Abstract:
Snake bites in Sri Lanka cause death to nearly
100 people each year. Among the many reasons for this
condition is the inability of people to identify the snake
type which prevents administering the appropriate anti
venom treatment. Misidentification of snakes also causes
threats to the existence of harmless snakes that
contributes to the biodiversity of reptile species. A
conducted with 223 participants to ascertain the ability of
people to correctly identify the snake type when an image
of a snake is available revealed that the majority
the participants was unable to recognize the snake type
This paper presents some of the survey results and
review of various methods such as k-nearest
(KNN), Support Vector Machines (SVM), Image Processing
techniques, Probabilistic Graphical Models, Artificial
Neural Networks (ANN), Convolutional Neural Networks
(CNN) which are used to automatically identify objects
birds, marine species, humans and animals that could be
applied for snake recognition to assist people
identifying snake types which can contribute in reducing
morbidity and mortality due to snake bites as well as to
minimize the harm caused to innocent snake types
