African university explores data-driven smart farming

For some time now, big data has held the promise of having a revolutionary impact on various sectors of society that will lead to better, faster decision-making, improved processes, and ground-breaking discoveries.

In agriculture, better data management, analysis and application can boost animal and crop improvement, biosecurity and disease control, postharvest technology, agroprocessing, value chain analysis and development, as well as food sustainability and security. It can also help counter and build resilience to climate change.

Dr Jan Greyling, a senior lecturer in the department of agricultural economics at Stellenbosch University (SU), believes proper big data analysis can improve food availability, access and utilisation, among other things.

“Big data can change the world, but it’s gushing at us like water from a fire hose, and we’re not actually making sense of it. We need to look at it differently. We must move from numbers to actionable insights,” he says.

Revolutionary impact

According to the United Nations’ Food and Agriculture Organization, big data analysis is revolutionising agriculture. The use of digital technologies – including smartphones, tablets, sensors, drones, and satellites – has become common in the sector, providing a range of farming solutions, from the remote measurement of soil conditions to real-time livestock and crop monitoring.

“There’s lots of technology in agriculture and forestry – autosteering tractors connected to satellites and, lately, to the internet, machines going through orchards and counting fruit, drones spraying crops and taking high-resolution images, and so on,” Greyling says.

“But these technologies are all things we can physically see and touch. What’s not so visible is the constant stream of data that they produce. We’re not fully exploiting that yet.”

Greyling has a specific challenge for the research community: “We need to view the data, in itself, as an asset, something valuable to invest in and not just to be written up in a thesis or journal article and then to be dumped in an obscure repository where it is ostensibly available to others.

“Only once we start combining existing stores of big data in new ways and analyse it properly, agroinformatics will really change the world.”

A big data initiative

Professor Danie Brink, the dean of the faculty of AgriSciences at SU, has placed agroinformatics at the heart of his faculty’s research and innovation strategy.

As such, one of its new projects focuses on changing the way in which big data is integrated into agriculture and forestry research. It’s called the Stellenbosch Agroinformatics Initiative (SAI), which is aimed at enabling data-intensive interdisciplinary research and innovation partnerships that span departments, faculties, universities, companies and continents.

Greyling, head of the SAI, says: “Effective data management, sharing, and collaboration across disciplines are crucial components of research that can make a meaningful impact on society and the agricultural sector.”

Collaborating for maximum impact

Greyling says calling the project an ‘initiative’ was a deliberate choice.

“We could have established a standalone centre or institute in the faculty, but that might have sidelined data science, which would have been counterproductive.

“Instead, we want to make data science a part of every department and research group in the faculty because the management, analysis and utilisation of data have become integral to all our work.”

In the words of Professor Kennedy Dzama, the faculty’s vice-dean for research, innovation and postgraduate studies, “It is intrinsically valuable to share information and ways of thinking.

“Different universities and entities within them will have different strengths, but they can complement each other through collaboration.”

An aspect of collaboration highlighted by Brink is the interdisciplinary coordination of research. The SAI brings researchers from different academic fields together to establish communities of practice around five core data-intensive research techniques: image processing, LiDAR (light detection and ranging), the Internet of Things, special analysis, and genomics.

“The challenge is to bring domain experts from different fields together, combining their expertise to solve the major challenges facing the agri-food and natural resources sectors,” says Greyling.

Image processing

In the SAI’s image processing community of practice, Professor Lizel Mostert of the faculty’s department of plant pathology is using AI to help with the identification of plant disease symptoms.

“Grapevine leafroll is an economically devastating disease for grapevine producers in South Africa,” she says.

“To ensure clean propagation material, it is essential to identify diseased vines. But that’s easier said than done as it can be quite hard to distinguish infected leaves from ones that have been mechanically damaged or are suffering from a phosphorus deficiency.”

This fact has led to a study aimed at developing an AI-based system that uses deep learning techniques to identify leafroll-infected vines via photos taken with a smartphone.


Elsefie Fouché, a BSc Hons student at SU, takes a photo with a smartphone in a vineyard as part of a research project to develop an AI-based system using deep learning techniques to identify leafroll. Leaves of these vines were used to include examples of a cultivar that naturally turns red in autumn, Image: Dr Beatrix Coetzee

Hyperspectral imaging

While field analysis on the go is useful, more extensive investigation is sometimes necessary. It is then that the HySpex hyperspectral imaging system comes into its own.

Housed in the faculty’s department of food science, this system is used in plant pathology studies, as well as food chemistry and microbiology research. It lies in the capable hands of Dr Paul Williams, a data scientist and spectroscopy expert in the department.

Hyperspectral imaging is a technique that combines digital imaging with spectroscopy to capture and analyse reflected light across hundreds of spectral bands, Williams explains.

While our eyes typically perceive only the colours red, green and blue, hyperspectral imaging captures a much broader range of the electromagnetic spectrum, thereby providing a very detailed picture of an object.

Hyperspectral snapshot cameras enable the real-time inspection of crops for the early detection of disease and water stress, and for the analysis of soil quality.

“Hyperspectral imaging is fast, accurate and allows samples to be analysed non-destructively and non-invasively with minimum or no sample preparation,” Williams says.

Smart farming for the future

Non-invasive technologies also feature prominently in another of the faculty’s communities of practice, namely the one focused on the Internet of Things.

The latter consists of sensor-embedded devices that share data with each other and with analytic systems via the internet or other communication networks.

The Internet of Things is transforming agriculture into a more data-driven and efficient practice, often referred to as ‘precision agriculture’ or ‘smart farming’. Producers using this technology can gain a deeper understanding of their farming environment, which allows them to optimise their operations.

Professor Carlos Poblete-Echeverría coordinates SAGWRI’s (the South African Grape and Wine Research Institute at SU) research group on digital agriculture, which is based on the principles of smart farming.

In a keynote address presented at the 10th International Table Grape Symposium in Somerset West in South Africa’s Western Cape Province at the end of 2023, he argued that table-grape growers stand to benefit from the latest advances in data collection and processing.

“We are looking at a group of new technologies – sensors, platforms, algorithms and the like – that can be used to provide useful information for optimising management practices in viticulture and the broader field of agriculture,” he says.

‘Robots are coming’

For sensors to collect data from a whole vineyard, they must be mounted on observation platforms such as satellites, manned or unmanned aircraft, ground-based vehicles, or robots.

According to Poblete-Echeverría, drones represent a radical shift for aerial sensors. “We are almost at a moment where unmanned aerial vehicles are fully automatic, and we can have excellent resolution with the images we obtain.”

Ground-based sensors, on the other hand, can be mounted on tractors or quad bikes and linked to global positioning systems to obtain accurate spatial data.

“Robots are coming – it’s certain that they will soon be used to perform specific tasks and capture information,” says Poblete-Echeverría.

Already, SU’s department of viticulture and oenology has collaborated with South Africa’s Council for Scientific and Industrial Research to implement a prototype robotic platform specifically designed for vineyards.

Called ‘the Dassie’, the robot trundles up and down the work rows, carrying a suit of sensors to collect data about each vine it passes. This information is fed to a computer programme to evaluate aspects such as canopy growth or the temperature and humidity around leaves and grape bunches.


Professor Carlos Poblete-Echeverría (left) with two of his students using a drone and 'the Dassie', a robotic platform designed for vineyards, Image provided

AI in agriculture

Increasingly, AI is used to comprehend the data extracted from sensors that are based on observational platforms. Computers are trained to simulate human reasoning to draw conclusions from data.

“Today, AI is a hot topic. It’s amazing what we can do with it,” says Poblete-Echeverría.

He is confident about the value of new technologies, but cautions that AI “is not magic”.

“We need to follow certain steps to produce correct models, and models should be trained and tested properly before being released to the market.”

Future of farming

The transformative potential of big data in agriculture holds great promise. By integrating advanced technologies and fostering interdisciplinary collaborations, initiatives like the SAI are setting the stage for a more data-driven and resilient agricultural sector.

As researchers work to unlock actionable insights from the vast amounts of data, there is cautious optimism that this approach can lead to improvements in food security, sustainability and climate resilience.

The future of farming may lie in the effective fusion of data science and agricultural practices, potentially ushering in an era of smarter, more efficient, and sustainable agriculture.