Authors :
Dr. A Saravanan; Sai Chandan P Reddy; Swastik H. S; Allabhaksha Lalmastar; Priyanshi Thakur
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3B4FO9j
DOI :
https://doi.org/10.5281/zenodo.6867632
Abstract :
India is mainly an agriculture based country.
Security of agricultural farm is of utmost importance for
protecting the produce. Not being able to make the
grown crops to the market is another side of the
problem. Valuable investments and efforts can be ruined
in minutes intentionally or unintentionally by persons or
by animals. Introducing machine learning to this
problem paves a way to smart agriculture. The proposed
system employs Raspberry Pi board to detect any
malicious activities or motion in the farm land and
triggers the thermal camera to take picture of the scene
image. The captured image is fed into the trained CNN
(Convolutional Neural Network) model of deep learning.
And after detecting the class of the animal, a sound file
mapped to that class is played at little higher frequency,
so as to scare away the animal. The result shows that the
system is 80-95% accurate and 100% consistent for
detecting any suspicious movement and to act
accordingly.
Keywords :
Deep Learning, Convolutional Neural Network, Thermal Cameras, Tensor flow, Fastai, Raspberry Pi, PIR sensor etc.
India is mainly an agriculture based country.
Security of agricultural farm is of utmost importance for
protecting the produce. Not being able to make the
grown crops to the market is another side of the
problem. Valuable investments and efforts can be ruined
in minutes intentionally or unintentionally by persons or
by animals. Introducing machine learning to this
problem paves a way to smart agriculture. The proposed
system employs Raspberry Pi board to detect any
malicious activities or motion in the farm land and
triggers the thermal camera to take picture of the scene
image. The captured image is fed into the trained CNN
(Convolutional Neural Network) model of deep learning.
And after detecting the class of the animal, a sound file
mapped to that class is played at little higher frequency,
so as to scare away the animal. The result shows that the
system is 80-95% accurate and 100% consistent for
detecting any suspicious movement and to act
accordingly.
Keywords :
Deep Learning, Convolutional Neural Network, Thermal Cameras, Tensor flow, Fastai, Raspberry Pi, PIR sensor etc.
