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Workers’ Opinions on Using the Internet of Things to Enhance the Performance of the Olive Oil Industry: A Machine Learning Approach. Processes (Basel) 2023. [DOI: 10.3390/pr11010271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things (IoT) applications in the olive oil industry are almost limitless. Although previous studies have investigated the impact of the IoT on the performance of industries, this issue has yet to be explored in the olive oil industry. In this study we aimed to develop a new model to investigate the factors influencing supply chain improvement in olive oil companies. The model was used to evaluate the relationship between supply chain improvement and olive oil companies’ performance. Demand planning, manufacturing, transportation, customer service, warehousing, and inventory management were the main factors incorporated into the proposed model. Self-organizing map (SOM) clustering and decision trees were employed in the development of the method. The data were collected from respondents with knowledge related to integrating new technologies into the industry. The results demonstrated that IoT implementation in olive oil companies significantly improved their performance. Moreover, it was found that there was a positive relationship between supply chain improvements via IoT implementation in olive oil companies and their performance.
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Hassoun A, Cropotova J, Trollman H, Jagtap S, Garcia-Garcia G, Parra-López C, Nirmal N, Özogul F, Bhat Z, Aït-Kaddour A, Bono G. Use of industry 4.0 technologies to reduce and valorize seafood waste and by-products: A narrative review on current knowledge. Curr Res Food Sci 2023; 6:100505. [PMID: 37151380 PMCID: PMC10160358 DOI: 10.1016/j.crfs.2023.100505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/07/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Fish and other seafood products represent a valuable source of many nutrients and micronutrients for the human diet and contribute significantly to global food security. However, considerable amounts of seafood waste and by-products are generated along the seafood value and supply chain, from the sea to the consumer table, causing severe environmental damage and significant economic loss. Therefore, innovative solutions and alternative approaches are urgently needed to ensure a better management of seafood discards and mitigate their economic and environmental burdens. The use of emerging technologies, including the fourth industrial revolution (Industry 4.0) innovations (such as Artificial Intelligence, Big Data, smart sensors, and the Internet of Things, and other advanced technologies) to reduce and valorize seafood waste and by-products could be a promising strategy to enhance blue economy and food sustainability around the globe. This narrative review focuses on the issues and risks associated with the underutilization of waste and by-products resulting from fisheries and other seafood industries. Particularly, recent technological advances and digital tools being harnessed for the prevention and valorization of these natural invaluable resources are highlighted.
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Affiliation(s)
- Abdo Hassoun
- Univ. Littoral Côte D’Opale, UMRt 1158 BioEcoAgro, USC ANSES, INRAe, Univ. Artois, Univ. Lille, Univ. Picardie Jules Verne, Univ. Liège, Junia, F-62200, Boulogne-sur-Mer, France
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Corresponding author. Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France.
| | - Janna Cropotova
- Department of Biological Sciences, Ålesund, Norwegian University of Science and Technology, Larsgårdsvegen 4, 6025, Ålesund, Norway
- Corresponding author.
| | - Hana Trollman
- School of Business, University of Leicester, Leicester, LE2 1RQ, UK
| | - Sandeep Jagtap
- Sustainable Manufacturing Systems Centre, School of Aerospace, Transport & Manufacturing, Cranfield University, Cranfield, MK43 0AL, UK
| | - Guillermo Garcia-Garcia
- Department of Agrifood System Economics, Centre ‘Camino de Purchil’, Institute of Agricultural and Fisheries Research and Training (IFAPA), P.O. Box 2027, 18080, Granada, Spain
| | - Carlos Parra-López
- Department of Agrifood System Economics, Centre ‘Camino de Purchil’, Institute of Agricultural and Fisheries Research and Training (IFAPA), P.O. Box 2027, 18080, Granada, Spain
| | - Nilesh Nirmal
- Institute of Nutrition, Mahidol University, 999 Phutthamonthon 4 Road, Salaya, Phutthamonthon, Nakhon Pathom, 73170, Thailand
| | - Fatih Özogul
- Department of Seafood Processing Technology, Faculty of Fisheries, Cukurova University, 01330, Balcali, Adana, Turkey
| | - Zuhaib Bhat
- Division of Livestock Products Technology, SKUAST-Jammu, Jammu, 181102, J&K, India
| | | | - Gioacchino Bono
- Institute for Biological Resources and Marine Biotechnologies, National Research Council (IRBIM-CNR), Mazara Del Vallo, Italy
- Dipartimento di Scienze e Technologie Biologiche, Chimiche e Farmaceutiche (STEBICEF), Università Di Palermo, Viale Delle Scienze, 90128, Palermo, Italy
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Kyaw KS, Adegoke SC, Ajani CK, Nwabor OF, Onyeaka H. Toward in-process technology-aided automation for enhanced microbial food safety and quality assurance in milk and beverages processing. Crit Rev Food Sci Nutr 2022; 64:1715-1735. [PMID: 36066463 DOI: 10.1080/10408398.2022.2118660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to achieve acceptable food quality and safety. This notwithstanding, due to human limitations, processed foods are often contaminated either with microorganisms, microbial byproducts, or chemical agents, resulting in the compromise of product quality with far-reaching consequences including foodborne diseases, food intoxication, and food recall. Transitioning from manual food processing to automation-aided food processing (smart food processing) which is guided by artificial intelligence will guarantee the safety and quality of food. However, this will require huge investments in terms of resources, technologies, and expertise. This study reviews the potential of artificial intelligence in food processing. In addition, it presents the technologies and methods with potential applications in implementing automated technology-aided processing. A conceptual design for an automated food processing line comprised of various operational layers and processes targeted at enhancing the microbial safety and quality assurance of liquid foods such as milk and beverages is elaborated.
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Affiliation(s)
- Khin Sandar Kyaw
- Department of International Business Management, Didyasarin International College, Hatyai University, Songkhla, Thailand
| | - Samuel Chetachukwu Adegoke
- Joint School of Nanoscience and Nanoengineering, Department of Nanoscience, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
| | - Clement Kehinde Ajani
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Ozioma Forstinus Nwabor
- Infectious Disease Unit, Department of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
- Center of Antimicrobial Biomaterial Innovation-Southeast Asia and Natural Product Research Center of Excellence, Faculty of Science, Prince of Songkla University, Songkhla, Thailand
| | - Helen Onyeaka
- School of Chemical Engineering, University of Birmingham, Edgbaston, United Kingdom
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Hassoun A, Alhaj Abdullah N, Aït-Kaddour A, Ghellam M, Beşir A, Zannou O, Önal B, Aadil RM, Lorenzo JM, Mousavi Khaneghah A, Regenstein JM. Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies. Crit Rev Food Sci Nutr 2022; 64:873-889. [PMID: 35950635 DOI: 10.1080/10408398.2022.2110033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.
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Affiliation(s)
- Abdo Hassoun
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Syrian Academic Expertise (SAE), Gaziantep, Turkey
| | | | | | - Mohamed Ghellam
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Ayşegül Beşir
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Oscar Zannou
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Begüm Önal
- Gourmet International Ltd, Izmir, Turkey
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Jose M Lorenzo
- Centro Tecnológico de la Carne de Galicia, Ourense, Spain
| | - Amin Mousavi Khaneghah
- Department of Fruit and Vegetable Product Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology - State Research Institute, Warsaw, Poland
| | - Joe M Regenstein
- Department of Food Science, Cornell University, Ithaca, New York, USA
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5
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Dictyocaulus viviparus bulk tank milk seropositivity is correlated with meteorological variables. Int J Parasitol 2022; 52:659-665. [DOI: 10.1016/j.ijpara.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/14/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022]
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Lamberty A, Kreyenschmidt J. Ambient Parameter Monitoring in Fresh Fruit and Vegetable Supply Chains Using Internet of Things-Enabled Sensor and Communication Technology. Foods 2022; 11:foods11121777. [PMID: 35741974 PMCID: PMC9222862 DOI: 10.3390/foods11121777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/07/2022] [Accepted: 06/09/2022] [Indexed: 11/16/2022] Open
Abstract
Up to half of the global fruit and vegetable production is wasted or lost along the supply chain, causing wastage of resources and economic losses. Ambient parameters strongly influence quality and shelf life of fresh fruit and vegetables. Monitoring these parameters by using Internet of things (IoT)-enabled sensor and communication technology in supply chains can help to optimize product qualities and hence reduce product rejections and losses. Various corresponding technical solutions are available, but the diverse characteristics of fresh plant-based produce impede establishing valuable applications. Therefore, the aim of this review is to give an overview of IoT-enabled sensor and communication technology in relation to the specific quality and spoilage characteristics of fresh fruit and vegetables. Temperature, relative humidity (RH), O2, CO2 and vibration/shock are ambient parameters that provide most added value regarding product quality optimization, and can be monitored by current IoT-enabled sensor technology. Several wireless communication technologies are available for real-time data exchange and subsequent data processing and usage. Although many studies investigate the general possibility of monitoring systems using IoT-enabled technology, large-scale implementation in fresh fruit and vegetable supply chains is still hindered by unsolved challenges.
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Affiliation(s)
- Anna Lamberty
- Department of Fresh Produce Logistics, Hochschule Geisenheim University, 65366 Geisenheim, Germany;
- Projects and Innovation Department, Euro Pool System International (Deutschland) GmbH, 53332 Bornheim, Germany
- Correspondence:
| | - Judith Kreyenschmidt
- Department of Fresh Produce Logistics, Hochschule Geisenheim University, 65366 Geisenheim, Germany;
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Hakim GPN, Habaebi MH, Toha SF, Islam MR, Yusoff SHB, Adesta EYT, Anzum R. Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle, and Open Dirt Road Environments. SENSORS 2022; 22:s22093267. [PMID: 35590957 PMCID: PMC9101881 DOI: 10.3390/s22093267] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 04/21/2022] [Accepted: 04/21/2022] [Indexed: 01/09/2023]
Abstract
In Wireless Sensor Networks which are deployed in remote and isolated tropical areas; such as forest; jungle; and open dirt road environments; wireless communications usually suffer heavily because of the environmental effects on vegetation; terrain; low antenna height; and distance. Therefore; to solve this problem; the Wireless Sensor Network communication links must be designed for their best performance using the suitable electromagnetic wave behavior model in a given environment. This study introduces and analyzes the behavior of the LoRa pathloss propagation model for signals that propagate at near ground or that have low transmitter and receiver antenna heights from the ground (less than 30 cm antenna height). Using RMSE and MAE statistical analysis tools; we validate the developed model results. The developed Fuzzy ANFIS model achieves the lowest RMSE score of 0.88 at 433 MHz and the lowest MAE score of 1.61 at 433 MHz for both open dirt road environments. The Optimized FITU-R Near Ground model achieved the lowest RMSE score of 4.08 at 868 MHz for the forest environment and lowest MAE score of 14.84 at 868 MHz for the open dirt road environment. The Okumura-Hata model achieved the lowest RMSE score of 6.32 at 868 MHz and the lowest MAE score of 26.12 at 868 MHz for both forest environments. Finally; the ITU-R Maximum Attenuation Free Space model achieved the lowest RMSE score of 9.58 at 868 MHz for the forest environment and the lowest MAE score of 38.48 at 868 MHz for the jungle environment. These values indicate that the proposed Fuzzy ANFIS pathloss model has the best performance in near ground propagation for all environments compared to other benchmark models.
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Affiliation(s)
- Galang P. N. Hakim
- Department of Electrical Engineering, Faculty of Engineering, Universitas Mercu Buana, Jakarta 11650, Indonesia;
- Department of Electrical and Computer Engineering, Kulliyyah of Engineering (KOE), International Islamic University Malaysia (IIUM), Kuala Lumpur 53100, Malaysia; (M.H.H.); (M.R.I.); (S.H.B.Y.); (R.A.)
| | - Mohamed Hadi Habaebi
- Department of Electrical and Computer Engineering, Kulliyyah of Engineering (KOE), International Islamic University Malaysia (IIUM), Kuala Lumpur 53100, Malaysia; (M.H.H.); (M.R.I.); (S.H.B.Y.); (R.A.)
| | - Siti Fauziah Toha
- Department of Mechatronics, Kulliyyah of Engineering (KOE), International Islamic University Malaysia (IIUM), Kuala Lumpur 53100, Malaysia
- Correspondence:
| | - Mohamed Rafiqul Islam
- Department of Electrical and Computer Engineering, Kulliyyah of Engineering (KOE), International Islamic University Malaysia (IIUM), Kuala Lumpur 53100, Malaysia; (M.H.H.); (M.R.I.); (S.H.B.Y.); (R.A.)
| | - Siti Hajar Binti Yusoff
- Department of Electrical and Computer Engineering, Kulliyyah of Engineering (KOE), International Islamic University Malaysia (IIUM), Kuala Lumpur 53100, Malaysia; (M.H.H.); (M.R.I.); (S.H.B.Y.); (R.A.)
| | - Erry Yulian Triblas Adesta
- Department of Industrial Engineering Safety and Health, Faculty of Engineering, Universitas Indo Global Mandiri (UIGM), Palembang 30129, Indonesia;
| | - Rabeya Anzum
- Department of Electrical and Computer Engineering, Kulliyyah of Engineering (KOE), International Islamic University Malaysia (IIUM), Kuala Lumpur 53100, Malaysia; (M.H.H.); (M.R.I.); (S.H.B.Y.); (R.A.)
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Abstract
The continuously rising interest in chemical sensors’ applications in environmental monitoring, for soil analysis in particular, is owed to the sufficient sensitivity and selectivity of these analytical devices, their low costs, their simple measurement setups, and the possibility to perform online and in-field analyses with them. In this review the recent advances in chemical sensors for soil analysis are summarized. The working principles of chemical sensors involved in soil analysis; their benefits and drawbacks; and select applications of both the single selective sensors and multisensor systems for assessments of main plant nutrition components, pollutants, and other important soil parameters (pH, moisture content, salinity, exhaled gases, etc.) of the past two decades with a focus on the last 5 years (from 2017 to 2021) are overviewed.
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Alves L, Ferreira Cruz E, Lopes SI, Faria PM, Rosado da Cruz AM. Towards circular economy in the textiles and clothing value chain through blockchain technology and IoT: A review. WASTE MANAGEMENT & RESEARCH : THE JOURNAL OF THE INTERNATIONAL SOLID WASTES AND PUBLIC CLEANSING ASSOCIATION, ISWA 2022; 40:3-23. [PMID: 34708680 PMCID: PMC8832563 DOI: 10.1177/0734242x211052858] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
The textile and clothing industry sector has today a big environmental impact, not only due to the consumption of water and the use of toxic chemicals but also due to the increasing levels of textile waste. One way to reduce the problem is to circularise the, currently linear, textile and clothing value chain, by using discarded clothes as raw material for the production of new clothes, transforming it into a model of circular economy. This way, while reducing the need to produce new raw materials (e.g. cotton), the problem of textile waste produced is also reduced, thus contributing to a more sustainable industry. In this article, we review the current approaches for traceability in the textile and clothing value chain, and study a set of technologies we deem essential for promoting the circular economy in this value chain - namely, the blockchain technology - for registering activities on traceable items through the value chain, and the Internet of Things (IoT) technology, for easily identifying the traceable items' digital twins.
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Affiliation(s)
- Luís Alves
- IPVC – Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
| | - Estrela Ferreira Cruz
- IPVC – Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
- Algoritmi Research Centre, Escola de Engenharia, Universidade do Minho, Guimarães, Portugal
| | - Sérgio I Lopes
- IPVC – Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
- IT – Instituto de Telecomunicações, Aveiro, Portugal
| | - Pedro M Faria
- IPVC – Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
| | - António Miguel Rosado da Cruz
- IPVC – Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
- Algoritmi Research Centre, Escola de Engenharia, Universidade do Minho, Guimarães, Portugal
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10
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A Sensor-Based Drone for Pollutants Detection in Eco-Friendly Cities: Hardware Design and Data Analysis Application. ELECTRONICS 2021. [DOI: 10.3390/electronics11010052] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The increase in produced waste is a symptom of inefficient resources usage, which should be better exploited as a resource for energy and materials. The air pollution generated by waste causes impacts felt by a large part of the population living in and around the main urban areas. This paper presents a mobile sensor node for monitoring air and noise pollution; indeed, the developed system is installed on an RC drone, quickly monitoring large areas. It relies on a Raspberry Pi Zero W board and a wide set of sensors (i.e., NO2, CO, NH3, CO2, VOCs, PM2.5, and PM10) to sample the environmental parameter at regular time intervals. A proper classification algorithm was developed to quantify the traffic level from the noise level (NL) acquired by the onboard microphone. Additionally, the drone is equipped with a camera and implements a visual recognition algorithm (Fast R-CNN) to detect waste fires and mark them by a GPS receiver. Furthermore, the firmware for managing the sensing unit operation was developed, as well as the power supply section. In particular, the node’s consumption was analysed in two use cases, and the battery capacity needed to power the designed device was sized. The onfield tests demonstrated the proper operation of the developed monitoring system. Finally, a cloud application was developed to remotely monitor the information acquired by the sensor-based drone and upload them on a remote database.
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Digitalization and Future Agro-Food Supply Chain Management: A Literature-Based Implications. SUSTAINABILITY 2021. [DOI: 10.3390/su132112181] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Achieving transition towards sustainable and resilient food systems is a critical issue on the current societal agenda. This study examined the potential contribution of digitalization of the food system to such transition by reviewing 76 relevant journal articles, indexed on the Scopus database, using the integrative literature review approach and descriptive content analysis with MAXQDA 2020 software. ‘Blockchain’ was the top hit among keywords and main concepts applied to the food system. The UK as a country and Europe as a continent were found to lead the scientific research on food system digitalization. Use of digital technologies such as blockchain, the Internet of Things, big-data analytics, artificial intelligence, and related information and communications technologies were identified as enablers. Traceability, sustainability, resilience to crises such as the COVID-19 pandemic, and reducing food waste were among the key benefit areas associated with digitalization for different food commodities. Challenges to practical applications related to infrastructure and cost, knowledge and skill, law and regulations, the nature of the technologies, and the nature of the food system were identified. Developing policies and regulations, supporting infrastructure development, and educating and training people could facilitate fuller digitalization of the food system.
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II RSC, Lauguico SC, Alejandrino JD, Bandala AA, Sybingco E, Vicerra RRP, Dadios EP, Cuello JL. Adaptive Fertigation System Using Hybrid Vision-Based Lettuce Phenotyping and Fuzzy Logic Valve Controller Towards Sustainable Aquaponics. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2021. [DOI: 10.20965/jaciii.2021.p0610] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Sustainability is a major challenge in any plant factory, particularly those involving precision agriculture. In this study, an adaptive fertigation system in a three-tier nutrient film technique aquaponic system was developed using a non-destructive vision-based lettuce phenotype (VIPHLET) model integrated with an 18-rule Mamdani fuzzy inference system for nutrient valve control. Four lettuce phenes, that is, fresh weight, chlorophylls a and b, and vitamin C concentrations as outputted by the genetic programming-based VIPHLET model were optimized for each growth stage by injecting NPK nutrients into the mixing tank, as determined based on leaf canopy signatures. This novel adaptive fertigation system resulted in higher nutrient use efficiency (99.678%) and lower chemical waste emission (14.108 mg L-1) than that by manual fertigation (92.468%, 178.88 mg L-1). Overall, it can improve agricultural malpractices in relation to sustainable agriculture.
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Ramalingam B, Mohan RE, Pookkuttath S, Gómez BF, Sairam Borusu CSC, Wee Teng T, Tamilselvam YK. Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT. SENSORS 2020; 20:s20185280. [PMID: 32942750 PMCID: PMC7571233 DOI: 10.3390/s20185280] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/09/2020] [Accepted: 09/11/2020] [Indexed: 01/08/2023]
Abstract
Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.
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Affiliation(s)
- Balakrishnan Ramalingam
- Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (R.E.M.); (S.P.); (B.F.G.); (C.S.C.S.B.); (T.W.T.)
- Correspondence:
| | - Rajesh Elara Mohan
- Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (R.E.M.); (S.P.); (B.F.G.); (C.S.C.S.B.); (T.W.T.)
| | - Sathian Pookkuttath
- Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (R.E.M.); (S.P.); (B.F.G.); (C.S.C.S.B.); (T.W.T.)
| | - Braulio Félix Gómez
- Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (R.E.M.); (S.P.); (B.F.G.); (C.S.C.S.B.); (T.W.T.)
| | - Charan Satya Chandra Sairam Borusu
- Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (R.E.M.); (S.P.); (B.F.G.); (C.S.C.S.B.); (T.W.T.)
| | - Tey Wee Teng
- Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (R.E.M.); (S.P.); (B.F.G.); (C.S.C.S.B.); (T.W.T.)
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