Computer Vision For Greenhouse
AI-Powered Video Analytics and Sorting for NVIDIA Jetson Nano and Xavier
A solution has been developed to automate production processes using NVIDIA Jetson Nano and Xavier devices. The project combines advanced video analytics and artificial intelligence technologies, optimized for conveyor belt operation, ensuring high accuracy and data processing speed.
Project Features:
- Cube Content Identification
The system automatically analyzes cubes on the conveyor belt, identifying the presence and quantity of seeds. - Plant Recognition and Characteristic Analysis
A plant recognition algorithm has been implemented, allowing for the identification of plant type, condition, and other key parameters. - Class-Based Sorting
Based on the collected data, objects are classified, enabling real-time automated sorting. - Data Annotation Software
A tool has been developed for annotating images and videos directly on NVIDIA Jetson devices, simplifying data preparation for model training.
Project Highlights:
- High Performance
Optimization for NVIDIA Jetson platforms ensures real-time data processing even under high load. - Algorithm Accuracy
Advanced computer vision and machine learning models are used, providing high recognition accuracy. - Integration with Conveyor Systems
The system is fully adapted for operation on production lines with high flow rates. - Easy Configuration and Scalability
The project's interface and architecture allow for easy adaptation to new tasks and object types.
Application
The project is aimed at automating processes in agriculture, food production, and other industries where object classification and sorting are required.
This solution helps significantly improve the efficiency of production processes, reduce errors, and lower costs, making it an indispensable tool for businesses.
