Abstract:
Snake bites in Sri Lanka cause death to nearly 100 people annually.
Administering the appropriate anti-venom treatment for snake bite victims gets
delayed causing complications as a result of the inability of people to identify
the snake. Incorrect identification of snakes also causes threats to the existence
of harmless snakes threatening the biodiversity of Sri Lanka. As a remedial
measure to these problems, the first automatic snake identification from a given
image using convolutional neural networks (CNN) is described in this study
using 2000 images from each of six snake species found in Sri Lanka to train
five CNN models. Four of the models were developed using the pre-trained
architectures InceptionV3, VGG16, ResNet50 and MobileNet using transfer
learning while the fifth model was developed from scratch. This study revealed
that MobileNet with transfer learning yielding an accuracy of 90.5% is the most
suitable model for automatic snake identification.
