Paper PublishingAnimal Detection in Farms Using OpenCV
Objective
We propose an AI based system which helps in detecting and identifying the animal which tries to enter the field and alerts the farmer to take certain actions further.
Abstract
Animal intrusion in farms causes huge losses in agricultural revenue which a farmer cannot bear. Computer Vision are being increasingly applied in agricultural field for higher productivity by automating tasks. Agriculture is the most important sector of Indian Economy but the issue of damage to crops by wild animals has turned into an important social issue in current occasions.
So far, many of the farmers reply on guards to guard their crops which increases the overhead costs. But, due to current climate conditions, crop failure rate has increased dramatically. Debt in agricultural sector has increased tremendously. In these situations, a farmer cannot expect further destruction of crops and neither can afford increase costs in farming.
Keywords: Computer Vision, OpenCV, Deep Learning, Animals, MobileNet, MS COCO.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used: Pandas, Numpy,os,TensorFlow,opencv,Matplotlib.
Learning Outcomes
- Importance of classification.
- Scope of animal detection.
- Use of computer vision techniques.
- Importance of PyCharm IDE.
- Benefits of MobileNet model.
- What is OpenCV?
- Need of using pre trained model.
- Process of debugging a code.
- Input and Output modules
- How test the project based on user inputs and observe the output
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
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