Precision technology can help Africa’s farmers weed out risk
AI and machine learning models used to roll out easy-to-digest analytics.
Africa’s agriculture sector will benefit significantly from satellite technology and data analytics, according to industry experts.
“African farmers are still hesitant about using the present-day technologies as they have little experience operating digital tools and a poor understanding of the benefits the agritech implementation can bring them in the short- and long term,” said Brijesh Thoppil, Director of Strategic Partnerships at EOS Data Analytics.
The company has over 37,000 users in Africa and its market includes approximately 33 million smallholder farms.
Thoppil said that landowners can use satellite imagery analytics to adjust their crop growing strategy, plan seed planting, and predict weather changes that could affect crops.
Using satellite-driven data, farmers can ration fertiliser application.
“For example, it’s possible to manually input the amount of nitrogen, phosphorus, and potassium required to support undernourished field plots, avoiding the areas with potentially high weed growth,” he commented.
His assertion is supported by a recent Brookings report titled From subsistence to disruptive innovation Africa, the Fourth Industrial Revolution, and the future of jobs, which details the telecommunications infrastructure challenge within agriculture and how this restricts operators from being able to adopt and use technology.
“One reason for the slow pace of technology diffusion among the African agricultural sector is limited internet access. In rural areas, less than 30% of adults in rural areas report having access, mostly to mobile broadband at 2G and 3G speeds, which is not fast enough for many applications,” the report stated.
Thoppil added that now, when higher crop yields are needed, technology can be the game-changer in helping farmers grow and take to market better quality product and more of it.
The company collects miscellaneous data and transforms this using AI and machine learning models to roll out easy-to-digest analytics.
“Firstly, we take Sentinel and Landsat satellite imagery and combine it with the agronomic data - such as soil moisture, yield statistics, weather patterns, and soil types - we collect from open yet reliable sources,” he explained.
These data sets are processed with mathematical algorithms to deliver insights users need to manage crops.
“Precision agriculture technologies boost farm productivity and profitability by employing rational farming approaches that help improve yields and reduce input costs,” said Thoppil.