Securing Crops and Equipment
Challenge:
Securing agricultural assets from theft and monitoring crop growth and field activities requires advanced technology to detect and respond to potential threats effectively.
Aim:
Implement edge ML algorithms to enhance security and provide insights into crop growth and field activities.
Solution:
Deploy edge ML devices to continuously capture and analyze video and audio data. Machine learning models will be used to:
- Detect and identify people and vehicles in the monitored area, distinguishing between normal and suspicious behavior.
- Monitor crop progress, including growth status, height, and size, to assist in agricultural decision-making.
- Recognize and classify audio patterns and noise to detect unusual activities or disturbances.
Benefit:
This solution improves security by enabling rapid detection of threats and provides actionable insights into crop development and field conditions. Enhanced monitoring capabilities lead to better protection of assets and optimized agricultural practices.