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Traceability, namely the ability to access information about a product and its movement across all stages of the supply chain, has been emerged as a key criterion of a product’s quality and safety. Managing fresh products, such as fruits and vegetables, is a particularly complicated task, since they are perishable with short shelf lives and are vulnerable to environmental conditions. This makes traceability of fresh produce very significant. The present study provides a brief overview of the relative literature on fresh produce traceability systems. It was concluded that the commercially available traceability systems usually neither cover the entire length of the supply chain nor rely on open and transparent interoperability standards. Therefore, a user-friendly open access traceability system is proposed for the development of an integrated solution for traceability and agro-logistics of fresh products, focusing on interoperability and data sharing. Various Internet of Things technologies are incorporated and connected to the web, while an android-based platform enables the monitoring of the quality of fruits and vegetables throughout the whole agri-food supply chain, starting from the field level to the consumer and back to the field. The applicability of the system, named AgroTRACE, is further extended to waste management, which constitutes an important aspect of a circular economy.
Aristotelis C. Tagarakis; Lefteris Benos; Dimitrios Kateris; Nikolaos Tsotsolas; Dionysis Bochtis. Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System. Applied Sciences 2021, 11, 7596 .
AMA StyleAristotelis C. Tagarakis, Lefteris Benos, Dimitrios Kateris, Nikolaos Tsotsolas, Dionysis Bochtis. Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System. Applied Sciences. 2021; 11 (16):7596.
Chicago/Turabian StyleAristotelis C. Tagarakis; Lefteris Benos; Dimitrios Kateris; Nikolaos Tsotsolas; Dionysis Bochtis. 2021. "Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System." Applied Sciences 11, no. 16: 7596.
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.
Lefteris Benos; Aristotelis Tagarakis; Georgios Dolias; Remigio Berruto; Dimitrios Kateris; Dionysis Bochtis. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors 2021, 21, 3758 .
AMA StyleLefteris Benos, Aristotelis Tagarakis, Georgios Dolias, Remigio Berruto, Dimitrios Kateris, Dionysis Bochtis. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors. 2021; 21 (11):3758.
Chicago/Turabian StyleLefteris Benos; Aristotelis Tagarakis; Georgios Dolias; Remigio Berruto; Dimitrios Kateris; Dionysis Bochtis. 2021. "Machine Learning in Agriculture: A Comprehensive Updated Review." Sensors 21, no. 11: 3758.
The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research.
Athanasios Anagnostis; Lefteris Benos; Dimitrios Tsaopoulos; Aristotelis Tagarakis; Naoum Tsolakis; Dionysis Bochtis. Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture. Applied Sciences 2021, 11, 2188 .
AMA StyleAthanasios Anagnostis, Lefteris Benos, Dimitrios Tsaopoulos, Aristotelis Tagarakis, Naoum Tsolakis, Dionysis Bochtis. Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture. Applied Sciences. 2021; 11 (5):2188.
Chicago/Turabian StyleAthanasios Anagnostis; Lefteris Benos; Dimitrios Tsaopoulos; Aristotelis Tagarakis; Naoum Tsolakis; Dionysis Bochtis. 2021. "Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture." Applied Sciences 11, no. 5: 2188.
COVID-19 and the restrictive measures towards containing the spread of its infections have seriously affected the agricultural workforce and jeopardized food security. The present study aims at assessing the COVID-19 pandemic impacts on agricultural labor and suggesting strategies to mitigate them. To this end, after an introduction to the pandemic background, the negative consequences on agriculture and the existing mitigation policies, risks to the agricultural workers were benchmarked across the United States’ Standard Occupational Classification system. The individual tasks associated with each occupation in agricultural production were evaluated on the basis of potential COVID-19 infection risk. As criteria, the most prevalent virus transmission mechanisms were considered, namely the possibility of touching contaminated surfaces and the close proximity of workers. The higher risk occupations within the sector were identified, which facilitates the allocation of worker protection resources to the occupations where they are most needed. In particular, the results demonstrated that 50% of the agricultural workforce and 54% of the workers’ annual income are at moderate to high risk. As a consequence, a series of control measures need to be adopted so as to enhance the resilience and sustainability of the sector as well as protect farmers including physical distancing, hygiene practices, and personal protection equipment.
Dionysis Bochtis; Lefteris Benos; Maria Lampridi; Vasso Marinoudi; Simon Pearson; Claus Sørensen. Agricultural Workforce Crisis in Light of the COVID-19 Pandemic. Sustainability 2020, 12, 8212 .
AMA StyleDionysis Bochtis, Lefteris Benos, Maria Lampridi, Vasso Marinoudi, Simon Pearson, Claus Sørensen. Agricultural Workforce Crisis in Light of the COVID-19 Pandemic. Sustainability. 2020; 12 (19):8212.
Chicago/Turabian StyleDionysis Bochtis; Lefteris Benos; Maria Lampridi; Vasso Marinoudi; Simon Pearson; Claus Sørensen. 2020. "Agricultural Workforce Crisis in Light of the COVID-19 Pandemic." Sustainability 12, no. 19: 8212.
An emerging scientific field is the study of safety and ergonomics in the agricultural sector during human-robot interaction. Human-robotic synergetic systems are considered to be the most mature way to circumvent problems appearing due to the complex and unpredictable nature of the agricultural environment, which contrasts with the stable domain found in industrial settings. In promising working ecosystems, the distinctive cognitive human characteristics of perception, decision making and acting can be combined with the strength and repeatable accuracy of robots. However, safety must be guaranteed both in terms of avoiding accidents during unwanted physical contacts and provoking musculoskeletal disorders. The latter is a concise term for describing numerous soft tissues disorders, which have reached epidemic proportions among farmers undermining their quality of life. This investigation, by describing the fundamentals of human-robot interaction from an agriculture-oriented perspective, methodically tries to identify potential hazards that can put human safety at risk. In order to overcome these hazards, approaches for minimising the occurrence of injuries analysed along with methods for safe collaboration. The innovation of this study lies on focusing on ergonomics during agricultural human-robot interactive operations. Thus, through reviewing the basic ergonomic principles and the main risk factors, potential challenges are captured concerning human factors, technologies and policy directions. Ensuring of safety in this kind of systems should have a positive impact in technological, societal and economic aspects. For this purpose, an intensive effort and interdisciplinary collaboration are required to establish a sustainable anthropocentric human-robot interactive ecosystem.
Lefteris Benos; Avital Bechar; Dionysis Bochtis. Safety and ergonomics in human-robot interactive agricultural operations. Biosystems Engineering 2020, 200, 55 -72.
AMA StyleLefteris Benos, Avital Bechar, Dionysis Bochtis. Safety and ergonomics in human-robot interactive agricultural operations. Biosystems Engineering. 2020; 200 ():55-72.
Chicago/Turabian StyleLefteris Benos; Avital Bechar; Dionysis Bochtis. 2020. "Safety and ergonomics in human-robot interactive agricultural operations." Biosystems Engineering 200, no. : 55-72.
Background: Musculoskeletal disorders (MSDs) have long been recognized as the most common risks that operation of agricultural machineries poses, thus, undermining the ability to labor and quality of life. The purpose of this investigation was to thoroughly review the recent scholarly literature on ergonomics in agricultural mechanized operations; Methods: Electronic database research over the last ten years was conducted based on specific inclusion criteria. Furthermore, an assessment of the methodological quality and strength of evidence of potential risk factors causing MSDs was performed; Results: The results demonstrated that ergonomics in agriculture is an interdisciplinary topic and concerns both developed and developing countries. The machines with driving seats seem to be associated with painful disorders of the low back, while handheld machines with disorders of the upper extremities. The main roots of these disorders are the whole-body vibration (WBV) and hand-arm transmitted vibration (HATV). However, personal characteristics, awkward postures, mechanical shocks and seat discomfort were also recognized to cause MSDs; Conclusions: The present ergonomic interventions aim mainly at damping of vibrations and improving the comfort of operator. Nevertheless, more collaborative efforts among physicians, ergonomists, engineers and manufacturers are required in terms of both creating new ergonomic technologies and increasing the awareness of workers for the involved risk factors.
Lefteris Benos; Dimitrios Tsaopoulos; Dionysis Bochtis. A Review on Ergonomics in Agriculture. Part II: Mechanized Operations. Applied Sciences 2020, 10, 3484 .
AMA StyleLefteris Benos, Dimitrios Tsaopoulos, Dionysis Bochtis. A Review on Ergonomics in Agriculture. Part II: Mechanized Operations. Applied Sciences. 2020; 10 (10):3484.
Chicago/Turabian StyleLefteris Benos; Dimitrios Tsaopoulos; Dionysis Bochtis. 2020. "A Review on Ergonomics in Agriculture. Part II: Mechanized Operations." Applied Sciences 10, no. 10: 3484.
Background: Agriculture involves several harmful diseases. Among the non-fatal ones, musculoskeletal disorders (MSDs) are the most prevalent, as they have reached epidemic proportions. The main aim of this investigation is to systematically review the major risk factors regarding MSDs as well as evaluate the existing ergonomic interventions. Methods: The search engines of Google Scholar, PubMed, Scopus, and ScienceDirect were used to identify relevant articles during the last decade. The imposed exclusive criteria assured the accuracy and current progress in this field. Results: It was concluded that MSDs affect both developed and developing countries, thus justifying the existing global concern. Overall, the most commonly studied task was harvesting, followed by load carrying, pruning, planting, and other ordinary manual operations. Repetitive movements in awkward postures, such as stooping and kneeling; individual characteristics; as well as improper tool design were observed to contribute to the pathogenesis of MSDs. Furthermore, low back disorders were reported as the main disorder. Conclusions: The present ergonomic interventions seem to attenuate the MSDs to a great extent. However, international reprioritization of the safety and health measures is required in agriculture along with increase of the awareness of the risk factors related to MSDs.
Lefteris Benos; Dimitrios Tsaopoulos; Dionysis Bochtis. A Review on Ergonomics in Agriculture. Part I: Manual Operations. Applied Sciences 2020, 10, 1905 .
AMA StyleLefteris Benos, Dimitrios Tsaopoulos, Dionysis Bochtis. A Review on Ergonomics in Agriculture. Part I: Manual Operations. Applied Sciences. 2020; 10 (6):1905.
Chicago/Turabian StyleLefteris Benos; Dimitrios Tsaopoulos; Dionysis Bochtis. 2020. "A Review on Ergonomics in Agriculture. Part I: Manual Operations." Applied Sciences 10, no. 6: 1905.
The studies dealing with micropolar magnetohydrodynamic (MHD) flows usually ignore the micromagnetorotation (MMR) effect, by assuming that magnetization and magnetic field vectors are parallel. The main objective of the present investigation is to measure the effect of MMR and the possible differences encountered by ignoring it. The MHD planar Couette micropolar flow is solved analytically considering and by ignoring the MMR effect. Subsequently, the influence of MMR on the velocity and microrotation fields as well as skin friction coefficient, is evaluated for various micropolar size and electric effect parameters and Hartmann numbers. It is concluded that depending on the parameters’ combination, as MMR varies, the fluid flow may accelerate, decelerate, or even excite a mixed pattern along the channel height. Thus, the MMR term is a side mechanism, other than the Lorentz force, that transfers or dissipates magnetic energy in the flow direct through microrotation. Acceleration or deceleration of the velocity from 4% to even up to 45% and almost 15% deviation of the skin friction were measured when MMR was considered. The crucial effect of the micromagnetorotation term, which is usually ignored, should be considered for the future design of industrial and bioengineering applications.
Kyriaki-Evangelia Aslani; Lefteris Benos; Efstratios Tzirtzilakis; Ioannis E. Sarris. Micromagnetorotation of MHD Micropolar Flows. Symmetry 2020, 12, 148 .
AMA StyleKyriaki-Evangelia Aslani, Lefteris Benos, Efstratios Tzirtzilakis, Ioannis E. Sarris. Micromagnetorotation of MHD Micropolar Flows. Symmetry. 2020; 12 (1):148.
Chicago/Turabian StyleKyriaki-Evangelia Aslani; Lefteris Benos; Efstratios Tzirtzilakis; Ioannis E. Sarris. 2020. "Micromagnetorotation of MHD Micropolar Flows." Symmetry 12, no. 1: 148.
Water quality problems are a persistent global issue since population growth has continually stressed hydrological resources. Heavy metals released into the environment from plating plants, mining, and alloy manufacturing pose a significant threat to the public health. A possible solution for water purification from heavy metals is to capture them by using nanoparticles in micromixers. In this method, conventionally heavy metal capture is achieved by effectively mixing two streams, a particle solution and the contaminated water, under the action of external magnetic fields. In the present study, we investigated the effective mixing of iron oxide nanoparticles and water without the use of external magnetic fields. For this reason, the mixing of particles and the contaminated water was studied for various inlet velocity ratios and inflow angles of the two streams using computational fluid dynamics techniques. The Navier-Stokes equations were solved for the water flow, the discrete motion of particles was evaluated by a Lagrangian method, while the flow of substances of the contaminated water was studied by a scalar transport equation. Results showed that as the velocity ratio between the inlet streams increased, the mixing of particles with the contaminated water was increased. Therefore, nanoparticles were more uniformly distributed in the duct and efficiently absorbed the substances of the contaminated water. On the other hand, the angle between two streams was found to play an insignificant role in the mixing process. Consequently, the results from this study could be used in the design of more compact and cost efficient micromixer devices.
Evangelos Karvelas; Christos Liosis; Lefteris Benos; Theodoros Karakasidis; Ioannis Sarris. Micromixing Efficiency of Particles in Heavy Metal Removal Processes under Various Inlet Conditions. Water 2019, 11, 1135 .
AMA StyleEvangelos Karvelas, Christos Liosis, Lefteris Benos, Theodoros Karakasidis, Ioannis Sarris. Micromixing Efficiency of Particles in Heavy Metal Removal Processes under Various Inlet Conditions. Water. 2019; 11 (6):1135.
Chicago/Turabian StyleEvangelos Karvelas; Christos Liosis; Lefteris Benos; Theodoros Karakasidis; Ioannis Sarris. 2019. "Micromixing Efficiency of Particles in Heavy Metal Removal Processes under Various Inlet Conditions." Water 11, no. 6: 1135.