Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty

Garre, Alberto; Ruiz, Mari Carmen; Hontoria, Eloy

doi:10.1016/j.orp.2020.100147

Cited by 52 publications

(26 citation statements)

References 37 publications

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“…For example, Big Data empower supply chain management [157], helping to reduce agriculture waste or machine learning [194] to detect defective horticultural products. The authors of [195] applied machine learning to make production planning more sustainable in a food company in Spain. Other researchers focused on product life-cycle management and studied the advancements and influences of Big Data and relevant technologies on each stage, from supply and production to maintenance, recycling and waste disposal [196].…”

Section: Big Data and Responsible Consumption And Productionmentioning

confidence: 99%

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

Hassani

Huang

MacFeely³

et al. 2021

BDCC

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The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.

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Section: Big Data and Responsible Consumption And Productionmentioning

confidence: 99%

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

Hassani

Huang

MacFeely³

et al. 2021

BDCC

View full text Add to dashboard Cite

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“…In a backward process the least contributing terms are pruned to minimize overfitting. Garre et al [50] compared a model developed using MARS to similar regression models developed to predict the amount of waste in food production and quantify model uncertainties. The MARS model achieved a precision comparable to that of more sophisticated machine learning models such as random forest methods developed to deal with the high variability in decision trees while maintaining low bias [51].…”

Section: Splinesmentioning

confidence: 99%

Advanced Optical Technologies in Food Quality and Waste Management

Chauvin¹,

Duran²,

Ng³

et al. 2021

Innovation in the Food Sector Through the Valorization of Food and Agro-Food by-Products

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Food waste is a global problem caused in large part by premature food spoilage. Seafood is especially prone to food waste because it spoils easily. Of the annual 4.7 billion pounds of seafood destined for U.S. markets between 2009 and 2013, 40 to 47 percent ended up as waste. This problem is due in large part to a lack of available technologies to enable rapid, accurate, and reliable valorization of food products from boat or farm to table. Fortunately, recent advancements in spectral sensing technologies and spectroscopic analyses show promise for addressing this problem. Not only could these advancements help to solve hunger issues in impoverished regions of the globe, but they could also benefit the average consumer by enabling intelligent pricing of food products based on projected shelf life. Additional technologies that enforce trust and compliance (e.g., blockchain) could further serve to prevent food fraud by maintaining records of spoilage conditions and other quality validation at all points along the food supply chain and provide improved transparency as regards contract performance and attribution of liability. In this chapter we discuss technologies that have enabled the development of hand-held spectroscopic devices for detecting food spoilage. We also discuss some of the analytical methods used to classify and quantify spoilage based on spectral measurements.

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“…ML has shown to be advantageous in predicting the food insecurity in the UK [ 85 ]. Apart from that, ML has also proven to have predicted the trend of sales in the food industry [ 86 ] In addition to that, ML was also able to predict the food waste generated and give an insight to the production system [ 87 ]. Major applications of ML in the food industry and its positive highlights are briefly emphasized in Table 4 .…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Food Industry—a Guideline

et al. 2021

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Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

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Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty

Cited by 52 publications

References 37 publications

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

Advanced Optical Technologies in Food Quality and Waste Management

Application of Artificial Intelligence in Food Industry—a Guideline

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