Computer vision applied to food and agricultural products

Fracarolli, Juliana Aparecida; Pavarin, Fernanda Fernandes Adimari; Castro, Wilson; Blasco, José

doi:10.5935/1806-6690.20200087

Cited by 16 publications

(14 citation statements)

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“…Other applications exploit ML and AI solutions to detect the presence of weeds and diseases. This allows the targeted use of pesticides and chemical agents (Fracarolli et al. , 2020; Kamilaris and Prenafeta-Boldú, 2018; Mavridou et al.…”

Section: Discussionmentioning

confidence: 99%

“…, 2017; Zhang and Kovacs, 2012). Machine learning and AI solutions can also be used to simplify product counting and identification or to promptly detect the insurgence of pests and diseases (Fracarolli et al. , 2020; Liakos et al.…”

Section: Discussionmentioning

confidence: 99%

“…For these reasons, the most useful I4.0 technologies for this type of supply chain focus on resource management. These include IoT systems, ML, AI and Big Data for precision agriculture applications such as determining the water needs of crops and the targeted use of natural inputs and chemical agents (Fracarolli et al, 2020;Queiroz et al, 2020). The use of such technologies would help generate savings for farmers and reduce the environmental impact of production.…”

Section: Technologies Adoption In Different Agri-food Supply Chain Co...mentioning

confidence: 99%

“…Other applications exploit ML and AI solutions to detect the presence of weeds and diseases. This allows the targeted use of pesticides and chemical agents (Fracarolli et al, 2020;Kamilaris and Prenafeta-Bold u, 2018;Mavridou et al, 2019). These technologies are particularly useful in mass monoculture production, to obtain a significant reduction in input utilization and generate meaningful savings.…”

Section: Technologies Utilization In the Agri-food Industrymentioning

confidence: 99%

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Technological innovation in agri-food supply chains

Cricelli

Mauriello

Strazzullo

2022

BFJ

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PurposeThis study aims to analyse how the adoption of Industry 4.0 technologies can help different types of agri-food supply chains introduce and manage innovations in response to the challenges and opportunities that emerged following the COVID-19 pandemic.Design/methodology/approachA systematic literature review methodology was used to bring together the most relevant contributions from different disciplines and provide comprehensive results on the use of I4.0 technologies in the agri-food industry.FindingsFour technological clusters are identified, which group together the I4.0 technologies based on the applications in the agri-food industry, the objectives and the advantages provided. In addition, three types of agri-food supply chains have been identified and their configuration and dynamics have been studied. Finally, the I4.0 technologies most suited for each type of supply chain have been identified, and suggestions on how to effectively introduce and manage innovations at different levels of the supply chain are provided.Originality/valueThe study highlights how the effective adoption of I4.0 technologies in the agri-food industry depends on the characteristics of the supply chains. Technologies can be used for different purposes and managers should carefully consider the objectives to be achieved and the synergies between technologies and supply chain dynamics.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Technologies Adoption In Different Agri-food Supply Chain Co...mentioning

confidence: 99%

Section: Technologies Utilization In the Agri-food Industrymentioning

confidence: 99%

See 2 more Smart Citations

Technological innovation in agri-food supply chains

Cricelli

Mauriello

Strazzullo

2022

BFJ

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“…The use of some earlier unusual methods, such as images, does not obviously improve energy-related issues, but there are also some indirect implications, in addition to quality checking and process improvement by applying computer vision [74][75][76][77][78][79], such as other machine learning algorithms, e.g., kNN and random forest regression [80].…”

Section: Energy Efficiency Issues Of Dryingmentioning

confidence: 99%

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

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Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes.

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