Improving Ecological Surveys with Deep Learning: Reptile and Amphibian Classification Using CNNs

Reptiles and amphibians together make herpetofauna, which is essential for the stability and health of ecosystems. These species' sensitivity to toxins and habitat disturbances helps them to not only control prey numbers but also act as markers of environmental changes. Large datasets or subtle interspecies changes challenge conventional classification techniques in handling, which makes ecological monitoring and conservation activities difficult. This paper presents a Convolutional Neural Network (CNN) model meant to raise the amphibian and reptile categorization accuracy. With a 6,052-image dataset, our model attained an 83% accuracy. Through automatically learning and feature extraction from images, the CNN method improves species identification accuracy and efficiency. Through more precise biodiversity estimates, this progress promotes sustainable development goals connected to life on land and aquatic habitats. In line with more general ecological and environmental goals, our work shows the possibilities of deep learning technology to solve conservation issues and support the sustainable management of natural resources.