https://github.com/prinuvinod/agrivision

This a Website made for Weed Detection Using YOLO v3
https://github.com/prinuvinod/agrivision

ajax css flask fullstack-development html5 javascript machine-learning python3 yolov3

Last synced: about 2 months ago
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This a Website made for Weed Detection Using YOLO v3

• Host: GitHub
• URL: https://github.com/prinuvinod/agrivision
• Owner: PrinuVinod
• Created: 2023-10-08T13:53:31.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-21T09:20:38.000Z (10 months ago)
• Last Synced: 2024-04-16T11:21:47.544Z (8 months ago)
• Topics: ajax, css, flask, fullstack-development, html5, javascript, machine-learning, python3, yolov3
• Language: HTML
• Homepage:
• Size: 39.5 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# AgriVision: Transforming Agriculture Through Precision Weed Detection

AgriVision is a groundbreaking agricultural technology that harnesses the power of computer vision and machine learning to revolutionize weed detection and management in farming. This innovative system empowers farmers to identify and combat weeds efficiently, leading to increased crop yields, reduced resource wastage, and sustainable agricultural practices. AgriVision operates in real-time, utilizing high-resolution imaging to spot weeds and provide farmers with tailored recommendations for precise intervention. By integrating cutting-edge technology with the age-old practice of farming, AgriVision paves the way for a more prosperous and environmentally conscious agricultural future.

---

## `Features`

1. Real-Time Weed Detection: AgriVision employs high-resolution cameras and sophisticated computer vision algorithms to detect weeds as they appear in real-time, ensuring timely intervention.

2. Crop-Specific Adaptability: The system's algorithms adapt to various crop types and weed species, delivering customized recommendations for each unique agricultural setting.

3. Actionable Insights: AgriVision provides actionable recommendations to farmers, including targeted herbicide application, manual removal, or other weed management strategies.

4. Crop Health Optimization: By identifying and addressing weeds promptly, AgriVision helps maintain crop health, reducing competition for vital resources.

5. Resource Efficiency: Farmers can optimize the use of herbicides and manual labor, reducing operational costs and minimizing environmental impact.

6. User-Friendly Interface: AgriVision offers an intuitive, user-friendly interface accessible on various devices, simplifying access to critical information and recommendations.

7. Scalability: The system can scale to cover large agricultural areas, accommodating diverse crop types and regional requirements.

## `Created Using`

> FrontEnd > ***HTML***, ***CSS***, ***JavaScript***


BackEnd > ***MongoDB***


Framework > ***Flask***

### `Installation`

```

python -m venv venv

venv\Scripts\activate

pip install -r requirements.txt

```