An Africa based Agriculture Research Institute utilized CNN based solution to map crops to farms
Africa as a continent has been struggling with poverty. The political system is most of the countries are not stable. The high population growth rates are not helping in reducing the absolute number of people living below extreme poverty line. The rise in food imports in Africa has exacerbated the problem. Agriculture in Africa provides employment to two-thirds of the population and contributes between 30-60% of GDP for each country.
Locate, track & improve growth of crops
One of the avenues to boost agricultural output for small scale farmers is machine learning driven crop and growth monitoring for guided improvements in productivity.
- Identified 10 crop types with average accuracy of 92%
- Enabled an improvement of 6% in yield estimates
- Opened doors for scaling the solution across a wider region
Crops under cultivation and crop-farm mapping improves food security through better yield estimates, crop rotation, and soil productivity.
Africa’s struggle with poverty
Agriculture in Africa is dominated by smallholdings, constraining use of technology and other interventions to boost productivity (and income) in a sustainable manner. The struggle is to minimize the gap between actual and potential yields, enabling smallholders grow sufficient crops to feed their families, and a surplus to sell. This would help them meet food security needs and generate income, helping them move out of poverty.
Drones capture data from farms at regular intervals, that can be analysed for identifying area under cultivation, predict expected yield and help farmers with optimum planting and harvesting strategies. The hope is that technology driven solutions can provide scalable solutions that would allow us to create a data-driven approach towards crop management.
Machine learning driven fight for food security
Harnessing machine learning to help the most vulnerable people is the best way to use it.
Machine Learning solution over farm imageries captured through drones is an important step towards a bigger data-driven approach towards food security and income growth in agriculture. The farm to crop mapping exercise opens doors to:
a. Faster and more accurate prediction of yields – at farm, village and municipal area level
b. Efficient monitoring of crop growth, and addressing the risks
c. Better soil productivity and crop rotation Machine Learning solutions might change the way we farm and grow crops – reducing poverty in the process.