scholarly journals Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

2021 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Sina Ardabili ◽  
Zoltan Lakner ◽  
Csaba Mako ◽  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Multilayer Perceptron ◽  
Agricultural Production ◽  
Food Production ◽  
Livestock Production ◽  
Series Data ◽  
Accurate Estimation ◽  
The Future ◽  
Future Of Food

Abstract Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

scholarly journals Prediction of Food Production Using Machin Learning Algorithms of Multilayer Perceptron and ANFIS

2021 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Sina Ardabili ◽  
Zoltan Lakner ◽  
Csaba Mako ◽  
Amir Mosavi
Keyword(s):  
Food Security ◽  
Multilayer Perceptron ◽  
Agricultural Production ◽  
Food Production ◽  
Livestock Production ◽  
Series Data ◽  
Accurate Estimation ◽  
Inference System ◽  
The Future ◽  
Future Of Food

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

The future of food production – a text-mining approach

2019 ◽  
Vol 32 (5) ◽  
pp. 516-528
Author(s):  
Pavel Bakhtin ◽  
Elena Khabirova ◽  
Ilya Kuzminov ◽  
Thomas Thurner
Keyword(s):  
Text Mining ◽  
Food Production ◽  
The Future ◽  
Future Of Food

scholarly journals Food security issues in the economic security system of the Republic of Armenia

2021 ◽  
Vol 36 ◽  
pp. 08004
Author(s):  
M.G. Manucharyan
Keyword(s):  
Food Security ◽  
Rural Communities ◽  
Agricultural Production ◽  
Food Production ◽  
Food System ◽  
Raw Materials ◽  
Economic Security ◽  
Security Level ◽  
Poverty Level ◽  
The Republic

One of the most important components of national security is food security. The country's food security is mainly ensured through the development of agriculture, food production and food import systems. The main problems of the development of the agri-food system of the republic were the increase of the level of provision of the population with food, the increase of the level of economic protection of the country, which, first of all, requires an increase of agricultural production to provide the population with locally produced food products, raw materials to the processing industry as much as possible, as well as to increase export volumes. The main goal of the research is to develop and outline the ways of further development of the RA food self-sufficiency based on the development of agricultural production. Based on the analysis of the current situation in the agricultural market, to propose a set of economic development measures, which will contribute to the increase of the food security level, the development of the agri-food system, the reduction of the poverty level of the rural communities. The research substantiated the preconditions for further growth of agricultural production, as a result of comprehensive studies and analyzes, the main directions of improving food production and increasing efficiency were outlined, which conditioned the scientific novelty.

Trend and Pattern of Cereal Crops: Production, Productivity and Area Coverage in Nepal

Patan Pragya ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 188-195
Author(s):  
Bhaba Datta Sapkota
Keyword(s):  
Agricultural Production ◽  
Food Production ◽  
Time Series Data ◽  
Area Coverage ◽  
Series Data ◽  
Cereal Crops ◽  
Sources Of Information ◽  
Secondary Sources ◽  
Rugged Topography ◽  
Future Plans

This study conducted to examine growth trends and pattern in area, production and productivity of major cereal crops over the six fiscal years. Time series data of cereal crops coverage of annual yield were used for the study. This study was based on descriptive nature and used secondary sources of information on production, productivity and area coverage of major cereals (Paddy, Maize, Wheat, Millet, Buckwheat and Barley) covering the six fiscal years (2011/12-20116/17) data. Trends of crop's productivity were analyzed using graphical methods. Ratio and percentage were used to measure productivity (yield) growth rate of the selected crops of study. The yielding trend of cereal production is not satisfactory in Nepalese economy. Pattern of agricultural production in Nepal is affected by multiple factors including rugged topography, monsoon, insignificant investment in infrastructure, and research and development. Production and productivity would be helpful to develop the future plans and take the appropriate decisions to uphold the situation for the sustainability in food production.

The Challenges of Climate Change and Food Security in the United Arab Emirates (UAE): From Deep Understanding to Quick Actions

2019 ◽  
Vol 15 (5) ◽  
pp. 422-429 ◽  
Author(s):  
Rahaf M. Ajaj ◽  
Suzan M. Shahin ◽  
Mohammed A. Salem
Keyword(s):  
Climate Change ◽  
Food Security ◽  
United Arab Emirates ◽  
Food Production ◽  
Agricultural Sector ◽  
Deep Understanding ◽  
Primary Objective ◽  
World Population ◽  
The World ◽  
The Future

Climate change and global warming became a real concern for global food security. The world population explosion is a critical factor that results in enormous emissions of greenhouse gasses (GHGs), required to cover the growing demands of fresh water, food, and shelter. The United Arab Emirates (UAE) is a significant oil-producing country, which is included in the list of 55 countries that produce at least 55% of the world’s GHGs and thus involved in the top 30 countries over the world with emission deficits. At the same time, the UAE is located in an arid region of the world, with harsh environmental conditions. The sharp population increases and the massive growth in the urbanization are primary sources, lead to further stresses on the agricultural sector. Thus, the future of the food production industry in the country is a challenging situation. Consequently, the primary objective of this work is to shed light on the current concerns related to climate change and food security, through describing the implications of climate change on the food production sector of the UAE. Tailored solutions that can rescue the future of food security in the country are also highlighted.

A diary from the future

2021 ◽  
Author(s):  
Stine Eikrem ◽  
Keyword(s):  
Social Justice ◽  
Food Security ◽  
Food Production ◽  
Large Scale ◽  
Environmental And Health Effects ◽  
Change Resistance ◽  
Creative Potential ◽  
The Future ◽  
Complex Relationships ◽  
Social Environmental

‘A diary from the future’ thinks through the positive social, environmental and health effects of a large-scale transition to plant-based diets. These include positive changes for food production, consumption and food security, and with that, also social justice, equity, education, poverty and the co-existence with other species. Even just the way the story thinks through and recognises these complex relationships and effects is an achievement and a novelty for physiotherapists in itself. Reaching well beyond this, however, this is also a story about how fear and darkness can turn into hope, optimism and curiosity for the future as a result of learning and thinking about these complex relationships. Finally, ‘A diary from the future’ is also a reflection on the possibilities of broadening our understanding of physiotherapy, of the need for change, resistance to it, and the creative potential that is released when these resistances are overcome.

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