- The system that has been developed by the International Centre for Agriculture (CIAT) and will be rolled out later this year.
- Current warning systems, mainly due to financial and technological constraints, tend to focus on just a few variables influencing malnutrition and food security.
- Artificial intelligence is an area of computer science that emphasises the creation of intelligent machines that work and react like humans.
A new prediction and analysis technology could play a key role in tackling food insecurity and widespread malnutrition that dent Kenya’s health and economic development.
The innovative digital tool, known as the Nutrition Early Warning System (NEWS), is designed to make use of artificial intelligence (AI) to aggregate and analyse huge volumes of data to effectively predict food shortage risks in Africa.
The system that has been developed by the International Centre for Agriculture (CIAT) and will be rolled out later this year.
“Through NEWS we will be making use of advances made in the computer science field to tackle malnutrition challenges in Kenya and the entire continent,” said Debisi Araba, CIAT regional director for Africa.
The platform uses a type of AI technique known as machine learning where software is programmed to not only collect and aggregate large volumes of information (Big Data) but also to make sense of it. This information will be drawn from different public and private sector organisations.
Dr Araba says the CIAT has focused on Big Data since food security and good nutrition are influenced by very many factors that cut across multiple sectors. They include climate, trade policies, infrastructure deficiencies, inflation rates, security threats, migration, urbanisation and disease.
“So you can’t just focus on one thing. You need to analyse data from all these sectors simultaneously to be able to detect signs of food shortages long before hunger becomes a crisis.”
Current warning systems, mainly due to financial and technological constraints, tend to focus on just a few variables influencing malnutrition and food security.
As such, these platforms are prone to generating inaccurate predictions that make it hard for governments to use them as effective decision-making tools.
But NEWS is equipped to tackle this challenge as AI machine learning will enable the system to analyse Big Data sets, establish trends and give practical solutions — in form of advisories — for tackling malnutrition challenges.
“It will provide us with high-quality data and predictions that we can use to prevent food problems like drought that has really affected us this year,” said Clement Munyesu, senior assistant director at the State Department of Agriculture.
“This is a type of data revolution that Kenya will be proud to be part of, so as to get accurate information on agriculture.”
Just as human beings become more intelligent as they expose their minds to new information, NEWS — thanks to AI — is designed to keep becoming smarter as it dissects Big Data and learns from emerging trends over time.
“This means that the predictions will become more and more accurate and thus trustworthy over time,” stated Dr Araba.
Early this year, the Kenya Red Cross estimated that close to four million Kenyans were at risk of hunger following insufficient rainfall patterns in 2016.
These perennial food shortages contribute immensely to the burden of malnutrition among children who are most vulnerable to effects of hunger.
The Ministry of Health statistics shows that about 26 per cent of Kenyan children suffer from chronic malnutrition.
This makes them vulnerable to infections that cost billions to treat. It also predisposes affected kids to irreversible brain damage or intellectual impairment that hinders their chances of becoming effective future drivers of economic growth.
Artificial intelligence is an area of computer science that emphasises the creation of intelligent machines that work and react like humans.
Social media platforms such as Facebook rely on the technology to analyse data on people’s preferences based on posts they react to or links they open.
This enables the platform to study habits of users over time and thus be in a position to predict their preferences and hence ‘prime’ them for commercial advertisements.