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IoT can boost agri in Africa

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The Internet of Things has the potential to increase agriculture in Africa by 70% by 2050, a figure similar to which the demand for food is set to grow in the same period.

It is estimated that, through technological innovation, the Internet of Things (IoT) has the potential to increase agricultural productivity in Africa by 70% by 2050. This is exactly the figure by which demand for food in Africa is set to increase based on population growth. This is according to a Deloitte US report on the impact of IoT on agriculture, titled Dirt to Data: The second green revolution and the Internet of Things.

Agriculture is seen as a key economic driver by the World Economic Forum (WEF), which holds its Africa regional meeting in Kigali, Rwanda on 11-13 May. Under the theme ‘Connecting Africa’s Resources through Digital Transformation, the 26th WEF on Africa will convene regional and global leaders from business, government and civil society to discuss the digital economy and agree on strategic actions that can deliver shared prosperity across the continent.

WEF has identified the IoT as one of 21 ‘tipping points’, when a specific technological shift enters mainstream society. For the IoT, WEF estimates that this point will be reached by 2022. Given rising agriculture demand and the associated resource scarcity challenges, the IoT will ensure that the tipping point is reached sooner rather than later.

Carlton Jones, Agriculture Sector Leader for Deloitte Consulting, says the drought in Southern Africa caused by the El Niño phenomenon resulted in lower than expected crop yields. “To some extent, the crop failures reported could have been avoided through use of technology that is only now becoming available. Technological innovation within the agricultural sector could have helped ensure that farmers were better prepared in dealing with the current drought by informing them of what to plant and where to plant it given the El Nino effect on the region. “While these technological advances may help farmers mitigate against bad yields, implementing such technologies remain fairly expensive and may not yet be feasible for small holders  farmers, but rather is likely to be implemented via multinational corporations at present.

Enhanced data translates into better products being developed for the market therefore ensuring all round benefits.  “The IoT has the potential to ensure that all stakeholders within the agricultural value chain, whether large company, smallholder farmer, food manufacturer, retailer, or consumer are able to maximise onvalue”, adds Jones.

“The report notes that the IoT has proven its value in numerous industries and that the main question for stakeholders in the nascent agricultural IoT ecosystem is how to commercialise and scale the technologies, and who will pay for their development and deployment”, says Jones.

He adds that these are the strategic issues, which he would like to see WEF apply its collective mind to across the agricultural value chain. Technological innovation tied in with data analysis has the potential to ensure that food production will be able to keep pace with population growth globally.

“Despite the green revolution having being modelled in the USA, an African green revolution is yet to take place. Such a revolution will take into account localised factors, learning’s from other developing economies and use the IoT as an enabler to enhance the sector as a whole,” says Jones.

This revolution is one driven less by new techniques with consequences of resource depletion and soil degradation, but rather by technology which gives farmers the data to help make better choices. It will likely be grounded in the use of data to inform more efficient and effective farming practices and drive associated environmental and social benefits.

A wave of innovations, from satellite geo-mapping by NASA to the use of drones to collect aerial data, provides insights into the health of the land on a real-time basis. Technologies such as advanced sensors and monitoring equipment can now allow farmers to monitor crops more precisely and continuously, thereby enabling more strategic decision-making to increase productivity with reduced impacts on the environment, thereby doing more with less.

“The uses of these technologies cover the entire spectrum, from more productive farming techniques to improved nutrition. Sensors attached to livestock give early warning of illness, enabling prevention and thereby increasing milk yields. Such a targeted approach to veterinary care can have the added benefit of reducing the need for herd-wide preventative antibiotics, which have been shown to contribute to drug-resistant bacteria,” says Jones.

One method whereby smallholder farmers can benefit from IoT is through aggregation of their resources and equipment, something already implemented in South Africa.

Additional value can be created when one considers the role of agriculture in emerging economies. In these economies, the IoT can provide value not only through increased resource efficiency and crop productivity, but also by providing social value and financial benefits for smallholder farmers.

Collaborations like these to deploy IoT technologies will be increasingly vital if we are to put the world’s farms on track to feed the estimated 11 billion people who will inhabit the earth by 2050.

“Despite the challenges,” says Jones, “there is cause for optimism.”

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Smart grids needed for Africa’s utilities

Power utilities across Africa should rethink their business models and how they manage and monetise their assets to keep pace with the changing energy ecosystem, says COLIN BEANEY, Global Industry Director for Asset-intensive and Energy and Utilities at IFS.

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Africa’s abundant natural resources and urgent need for power mean that it is one of the most exciting and innovative energy markets in a world that is moving rapidly towards clean, renewable energy sources. The continent’s energy industry is taking new approaches to providing unserved and underserved communities with access to power, with an emphasis on smart technologies and greener energy sources.

Power systems are evolving from centralised, top-down systems as interest in off-grid technology grows among African businesses and consumers. And according to PwC, we will see installed power capacity rise from 2012’s 90GW to 380GW in 2040 in sub-Saharan Africa. Power utilities are needing to rethink their business models and how they manage and monetise their assets to keep pace with the changing energy ecosystem.

Energy and utilities providers are transforming from centralised supply companies to more distributed, bi-directional service providers. They can only achieve this through the evolution of “smart grids” where sensors and smart meters will be able to provide the consumer with a more granular level of detail of power usage. This shift from an energy supplier to “lifestyle provider” will require a much more dynamic and optimised approach to maintenance and field service.

African companies must thus embrace digital transformation as an imperative. This transformation begins by embracing enterprise asset management to improve asset utilisation. The subsequent steps are enhancing upstream and downstream supply chain management; resource optimisation; introducing enterprise operational intelligence; embracing new technologies such as the Internet of Things, machine learning, and predictive maintenance; and becoming a smart utility.

Embracing mobility to drive ROI

Getting it right is about putting in place an enterprise backbone that accommodates asset and project management, multinational languages and currencies, new energies and markets, visualisation of the entire value chain, and mobility apps. Mobile technologies that support the field workforce have a vital role to play in driving better ROI from utilities’ investments in enterprise asset management and enterprise resource planning solutions.

Today’s leading enterprise asset management solutions feature powerful functionality for mobile management of the complete workflow of work orders – from logging status changes and updates, from receiving and creating new orders to concluding the job and reporting time, material and expenses. Such solutions are easy to deploy and intuitive for end users to learn and use.

Importantly for organisations operating in parts of the continent with poor telecoms infrastructure, connectivity is not an issue. The solutions work offline and synchronises when network connectivity is available. Users can work on any device—laptops, tablets, and smartphones—commercial or ruggedised.

By ensuring that field technicians have easy access to information and processes, the mobile solution enables technicians and maintenance engineers to easily do the following tasks:

·         Create a new work order on the fly and log new opportunities

·         Access both historical and planned work information when requested

·         Permit customers to sign when the job is completed

·         Capture measurements and inspection notes on route work orders

·         Create new fault reports on routing

·         Facilitate documentation through photo capturing

·         Provide easy access to technical data and preventive actions.

The power of mobility allows the engineer to be the origin of all data capture on a service event. They can easily inquire on asset history, record parts used or parts needed for repair, record labour hours, and expenses as they occur, and any notes of repairs performed. When coupled with workforce management tools, such solutions unlock significant productivity gains for utilities who are trying to get the most from their workforce and assets.

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How machine learning can save your life

Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

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Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

But what if we could have predicted the outbreak months before it happened, buying us time to take proactive measures to contain it and curb its spread?

With access to overwhelming volumes of data, the computational power needed to store and analyse this data in real time, and sophisticated algorithms that can find patterns in the data and alert authorities to health problems before they become, well, problems, pandemics don’t have to be as devastating as they have been in the past.

In fact, with advanced data analytics, we can better manage any disease – long-term, short-term or pandemic – resulting in better patient treatment, more efficient use of resources and cost savings.

It’s been done before.

By analysing data from social media, blogs, online forums and keyword searches, we were able to predict the 2012-2013 US flu season three months before the Center for Disease Control (CDC) issued its first official warning.

Imagine the impact if the same analytical power was applied across the entire healthcare spectrum – not only on a national and global level, but right down to the individual level.

Data evolution

In the past, health workers relied on manually intensive, paper-based systems to record infections and deaths during disease outbreaks. Not only was it easy for errors to slip through but because the data was anecdotal and historical, authorities did not get a complete understanding of the reach and impact of the outbreak.

During the Ebola outbreak, the CDC adopted a mobile data collection system that enabled health workers to instantly submit information to a database via text messages. This low-cost method of information gathering not only resulted in fewer errors but also allowed analysts to draw up detailed maps of population movements, which made it easier to understand how the disease was likely to spread, and where to set up treatment centres.

While this was certainly an improvement on the paper-based systems of old, the drawback was that mobile data was historic and did not provide researchers with the ability to track developments and population movements in real time.

Data-driven action

But mobile phones are just one source of data. Today, health authorities can overlay thousands of data sources – including social media, health and physician reports, keyword searches, media reports, transactional data from retailers and pharmacies, airline ticket sales, geospatial data and more – to not only better manage diseases and outbreaks when they do happen, but to see them coming months in advance – and what could happen if we don’t act on the information.

By mining structured and unstructured data, we can track the movements of infected populations and who they come into contact with; we can measure the success of containment policies, education campaigns and treatments – and what to do if they’re not working; we can determine the effect of weather and other environmental factors on the spread of diseases.

Never before have we been able to act on information to save lives, not just during pandemics but through better understanding and treatment of diseases.

Personalised treatment

Until now, standard treatments for diseases such as cancer and HIV have been applied to all patients, regardless of their unique profiles and with little understanding as to why some people respond well to certain treatments and others don’t.

But by analysing and creating ‘medical maps’ of individuals that take into account their anatomy, physiology, DNA, RNA and chemical composition, doctors can prescribe personalised treatments that have a greater chance of success.

There are many other benefits of data analysis in healthcare:

·        Personalised treatment can result in fewer hospital admissions and can produce faster results and better experiences for patients;

·        By better understanding the impact of lifestyle and diet on health, medical aid providers can educate their members with the aim of improving their health, which could result in cost savings for both the provider and the member;

·        Governments can use data to develop proactive approaches to protecting and promoting public health, to prioritise services and to find ways to cut costs so that they can provide healthcare to more citizens.

·        By sharing data and results from clinical trials and combining that data with academic, patient and industry data, medical researchers can better understand the genetics of viruses, why some strains are more deadly than others, and why some people are more resistant to viruses. This could spark innovation and generate new insights that ultimately improve treatment and outcomes.

AI and machine learning

As the use of intelligent algorithms, machine learning and natural language processing becomes more entrenched in advanced data analytics, technology will increasingly supplement the skills of humans to produce faster and more accurate medical diagnoses.

We’re already seeing successful applications of artificial intelligence (AI) in predicting relapse in leukaemia patients and in distinguishing between different types of cancer.

Machine learning can extract valuable insights from unstructured data like clinical notes and academic journals to provide even larger datasets that will transform the medical industry into a proactive front against diseases.

There are plenty of doomsday theories about how machines will supersede our intelligence and rise against us. But there aren’t enough stories about the potential of data analytics, AI and machine learning to supplement human skills and knowledge to drastically changes lives for the better – and even save them. Right now, it’s looking more likely that machines will actually help us to live longer – and I don’t know many people who would object to that.

 

  • ANESHAN RAMALOO, ‎Data Scientist and Senior Business Solutions Manager at SAS.
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