AI for Optimizing Crop Farming through Sustainable Practices in Africa
Join a global team of 50 AI changemakers in this high-impact 2-month challenge to optimize crop farming with sustainable practices.
This challenge requires experience in Data Analysis and Machine Learning.
In Sub-Saharan Africa, more than 65% of food produced is from smallholders, but issues such as over-dependence on chemical input are not good for the soil, environment, and food. Soil microbes diminish by continuous use of these chemicals, reduction in biodiversity, and in the long term, the land productivity depreciates such that a higher amount of the chemicals is required to maintain the yield. Small scale farmers do not derive full benefit from their farmlands, as the chemical inputs account for more than 50% of the farm operating cost. These farmers are among the poorest in the society and prices of food keep increasing.
The project goals
The goal is to apply several data analyses and potentially AI-related methodologies to find answers to the following questions:
How to provide ready to apply agro-information for crop farmers (in local language) and farm managers via a dashboard to help them make maximum use of their farmlands
How to reduce overhead costs, get better yields, and effectively manage a unique bee-centered cropping system
What crops to plant (based on local data from the farms), when to plant, where to plant, when to harvest honey, the quantity of water required to grow the crops, and soil nutrients requirement