otoosakyidavid/Passion-Fruit-Disease-Detection

My solution to an hackathon hosted on Zindi.africa by IndabaX Uganda # Passion-Fruit-Disease-Detection 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. ## Type of Task 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) ## Dataset The competition dataset could be find on: zindi.africa ## Evaluation Metric The evaluation metric used is **Accuracy** ## Leaderboard Score 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)