Authors :
Aibin Abraham; Bibin Mathew; Devika Panikkar; Jaya John
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/nhdexknf
DOI :
https://doi.org/10.5281/zenodo.8031673
Abstract :
Agriculture plays a crucial role in the
economy, and farmers strive to increase their crop yields
annually. Hence effective reconnaissance is vital for
farmlands and rural terrains to prevent unauthorized
access and protect crops from animal damage. The
expansion of agricultural lands into wildlife territories has
escalated human-wildlife conflicts, with crop destruction
by animals becoming a major concern. To address this,
our project proposes an alerting system using YOLOv3,
a real- time object detection algorithm based on deep
convolutional neural networks, to classify and monitor
animals that intrude into agricultural areas. This
algorithm enables efficient iden-tification and tracking of
animals, aiding in mitigating crop damage and ensuring
the preservation of wildlife in their natural habitats.
Whenever an animal is detected, the system will sendan
SMS to the landowner and forest officials, providing them
with early warning notifications to take appropriate actions
based on the intruder’s type. This proposed system offers
significant benefits to farmers, helping them increase
yields and protect both humans and livestock from wild
animal attacks.
Keywords :
Image Processing, YOLO Algorithm, Raspberry Pi, Convolutional Neural Network, GSM Module.
Agriculture plays a crucial role in the
economy, and farmers strive to increase their crop yields
annually. Hence effective reconnaissance is vital for
farmlands and rural terrains to prevent unauthorized
access and protect crops from animal damage. The
expansion of agricultural lands into wildlife territories has
escalated human-wildlife conflicts, with crop destruction
by animals becoming a major concern. To address this,
our project proposes an alerting system using YOLOv3,
a real- time object detection algorithm based on deep
convolutional neural networks, to classify and monitor
animals that intrude into agricultural areas. This
algorithm enables efficient iden-tification and tracking of
animals, aiding in mitigating crop damage and ensuring
the preservation of wildlife in their natural habitats.
Whenever an animal is detected, the system will sendan
SMS to the landowner and forest officials, providing them
with early warning notifications to take appropriate actions
based on the intruder’s type. This proposed system offers
significant benefits to farmers, helping them increase
yields and protect both humans and livestock from wild
animal attacks.
Keywords :
Image Processing, YOLO Algorithm, Raspberry Pi, Convolutional Neural Network, GSM Module.
