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This research explores the application of CNNs for image categorisation to differentiate honey bees from bumble bees. The goal is to apply deep learning methods, and establish an accurate model that will be able to distinguish between these two species.
Our methodology includes:
- Data Collection: Compiling a collection of bee pictures some of which will be honey bees while others will be bumble bees.
- Data Pre-processing: The process of making the parameters of an image more usual and increasing the size of the image dataset in order to make the model more effective.
- Model Training: Retraining then on the bee image dataset a pre-trained model, CNN model.
- Model Evaluation: Measuring the performance of the model and checking accuracy rate, precision, recall and other parameters.
- Prediction Generation: To make predicted numbers for a single image with the help of the trained model.
The outcomes show that with the aid of the pre-trained CNN model it is possible to classify the bee images with a high degree of accuracy. When it comes to ecological studies and conservation, the model’s ability to distinguish between honey bees and bumble bees is valuable. This research focuses on the deep learning approach to the problem of image classification and contributes the general field of computer vision.
