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APRAS-AI-Empowered Self-Adaptive Federation of Platforms for Efficient Economic Collaboration in Rural Areas

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 22573

Special Issue Editors

Dr. Andrea Colantoni

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Guest Editor
Department of Agricultural and Forestry Sciences (DAFNE), Tuscia University of Viterbo, Via San Camillo de Lellis, snc, 01100 Viterbo, Italy
Interests: energy; environment; agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Filippo Gambella

E-Mail Website
Guest Editor
Univeristy of Sassari
Interests: agricultural mechanization; energy; IoT; precision farming and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Spyros Fountas

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
Interests: precision agriculture; UAV; information systems; farm machinery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ivan Andonovic

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Guest Editor
Department of Electrical and Electronic Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Interests: artificial intelligence; internet of things
Special Issues, Collections and Topics in MDPI journals
Dr. Athanasios Rentizelas

E-Mail Website
Guest Editor
Assistant Professor, School of Mechanical Engineering, National Technical University Athens, 15780 Athens, Greece
Interests: biofuel value chains; sustainable supply chain management; supply chain network design optimization; circular economy-enabling supply chains; reverse logistics; zero-emission logistics
Special Issues, Collections and Topics in MDPI journals
Eng. Marcella Ancis

E-Mail Website
Guest Editor
Tiscali S.p.A. Research and Development
Interests: research & education, network planning; project management; research and development and innovation projects; IoT; smart platforms

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) provides a unique opportunity for technology to transform many industries, including the food and agriculture sector. The agrifood sector has a rather low level of uptake of information and communications technology (ICT) and a relatively high cost of data capture. The stack of technologies in IoT includes sensors, actuators, drones, navigation systems, cloud-based data services, and analytics delivering a variety of decision support tools and could significantly change this sector. The potential offered by the integration of new digital technologies (engineering, mechatronics, IT, logistics, communication, etc.) is still largely unexpressed in Europe. This is largely due to the presence of significant fixed costs, typical of network technology systems, which need careful upstream programming to be efficiently distributed among the various stakeholders. Another important element which limits rapid and wide diffusion is represented by the need to simultaneously integrate and develop different areas of knowledge. In fact, both IoT platforms and precision mechanics and remote sensing application technologies require calibration and adaptation to production conditions, which can only be achieved through a combination of organized knowledge and field experimentation.

Dr. Andrea Colantoni
Prof. Filippo Gambella
Prof. Spyros Fountas
Prof. Ivan Andonovic
Prof. Athanasios Rentizelas
Eng. Marcella Ancis
Guest Editors

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Keywords

  • Internet of Things
  • Precisione farming
  • Machine learning
  • Smart platforms
  • Sustainability of resources
  • Optimizing the post-harvest management of the products
  • Traceability of the products
  • Monitoring the optimization in terms of safety and costs of all the processes that underlie an agro-food chain
  • Intelligent marketplaces

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Published Papers (5 papers)

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Research

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20 pages, 3958 KiB  
Article
Enriching IoT Modules with Edge AI Functionality to Detect Water Misuse Events in a Decentralized Manner
by Dimitrios Loukatos, Kalliopi-Agryri Lygkoura, Chrysanthos Maraveas and Konstantinos G. Arvanitis
Sensors 2022, 22(13), 4874; https://doi.org/10.3390/s22134874 - 28 Jun 2022
Cited by 18 | Viewed by 3615
Abstract
The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs of the population on Earth and the degradation of natural resources. Focusing on the “hot” area of natural resource preservation, the recent appearance of more efficient and cheaper [...] Read more.
The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs of the population on Earth and the degradation of natural resources. Focusing on the “hot” area of natural resource preservation, the recent appearance of more efficient and cheaper microcontrollers, the advances in low-power and long-range radios, and the availability of accompanying software tools are exploited in order to monitor water consumption and to detect and report misuse events, with reduced power and network bandwidth requirements. Quite often, large quantities of water are wasted for a variety of reasons; from broken irrigation pipes to people’s negligence. To tackle this problem, the necessary design and implementation details are highlighted for an experimental water usage reporting system that exhibits Edge Artificial Intelligence (Edge AI) functionality. By combining modern technologies, such as Internet of Things (IoT), Edge Computing (EC) and Machine Learning (ML), the deployment of a compact automated detection mechanism can be easier than before, while the information that has to travel from the edges of the network to the cloud and thus the corresponding energy footprint are drastically reduced. In parallel, characteristic implementation challenges are discussed, and a first set of corresponding evaluation results is presented. Full article
(This article belongs to the Special Issue APRAS-AI-Empowered Self-Adaptive Federation of Platforms for Efficient Economic Collaboration in Rural Areas)
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18 pages, 2821 KiB  
Article
Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning
by Aikaterini Kasimati, Borja Espejo-García, Nicoleta Darra and Spyros Fountas
Sensors 2022, 22(9), 3249; https://doi.org/10.3390/s22093249 - 23 Apr 2022
Cited by 26 | Viewed by 3804
Abstract
Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Analysis of [...] Read more.
Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Analysis of laboratory samples is the most popular method for determining the quality characteristics of grapes, although it is time-consuming and expensive. In recent years, several machine learning-based methods have been developed to predict crop quality. Although these techniques require the extensive involvement of experts, automated machine learning (AutoML) offers the possibility to improve this task, saving time and resources. In this paper, we propose an innovative approach for robust prediction of grape quality attributes by combining open-source AutoML techniques and Normalized Difference Vegetation Index (NDVI) data for vineyards obtained from four different platforms-two proximal vehicle-mounted canopy reflectance sensors, orthomosaics from UAV images and Sentinel-2 remote sensing imagery-during the 2019 and 2020 growing seasons. We investigated AutoML, extending our earlier work on manually fine-tuned machine learning methods. Results of the two approaches using Ordinary Least Square (OLS), Theil-Sen and Huber regression models and tree-based methods were compared. Support Vector Machines (SVMs) and Automatic Relevance Determination (ARD) were included in the analysis and different combinations of sensors and data collected over two growing seasons were investigated. Results showed promising performance of Unmanned Aerial Vehicle (UAV) and Spectrosense+ GPS data in predicting grape sugars, especially in mid to late season with full canopy growth. Regression models with both manually fine-tuned ML (R² = 0.61) and AutoML (R² = 0.65) provided similar results, with the latter slightly improved for both 2019 and 2020. When combining multiple sensors and growth stages per year, the coefficient of determination R² improved even more averaging 0.66 for the best-fitting regressions. Also, when considering combinations of sensors and growth stages across both cropping seasons, UAV and Spectrosense+ GPS, as well as Véraison and Flowering, each had the highest average R² values. These performances are consistent with previous work on machine learning algorithms that were manually fine-tuned. These results suggest that AutoML has greater long-term performance potential. To increase the efficiency of crop quality prediction, a balance must be struck between manual expert work and AutoML. Full article
(This article belongs to the Special Issue APRAS-AI-Empowered Self-Adaptive Federation of Platforms for Efficient Economic Collaboration in Rural Areas)
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21 pages, 18015 KiB  
Article
In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment
by Luca Ghiani, Alberto Sassu, Francesca Palumbo, Luca Mercenaro and Filippo Gambella
Sensors 2021, 21(11), 3908; https://doi.org/10.3390/s21113908 - 5 Jun 2021
Cited by 28 | Viewed by 3852
Abstract
An early estimation of the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on manual counting of fruits [...] Read more.
An early estimation of the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on manual counting of fruits or flowers by workers is a time consuming and expensive process and it is not feasible for large fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. In a typical image classification process, the task is not only to specify the presence or absence of a given object on a specific location, while counting how many objects are present in the scene. The success of these tasks largely depends on the availability of a large amount of training samples. This paper presents a detector of bunches of one fruit, grape, based on a deep convolutional neural network trained to detect vine bunches directly on the field. Experimental results show a 91% mean Average Precision. Full article
(This article belongs to the Special Issue APRAS-AI-Empowered Self-Adaptive Federation of Platforms for Efficient Economic Collaboration in Rural Areas)
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9 pages, 3595 KiB  
Communication
Preliminary Investigation on Systems for the Preventive Diagnosis of Faults on Agricultural Operating Machines
by Massimo Cecchini, Francesca Piccioni, Serena Ferri, Gianluca Coltrinari, Leonardo Bianchini and Andrea Colantoni
Sensors 2021, 21(4), 1547; https://doi.org/10.3390/s21041547 - 23 Feb 2021
Cited by 12 | Viewed by 3476
Abstract
This paper aims to investigate failures induced by vibrations on machines, focusing on agricultural ones. The research on literature has brought to light a considerable amount of data on the driven vehicles and not much on the operating machines, including the ones that [...] Read more.
This paper aims to investigate failures induced by vibrations on machines, focusing on agricultural ones. The research on literature has brought to light a considerable amount of data on the driven vehicles and not much on the operating machines, including the ones that we looked for. For this reason, it was decided to direct a survey with the people who work with agricultural machinery every day: operators, sub-contractors, and producers. They were asked about the most frequent breakage, particularly in relation to the rotary harrow, the topic of this work. The questionnaire results showed the types of failures the harrow is most vulnerable to, indicating the times of failure and reparation and the need to set up a potentially useful preventive maintenance supporting system on these machines. Part of the work was then focused on the proposition of a method to investigate bearing failures in the rotary harrow, considering that these have been analyzed in the technical literature and in the survey as the most at-risk components. The proposed method in this work serves as a beginning for the development of a future on board sent-shore-based maintenance system for continuous monitoring of the bearing. Full article
(This article belongs to the Special Issue APRAS-AI-Empowered Self-Adaptive Federation of Platforms for Efficient Economic Collaboration in Rural Areas)
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Review

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21 pages, 357 KiB  
Review
Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture
by Alberto Sassu, Filippo Gambella, Luca Ghiani, Luca Mercenaro, Maria Caria and Antonio Luigi Pazzona
Sensors 2021, 21(3), 956; https://doi.org/10.3390/s21030956 - 1 Feb 2021
Cited by 44 | Viewed by 6302
Abstract
New technologies for management, monitoring, and control of spatio-temporal crop variability in precision viticulture scenarios are numerous. Remote sensing relies on sensors able to provide useful data for the improvement of management efficiency and the optimization of inputs. unmanned aerial systems (UASs) are [...] Read more.
New technologies for management, monitoring, and control of spatio-temporal crop variability in precision viticulture scenarios are numerous. Remote sensing relies on sensors able to provide useful data for the improvement of management efficiency and the optimization of inputs. unmanned aerial systems (UASs) are the newest and most versatile tools, characterized by high precision and accuracy, flexibility, and low operating costs. The work aims at providing a complete overview of the application of UASs in precision viticulture, focusing on the different application purposes, the applied equipment, the potential of technologies combined with UASs for identifying vineyards’ variability. The review discusses the potential of UASs in viticulture by distinguishing five areas of application: rows segmentation and crop features detection techniques; vineyard variability monitoring; estimation of row area and volume; disease detection; vigor and prescription maps creation. Technological innovation and low purchase costs make UASs the core tools for decision support in the customary use by winegrowers. The ability of the systems to respond to the current demands for the acquisition of digital technologies in agricultural fields makes UASs a candidate to play an increasingly important role in future scenarios of viticulture application. Full article
(This article belongs to the Special Issue APRAS-AI-Empowered Self-Adaptive Federation of Platforms for Efficient Economic Collaboration in Rural Areas)
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