Radiant Earth Crop Detection in Africa challenge
“Sample fields (color coded with their crop class) overlayed on Google basemap from Western Kenya.” (Radiant Earth)
The Radiant Earth Foundation announced the winners of their Crop Detection in Africa challenge . The competition was hosted on Zindi, a platform that connects African data scientists to organizations with “the world’s most pressing challenges”—similar to Kaggle. Detecting crops from satellite imagery comes with extra challenges in Africa due to limited training data and the small size of farms.
A total of 440 data scientists across the world participated in building a machine learning model for classifying crop types in farms across Western Kenya using training data hosted on Radiant MLHub. The training data contained crop types for a total of more than 4,000 fields (3,286 in the training and 1,402 in the testing datasets). Seven different crop classes were included in the dataset, including: 1) Maize, 2) Cassava, 3) Common Bean, 4) Maize & Common Bean (intercropping), 5) Maize & Cassava (intercropping), 6) Maize & Soybean (intercropping), 7) Cassava & Common Bean (intercropping). Two major challenges with this dataset were class imbalance and the intercropping classes that are a common pattern in smallholder farms in Africa.
As climate change will make farming more difficult in many regions across the world, this type of work is vital for protecting food production capacities. Knowing what is being planted where is an important first step in this process. Last year I covered the AI Sowing App from India (DT #20), another climate resilience project that helps farmers decide when to plant which crop using weather and climate data; better data on crop types and locations can certainly help initiatives like that as well.