🐝 Bees Project

📖 Overview

This project applies AI techniques to support bee research and conservation. It focuses on two key challenges:

Pollen Classification – Detecting pollen presence on bees using a CNN.

Bee-eater Sound Detection – Identifying bee-eater bird sounds (a natural predator) using an LSTM-based audio model.

A presentation is also included to detail the data loading, preprocessing, training, and testing pipelines.

📂 Project Structure

pollen_classification(1).ipynb – CNN-based model for pollen detection.

bee_eater_sound_detection(1).ipynb – LSTM-based model for bee-eater sound detection.

presentation.pptx – Overview of methodology, preprocessing, and model evaluation.

🔬 Features

Pollen Detection 🌼

CNN model trained on bee images.

Identifies and classifies pollen presence.

Bee-eater Sound Detection 🎶

LSTM-based audio classifier.

Detects bee-eater calls as potential threats to bees.

🌍 Applications

Helps monitor pollination activity through pollen detection.

Detects environmental threats (bee-eaters) to bee populations.

Can be extended for ecological monitoring and bee conservation strategies.