Machine learning for food security: Principles for transparency and usability

2021 ◽  
Author(s):  
Yujun Zhou ◽  
Erin Lentz ◽  
Hope Michelson ◽  
Chungmann Kim ◽  
Kathy Baylis
Keyword(s):  
Machine Learning ◽  
Food Security

USING SATELLITES AND MACHINE LEARNING TO ENHANCE AND PROTECT FOOD SECURITY IN AFRICA

2020 ◽  
Author(s):  
Catherine Nakalembe ◽  
◽  
Inbal Becker-Reshef ◽  
Hannah Kerner ◽  
Ritvik Sahajpal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Food Security

scholarly journals Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

2021 ◽  
Vol 13 (9) ◽  
pp. 4728
Author(s):  
Zinhle Mashaba-Munghemezulu ◽  
George Johannes Chirima ◽  
Cilence Munghemezulu
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Rural Communities ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Subsistence Agriculture ◽  
Smallholder Farms ◽  
Main Driver ◽  
Sentinel 2

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.

scholarly journals Impact of Weather on Crops in Few Northern Parts of Bangladesh: HCI and Machine Learning Based Approach

2020 ◽  
pp. 7-15
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Crop Production ◽  
Performance Metrics ◽  
Machine Learning Algorithms ◽  
Production Level ◽  
Soft System Methodology ◽  
Average Production ◽  
The Impact ◽  
System Methodology

As Bangladesh is an agricultural country, the economy, as well as the food security of this country, mostly depends on the production level of different crops over the year. Therefore, there exists immense pressure on exaggerated crop production due to the fast growth of the population. But, the average production level is being hampered by the bad nature of the weather. We have conducted a survey on near about 100 farmers of two northern districts of Bangladesh: Pabna and Rajshahi and assessed the impact of rough nature on production. According to farmers and agriculturalists, it is noticed that rough weather causes about 30% to 70% production shortage than expectation with all other factors remaining constant. In this study, we have adopted Human-computer interaction (HCI) based approach (Soft System Methodology-SSM) to this aspect for efficacious collaboration with root-level farmers and agricultural trainers providing ease for understanding weather-related issues on the production of crops. Finally, some machine learning algorithms were also implemented on the obtained dataset to accurately classify the range of production level of rice and a comparison is made among the algorithms based on performance metrics. Moreover, an android based application is created to depict the summary of the study.

Alternatives to calorie-based indicators of food security: An application of machine learning methods

Food Policy ◽  
2019 ◽  
Vol 84 ◽  
pp. 77-91 ◽  
Author(s):  
Marup Hossain ◽  
Conner Mullally ◽  
M. Niaz Asadullah
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Learning Methods ◽  
Machine Learning Methods

Machine Learning in Food Security and Sustainability

2021 ◽  
pp. 1-17
Author(s):  
Wei Chien Ng ◽  
Yu Qing Soong ◽  
Sin Yin Teh
Keyword(s):  
Machine Learning ◽  
Food Security

scholarly journals Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security

2020 ◽  
Vol 698 ◽  
pp. 133999 ◽  
Author(s):  
Majid Bagheri ◽  
Khalid Al-jabery ◽  
Donald Wunsch ◽  
Joel G. Burken
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Plant Uptake ◽  
Emerging Contaminants

2 Food security and farming through IoT and machine learning

2021 ◽  
pp. 21-40
Author(s):  
Ashish Tripathi ◽  
Arun Kumar Singh ◽  
Khararee Narayan Singh ◽  
Krishna Kant Singh ◽  
Pushpa Choudhary ◽  
...  
Keyword(s):  
Machine Learning ◽  
Food Security

scholarly journals Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities

2021 ◽  
Vol 10 (11) ◽  
pp. 745
Author(s):  
Abrar Almalki ◽  
Balakrishna Gokaraju ◽  
Nikhil Mehta ◽  
Daniel Adrian Doss
Keyword(s):  
Machine Learning ◽  
North Carolina ◽  
Food Security ◽  
Correlation Coefficient ◽  
Food Access ◽  
Healthy Food ◽  
Matrix Analysis ◽  
Food Sources ◽  
Median Income ◽  
Health Issues

Food access is a major key component in food security, as it is every individual’s right to proper access to a nutritious and affordable food supply. Low access to healthy food sources influences people’s diet and activity habits. Guilford County in North Carolina has a high ranking in low food security and a high rate of health issues such as high blood pressure, high cholesterol, and obesity. Therefore, the primary objective of this study was to investigate the geospatial correlation between health issues and food access areas. The secondary objective was to quantitatively compare food access areas and heath issues’ descriptive statistics. The tertiary objective was to compare several machine learning techniques and find the best model that fit health issues against various food access variables with the highest performance accuracy. In this study, we adopted a food-access perspective to show that communities that have residents who have equitable access to healthy food options are typically less vulnerable to health-related disasters. We propose a methodology to help policymakers lower the number of health issues in Guilford County by analyzing such issues via correlation with respect to food access. Specifically, we conducted a geographic information system mapping methodology to examine how access to healthy food options influenced health and mortality outcomes in one of the largest counties in the state of North Carolina. We created geospatial maps representing food deserts—areas with scarce access to nutritious food; food swamps—areas with more availability of unhealthy food options compared to healthy food options; and food oases—areas with a relatively higher availability of healthy food options than unhealthy options. Our results presented a positive correlation coefficient of R2 = 0.819 among obesity and the independent variables of transportation access, and population. The correlation coefficient matrix analysis helped to identify a strong negative correlation between obesity and median income. Overall, this study offers valuable insights that can help health authorities develop preemptive preparedness for healthcare disasters.

Digital Farmers

2019 ◽  
pp. 221-238
Author(s):  
Gordon Conway ◽  
Ousmane Badiane ◽  
Katrin Glatzel
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Food Security ◽  
Digital Technologies ◽  
Soil Maps ◽  
Crop Selection ◽  
Blockchain Technology ◽  
Technology Applications ◽  
Sustainable Fashion ◽  
First Time

This chapter deals with digital technologies, both hardware and software, and how they can help increase agricultural production and food security in a sustainable fashion. Africa is going digital, enabling millions of Africans to connect for the first time. The effect is to revolutionize the lives of African farmers—by overcoming isolation, by speeding up change, and by taking success to scale. Digital connectivity has enormous implications for agriculture and nutrition. It is already being used to disseminate information on nutrition and health, providing timely information on everything from weather predictions, crop selection, and pest control to management and finance. Technologies include the analysis of big data, using machine learning and blockchain technology applications that help to produce and analyze digital soil maps, to provide sophisticated insurance and faster breeding cycles for traditional African crops.

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