| dc.description.abstract | Abstract: Every year, crop damaged by wild
animals is dramatically increasing in Sri
Lanka. It often poses risks to humans and
animals. Since more and more wild animals
are causing damage to their cultivation;
humans could not tolerate it. Therefore, they
require an effective mechanism to overcome
this situation. With that background, the
objective of this study is to detect wild
animals before entering into the crop fields
and implementing appropriate scare-away
mechanisms in real-time. The presence of
the animal will be sent to the farmer via a
mobile application. In this study, two
Convolutional Neural Network (CNN)
classification models have been developed
using the transfer learning approach with
the VGG-16 as a pretrained model to detect
elephants, wild boars, and buffalos. Both two
models were combined and runs on
Raspberry pi, which acts as the processing
unit for the system, captures the images of
animals, and predicts it. Whenever the
presence of the animal senses by the thermal
sensor which is installed on Arduino, it sends
a trigger to capture the image. Based on the
prediction sudden flashes of light,
ultrasound, and bee sound will be produced
to scare away the animals. The mobile
application was developed using react native
which is used to alert the user about the
animal, connected through the Firebase
database. The findings of this research
indicate that the accuracy rate of the
classification model is 77 percentage. This
system significantly reduces human-animal
conflict in crop fields by automatically
implementing scare-away mechanisms
based on the prediction. | en_US |
