Animal Species Prediction Using Deep Learning

Abstract

In the face of escalating biodiversity loss, effective monitoring of animal species is critical for conservation efforts. This study presents a deep learning approach for species detection and a multimodal feature identification technique for animals vulnerable to poaching. The suggested prediction system recognizes objects automatically by the application of deep learning techniques to detect objects and then recognize them by using computer vision techniques, and it is triggered when an object enters its range of vision. Convolutional neural network (CNN) architecture allows us to train a system that can automatically recognize objects in photos by filtering the datasets. The CNN model uses multiple layers to analyze images. It contains convolution layers to extract important features of the image, a max-pooling layer to reduce the size of the data (dimensions) while preserving the important information. After these layers, the processed information passes through an MLP (multi-layer perceptron) and predicts the final label of the animal in the output layer. MLP contains a number of hidden layers to process image data. By providing well-labeled and clean datasets, these models can easily identify different animals. It can be useful in ongoing animal conservation activities by alerting authorities in real time when endangered species are detected. It can be enhanced in the future with the training of models with a real-time dataset taken from wildlife sanctuaries.