How to Create a Machine Learning Model That Can Identify Lyme-Disease Transmitting Ticks!

This tutorial guides users through identifying tick species using machine learning. It covers setting up a Google Colab environment, collecting tick images from iNaturalist, and performing image augmentation. The tutorial also explains how to train a model to improve tick identification accuracy, comparing it to general public performance.

Why is it important to identify different types of ticks?

• To determine the risk of tick-borne diseases

• To find out their habitat

• To estimate their population

• To know their diet

What is the first step in setting up the Google Colab environment for tick identification?

• Downloading the Tick Identification zip folder

• Collecting images from iNaturalist

• Running the machine learning model

• Performing image augmentation

Which website is recommended for collecting tick images for the dataset?

• Flickr

• iNaturalist

• Google Images

• Shutterstock

What is the purpose of converting all images to JPEG format?

• To improve image quality

• To ensure uniformity for the machine learning model

• To make them compatible with all devices

• To reduce file size

What is image augmentation?

• A way to label images

• A method to compress images

• A technique to create more images from existing ones

• A process to enhance image quality

What can be done to improve the accuracy of the tick identification model?

• Use fewer images

• Skip the validation step

• Gather more data and adjust augmentation parameters

• Reduce the number of tick species

What metrics can be used to evaluate the model's performance?

• Precision, recall, and F1 score

• Image resolution and size

• Number of images processed

• Time taken to run the model