Can the Internet of Things help close livestock data gaps in Africa?
Hackathon students devise new ways to track animal health and productivity
By Vanessa Meadu, SEBI
There are major gaps in data on livestock health and productivity in Sub-Saharan Africa, which leaves governments, investors, businesses and farmers guessing at the best solutions.
Existing data is poor and fractured, and conventional data collection is time-consuming and expensive, and often subject to human error or biases. New data-gathering technologies may offer significant potential to help fill these gaps, but have been underused in the African context. That is why the Supporting Evidence Based Interventions (SEBI) program challenged students to propose how the Internet of Things (IoT) can help address critical data challenges in Sub-Saharan Africa.
The students were taking part in an IoT Hackathon in February 2019 coordinated by the University of Edinburgh’s Bayes Centre and the Centre for Intelligent Systems and their applications (CISAA).
The students considered two key problems related to livestock data:
- DISEASE: How might we use IoT to predict or combat the spread of disease in livestock?
- PRODUCTIVITY: How might we use IoT or embedded sensing to accurately predict the produce created by African Farmers (e.g. Milk, Eggs, Meat etc.)?
The students had two days to brainstorm solutions and propose their concepts to the Hackathon panel. Two teams took on the disease challenge, generating interesting proposals to use sensors to detect disease.
The first solution Cow Ontology Web would use two types of sensors: one attached to the cow to collect vital health information, while a second sensor spike would be installed in the field to collect soil and weather. The spike would also have a camera, which could be used to view animals and their surroundings. The cattle vital sensor would LED bit transmission to communicate cheaply with the sensor spike, and would help predict patterns in cattle health, diagnosing potential problems in real-time.
Students in this group reflected on the technical challenges around collecting data in African livestock. “We learnt about the importance of low cost and robust solutions for sub-Saharan Africa, and the challenge of designing IoT solutions under these constraints and also without ubiquitous connectivity,” they explained.
The second proposed solution was the East Coast Fever Detector, described as a smart collar that detects a contagious disease in cattle in an early stage and notifies the farmer to isolate the cow. A collar on each individual cow would collect temperature information from the animal’s lymph nodes and also track gait, location, and distance traveled. At 60-second intervals, the system would aggregate symptoms to gauge the health condition of the cow and the collar would illuminate to indicate a cow’s status. If the collar glowed red, the cow was “ILL” (high temperature, unsteady gait); a yellow collar would indicate a cow was “AT RISK” (had been in close contact with a cow previously deemed ILL). The illuminated collars would help keep farmers continuously updated on the status of their livestock.
Jack Radford, a research student from the University of Glasgow’s Centre for Doctoral Training in Intelligent Sensing and Measurement (CDT-ISM), was a member of the East Coast Fever Detector team. He chose to work on the livestock data challenge because of the chance to make a “real difference” on an international level. “I was hesitant to join the group because I have little knowledge of relevant diseases and the location that the problem is set,” he said, “[But] I was excited when the team took a technical approach to the problem and really leveraged the core idea of Internet of Things (IoT) to find a solution.”
He mentioned that a great advantage of IoT is the capability to relay information between multiple devices up to 10km apart, which could be beneficial in the context of African livestock, where mobile coverage or internet connectivity may be limited. The use of illuminated LED collars to communicate risk also overcomes any language or literacy barriers among livestock keepers, he explained.
Next steps
The Hackathon hinted at some of the ways cutting-edge information technologies such as IoT could be applied to address critical data gaps for livestock in Africa. These technologies, though largely untested in the African context, may become an additional means of monitoring livestock health and productivity if they can be made affordable and accessible at a large scale.
The teams at SEBI and Bayes will be further exploring opportunities for harnessing innovations to tackle real world challenges together.
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