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.
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.
install this tooling
A simple way how to use this basic functions:
! pip install https://github.com/Borda/kaggle_plant-pathology/archive/main.zip
run notebooks in Kaggle
- Plant Pathology with Flash
- Plant Pathology with Lightning
- Plant Pathology with Lightning [predictions]
run notebooks in Colab
I would recommend uploading the dataset to you personal gDrive and then in notebooks connect the gDrive which saves you lost of time with re-uploading dataset when ever your Colab is reset... :]
Training progress with ResNet50 with training for 10 epochs > over 96% validation accuracy: