Gather a large dataset of images of birds of different species. This dataset includes images of birds from various angles, in different environments and lighting conditions, etc.
Label each image with the species of bird. This is a crucial step performed by our chief birder, Jerar, and requires expertise in bird species identification.
Clean and preprocess the data to remove any irrelevant information, resize images to a consistent size, and convert them to a format suitable for use in machine learning models.
Meaningful features in the images can be used to identify different bird species. This would involve using computer vision techniques to detect key features, such as the shape of the bird's beak, the pattern of its feathers, etc.
Train a machine learning model using the labeled and preprocessed data. A popular approach for image classification tasks is Convolutional Neural Networks (CNNs), which are a type of deep learning algorithm.
We will evaluate the performance of the trained model on a separate dataset that it has not seen before. This will give us an idea of how well the model is able to adapt to new data.
Deployment of the trained model in a suitable environment for use in a real-world scenario. This would involve integrating the model into a mobile app