In this repository I will share my approach for solving the task given by the Plant Pathology Competition 2021 - FGVC8
A brief description of the task: " Apples are one of the most important temperate fruit crops in the world. Foliar (leaf) diseases pose a major threat to the overall productivity and quality of apple orchards. The current process for disease diagnosis in apple orchards is based on manual scouting by humans, which is time-consuming and expensive.
Although computer vision-based models have shown promise for plant disease identification, there are some limitations that need to be addressed. Large variations in visual symptoms of a single disease across different apple cultivars, or new varieties that originated under cultivation, are major challenges for computer vision-based disease identification. These variations arise from differences in natural and image capturing environments, for example, leaf color and leaf morphology, the age of infected tissues, non-uniform image background, and different light illumination during imaging etc.
The main objective of the competition is to develop machine learning-based models to accurately classify a given leaf image from the test dataset to a particular disease category, and to identify an individual disease from multiple disease symptoms on a single leaf image."
Link to the competition: https://www.kaggle.com/c/plant-pathology-2021-fgvc8/overview