Passion fruit pests and diseases in Uganda lead to reduced yields and decreased investment in farming over time. Most Ugandan farmers (including passion fruit farmers) are smallholder farmers from low-income households, and do not have sufficient information and means to combat these challenges. Without the required knowledge about the health of their crops, farmers cannot intervene promptly to avoid devastating losses.
The Marconi Society Machine Learning Laboratory at Makerere University is addressing the lack of a reliable, timely diagnostic platform for passion fruit diseases by developing a low-cost hand-held diagnostic device (based on the Raspberry Pi) making use of state-of-the-art machine learning techniques.
The challenge requires that these we classify the disease status of a plant given an image of a passion fruit. The task is a Computer Vision task and unstructured.
The model implement fastai library. (version 2.4.1 preferably)
The competition dataset could be find on: https://zindi.africa/hackathons/indabax-uganda-2021/data
The evaluation metric used is Accuracy
At the close of the hackathon,
Rank - 7th out of 12
Ranked Private Leaderboard score - 0.974789915966387
Best Private Leaderboard score (unselected) - 0.980392156862745 (code uploaded)