Recent advances and applications of artificial intelligence and related technologies in the food industry

Addanki, Mounika; Patra, Priyanka; Kandra, Prameela

doi:10.1016/j.afres.2022.100126

Cited by 60 publications

(28 citation statements)

References 40 publications

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“…These systems can enable the measurements of quality parameters of products due to image acquisition, segmentation, feature extraction, classification, and data interpretation [ 26 ]. Computer vision as a part of AI allows the classification of fruit based on quality and identification of damages [ 27 ]. Fruit classification using machine vision using appropriate image features and learning algorithms can result in achieving the maximum economic value of fruit efficiently and accurately [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)

Ropelewska

2022

Foods

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The objective of this study was to assess the influence of storage under different storage conditions on black currant quality in a non-destructive and inexpensive manner using image processing and artificial intelligence. Black currants were stored at a room temperature of 20 ± 1 °C and a temperature of 3 °C (refrigerator). The images of black currants directly after harvest and fruit stored for one and two weeks were obtained using a digital camera. Then, texture parameters were computed from the images converted to color channels R (red), G (green), B (blue), L (lightness component from black to white), a (green for negative and red for positive values), b (blue for negative and yellow for positive values), X (component with color information), Y (lightness), and Z (component with color information). Models for the classification of black currants were built using various machine learning algorithms based on selected textures for RGB, Lab, and XYZ color spaces. Models built using the IBk, multilayer perceptron, and multiclass classifier for textures from RGB color space, and the IBk algorithm for textures from Lab color space distinguished unstored black currants and samples stored in the room for one and two weeks with an average accuracy of 100%, and the kappa statistic and weighted averages of precision, recall, Matthews correlation coefficient (MCC), receiver operating characteristic (ROC) area, and precision–recall (PRC) area equal to 1.000. This indicated a very distinct change in the external structure of the fruit after the first week and more and more visible changes in quality with increasing storage time. A classification accuracy reaching 98.67% (multilayer perceptron, Lab color space) for the samples stored in the refrigerator may indicate smaller quality changes caused by storage at a low temperature. The approach combining image textures and artificial intelligence turned out to be promising to monitor the quality changes in black currants during storage.

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Section: Introductionmentioning

confidence: 99%

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)

Ropelewska

2022

Foods

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“…Technologies help consumers be more mindful of their food choices and minimise the amount of waste generated (Addanki et al. , 2022; Al-Obadi et al.…”

Section: Discussionmentioning

confidence: 99%

“…The IoT helps monitor and track the expiration dates of food items and receive alerts. AI and ML help to analyse sales data, weather patterns, and inventory management systems to predict food items that are likely to be unsold (Addanki et al. , 2022).…”

Section: Discussionmentioning

confidence: 99%

“…5.1.4 Cluster 4: technological factors. Technologies help consumers be more mindful of their food choices and minimise the amount of waste generated (Addanki et al, 2022;Al-Obadi et al, 2022;Kowalewska and Kołłajtis-Dołowy, 2018). Technologies such as the Internet of Things (IoT), devices connected to artificial intelligence (AI), machine learning (ML), blockchain, online grocery shopping, and biodegradable packaging help organisations and consumers in multiple ways to reduce waste and effectively use the supply chain.…”

Section: Summary Of Findingsmentioning

confidence: 99%

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Food wastage and consumerism in circular economy: a review and research directions

Agarwal,

Srivastava,

Gupta

et al. 2024

BFJ

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PurposeConsidering food waste as a global problem resulting from the wastage of valuable resources that could fulfil the requirements of malnourished people, the current research focusses on understanding consumerism’s impact on this phenomenon. Additionally, the circular economy (CE) approach can be critical in reducing food waste and promoting sustainability.Design/methodology/approachA systematic literature review was conducted using bibliometrics and network analysis. The study reviewed 326 articles within 10 years, from 2013 to 2023.FindingsThe findings reveal four prominent factors – behavioural, environmental, socioeconomic and technological – in managing food waste (FW). Reducing FW at a holistic level can benefit individuals and the environment in several ways.Research limitations/implicationsConsumers are encouraged to be more responsible for their food consumption by reducing food waste, as it affects societies and businesses both economically and environmentally. This can help promote a responsible consumption culture that values quality over quantity and encourages people to make more informed choices about what they eat and how they dispose of it post-consumption. All stakeholders, including firms, the government and consumers, must examine the motives behind inculcating pro-environmental behaviour.Originality/valueAddressing consumerism and the ability to decrease FW behaviour are complex issues that require a multidimensional approach. This study seeks to fill the gap in understanding consumerism and the capacity to reduce FW using the CE approach and understand the research gaps and future research trends.

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“…Currently, an AI revolution is happening in almost all industries (including the agriculture and food industry) on a global scale. Two examples of the application of AI in the agri-food sector are discussed in this section, while many other applications can be found in other recent publications ( Addanki et al, 2022 ; Ben Ayed and Hanana, 2021 ; Ramirez-Asis et al, 2022 ; Talaviya et al, 2020 ).…”

Section: Interconnection Between Vegetal and Digital Trendsmentioning

confidence: 99%

Emerging trends in the agri-food sector: Digitalisation and shift to plant-based diets

Hassoun

Boukid²,

Pasqualone³

et al. 2022

Current Research in Food Science

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Recent advances and applications of artificial intelligence and related technologies in the food industry

Cited by 60 publications

References 40 publications

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)

Food wastage and consumerism in circular economy: a review and research directions

Emerging trends in the agri-food sector: Digitalisation and shift to plant-based diets

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