In an era where pollinators like bees play crucial roles in our ecosystems and agriculture, their survival becomes paramount. "Naïve Bees: Deep Learning with Images" is an innovative project aimed at leveraging the power of artificial intelligence to distinguish between different species of bees, specifically honey bees and bumble bees.

With the backdrop of challenges such as colony collapse disorder threatening bee populations, this project seeks to provide a tool for researchers and ecologists to quickly and accurately identify bee species from images. The differentiation between honey bees (Apis) and bumble bees (Bombus) through machine learning could significantly enhance our understanding of bee behavior, population dynamics, and health.

The primary objective of this project is to use convolutional neural networks (CNNs), a class of deep learning algorithms, to classify images of bees. This not only aids in the rapid collection of field data but also contributes to conservation efforts by monitoring bee diversity and population health.

Pollinating bees are critical to the health of ecosystems and agriculture worldwide. By developing and refining machine learning models to identify bee species, we can better track and understand the impacts of environmental changes, diseases, and conservation efforts on these essential insects.

Data Collection: Utilizing a dataset of labeled images of honey bees and bumble bees. Machine Learning Model: Implementing a CNN to learn and distinguish between the bee species based on image data. Evaluation and Iteration: Testing the model's accuracy and refining its architecture and parameters to improve identification capabilities.

DataCamp for the initial project inspiration and dataset.