Machine learning for food security: Principles for transparency and usability

Zhou, Yiqiang; Lentz, Erin; Michelson, Hope; Kim, Chungmann; Baylis, Kathy

doi:10.1002/aepp.13214

Cited by 22 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the other employed models showed an acceptable performance rate, while KNN reported the lowest accuracy rate of all. Our results were found to be consistent with other studies that used ML in predicting FI and con rmed the feasibility of ML use in identifying FI (41,(50)(51)(52) . The models in this study reported accuracy rates ranging from 70-82%, while other models reviewed from previous research reported accuracy rates ranging from 55-85%.…”

Section: Discussionsupporting

confidence: 91%

Machine Learning Techniques for the Identification of Risk Factors Associated with Food Insecurity among Adults in Arab countries during the COVID-19 Pandemic

Qasrawi

Hoteit

Tayyem

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results: The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%,13.7%,13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions: Overall, ML algorithms seem to be an effective method in early detection and prediction of food insecurity. Future research would benefit from utilizing the proposed model in developing more complex and accurate models aiming to enhance granularity, with the ability to share data, to incorporate wide range of variables, and to make use of automation for effective prevention and intervention programs at the regional and individual levels.

show abstract

Section: Discussionsupporting

confidence: 91%

Machine Learning Techniques for the Identification of Risk Factors Associated with Food Insecurity among Adults in Arab countries during the COVID-19 Pandemic

Qasrawi

Hoteit

Tayyem

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Third, our model effectively captures across‐time changes in consumption expenditure for ultra‐poor communities in Uganda. Our study is the first attempt to predict future‐period consumption expenditure at the community level; it adds to the emerging literature that relies on machine learning methods and publicly available data to predict future‐period wealth index, malnutrition, and food insecurity (Browne et al, 2021; Yeh et al, 2020; Zhou et al, 2021).…”

mentioning

confidence: 99%

Predicting poverty with vegetation index

Tang

Liu

Matteson

2021

Applied Eco Perspectives Pol

View full text Add to dashboard Cite

Accurate and timely predictions of the poverty status of communities in developing countries are critical to policymakers. Previous work has applied convolutional neural networks (CNNs) to high‐resolution satellite imagery to perform community‐level poverty prediction. Although promising, such imagery has limitations in predicting poverty among poor communities. We provide the first evidence that a publicly available, moderate‐resolution vegetation index (the normalized difference vegetation index [NDVI]), can be used with CNNs to produce accurate poverty predictions contemporaneously among poor communities heavily dependent on agriculture. We also show that the NDVI can effectively detect consumption variation over time. To our knowledge, this is the first attempt to use remote sensing data to predict future‐period consumption expenditure at the community level.

show abstract

“…Finally, we would strive to involve the field experts, like the owner and the administrative staff, during the second iteration to incorporate their feedback and their domain knowledge. This could uncover aspects we might have missed in the initial model (Hannon et al 2019;Zhou et al 2022).…”

Section: Discussion and Limitationsmentioning

confidence: 99%

‘Why are the Sales Forecasts so low?’ Socio-Technical Challenges of Using Machine Learning for Forecasting Sales in a Bakery

Fries

Ludwig

2022

Comput Supported Coop Work

View full text Add to dashboard Cite

Artificial intelligence and the underlying machine learning (ML) methods are increasingly finding their way into our working world. One of these areas is sales planning, where machine learning is used to leverage a variety of different input parameters such as prices, promotions, or the weather, to forecast sales, and therefore directly affects the production of products and goods. To satisfy the goal of environmental sustainability as well as address short shelf life, the food industry represents an interesting application field for the use of ML for optimizing sales planning. Within this paper, we will examine the design, and especially the application, of ML methods in the food industry and show the current challenges that exist in the use of such concepts and technologies from the end-user’s point of view. Our study of a smaller bakery company shows that there are enormous challenges in setting up the appropriate infrastructure and processes for the implementation of ML, that the output quality of ML processes does not always match the perceived result quality, and that trust in the functioning of the algorithms is the most important criterion for using ML processes in practice.

show abstract

Machine learning for food security: Principles for transparency and usability

Cited by 22 publications

References 27 publications

Machine Learning Techniques for the Identification of Risk Factors Associated with Food Insecurity among Adults in Arab countries during the COVID-19 Pandemic

Machine Learning Techniques for the Identification of Risk Factors Associated with Food Insecurity among Adults in Arab countries during the COVID-19 Pandemic

Predicting poverty with vegetation index

‘Why are the Sales Forecasts so low?’ Socio-Technical Challenges of Using Machine Learning for Forecasting Sales in a Bakery

Contact Info

Product

Resources

About