Mobile Application, Sensors, and Satellite Imaging are Central to Developing Successful Smart Farming Practices in Africa
This study focuses on an understanding of smart farming practices and the ways in which farmers can make data-driven decisions regarding crop rotation, planting and harvesting times, soil management, and weather prediction. The study will provide an analysis of the Sub-Saharan African region, with information on the Middle East and North Africa added as necessary. The forecasting will include both the regions.
The technology scope will cover the following categories: mobile application, sensors, robots, satellite imaging, and drones. The categories for start-ups are finance and insurance, logistics and equipment sharing, communication tools, integrated platforms, precision agriculture, and IoT network.
The study provides detailed information on key emerging technology convergence leading to the development of smart farming along with the key start-ups across the agriculture value chain, which requires automated processes and systems to sustainably increase output and influence economic growth.
The study also describes the factors influencing technology convergence in the farming sector such as autonomous systems, data management and analytics, robots, wireless communication, and regulations, whilst highlighting the technologies that will play a significant part in improving agricultural processes by increasing control over resources and enhancing productivity globally and regionally.
In terms of Africa’s digital agriculture journey, it has been ascertained that mobile connectivity and applications are enabling farmers to have access to market-related information, digital financial services, and innovative services such as tractor sharing. Furthermore, the combination of ICT solutions such as precision equipment, Internet of Things (IoT), sensors, geo-positioning systems, data analytics, and drones is changing the way in which traditional farming is conducted in Africa.
Key Issues Addressed
What are the main components in the agriculture value chain and who are the key stakeholders?
What are the key global trends shaping the agricultural sector?
What are the key drivers and restraints affecting the development of the smart agriculture market?
What is the size of the MEA market?
Who are the key startups in Africa driving the development and adoption of smart farming?
How are emerging African start-ups advancing in the smart farming market?
What are the potential growth opportunities that smart farming companies can capitalise on to grow their businesses and stay competitive?
Key Conclusion
- Identify Economies of Scale: Global suppliers of agricultural products and equipment are the more attractive market for IoT than fragmented farming production. Nonetheless, some mega-farms can offer the scale to make IoT service provision profitable.
- Gather Agriculture Data: African start-ups are gathering predictive intelligence that they believe will create an unassailable competitive advantage in key areas of agriculture and related industries. Predictions for the impact of weather, soil conditions, and livestock health will become increasingly accurate for Africa.
- Monetise Data to Multiple Customer Types: Across all stages of the agriculture value chain, agricultural data is valuable for government departments, insurers, commodity traders, and developers of machine learning systems, among others.
