14100275 - 27 Sep 2022
Cited by 7 | Viewed by 2200
Abstract
In this study, we used images obtained by Unmanned Aerial Vehicles (UAV) and an instance segmentation model based on deep learning (Mask R-CNN) to evaluate the ability to detect and delineate canopies in high density orange plantations. The main objective of the work [...] Read more.
In this study, we used images obtained by Unmanned Aerial Vehicles (UAV) and an instance segmentation model based on deep learning (Mask R-CNN) to evaluate the ability to detect and delineate canopies in high density orange plantations. The main objective of the work was to evaluate the improvement acquired by the segmentation model when integrating the Canopy Height Model (CHM) as a fourth band to the images. Two models were evaluated, one with RGB images and the other with RGB + CHM images, and the results indicated that the model with combined images presents better results (overall accuracy from 90.42% to 97.01%). In addition to the comparison, this work suggests a more efficient ground truth mapping method and proposes a methodology for mosaicking the results by Mask R-CNN on remotely sensed images. Full article
(This article belongs to the Special Issue Advances in Agriculture 4.0)
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16 pages, 13747 KiB  
Article
Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices
by Khadijeh Alibabaei, Eduardo Assunção, Pedro D. Gaspar, Vasco N. G. J. Soares and João M. L. P. Caldeira
Future Internet 2022, 14(7), 199; https://doi.org/10.3390/fi14070199 - 29 Jun 2022
Cited by 6 | Viewed by 2794
Abstract
The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors [...] Read more.
The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors of production such as seeds, fertilizer, water, etc., accordingly. Vine trunk detection can help create an accurate map of the vineyard that the agricultural robot can rely on to safely navigate and perform a variety of agricultural tasks such as harvesting, pruning, etc. In this work, the state-of-the-art single-shot multibox detector (SSD) with MobileDet Edge TPU and MobileNet Edge TPU models as the backbone was used to detect the tree trunks in the vineyard. Compared to the SSD with MobileNet-V1, MobileNet-V2, and MobileDet as backbone, the SSD with MobileNet Edge TPU was more accurate in inference on the Raspberrypi, with almost the same inference time on the TPU. The SSD with MobileDet Edge TPU achieved the second-best accurate model. Additionally, this work examines the effects of some features, including the size of the input model, the quantity of training data, and the diversity of the training dataset. Increasing the size of the input model and the training dataset increased the performance of the model. Full article
(This article belongs to the Special Issue Advances in Agriculture 4.0)
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