Plant Health Monitoring System

[ArindamHore-Student]

I’m proposing to build a Plant Health Monitoring System that uses image recognition to detect plant diseases early, which helps farmers prevent crop loss. This project is important because agriculture relies on timely disease detection, and my system automates that process. By using ML and OpenCV, as part of GSSOC , I’ll contribute a meaningful project that invites collaboration and real-world impact.

[Aryan-Chharia]

Go Ahead!

[alo7lika]

A Plant Health Monitoring System can be built using a combination of computer vision, machine learning, and IoT technologies. The system would typically consist of the following steps:

• Data Collection: Use sensors (temperature, humidity, soil moisture) and cameras to capture environmental data and images of the plants.
• Preprocessing: Clean and normalize the collected data. For image data, perform operations like resizing, noise reduction, and augmentation (to increase dataset variety).
• Model Training: Use a Convolutional Neural Network (CNN) to analyze plant images for diseases or nutrient deficiencies, and a machine learning regression model (e.g., Random Forest, Gradient Boosting) to predict plant health based on sensor data.
• Real-Time Monitoring: Use IoT devices to continuously monitor environmental conditions and send data to the model for analysis.
• Prediction and Alerts: The trained model detects issues such as diseases or nutrient deficiencies, providing alerts and recommendations through a user interface (e.g., mobile app or web dashboard).
• Actionable Insights: Use insights from the model to guide interventions like adjusting irrigation, applying fertilizers, or treating plant diseases.

This system helps in automating plant care and improving yield by providing timely insights into plant health.

assign me this work @Aryan-Chharia

[Aryan-Chharia]

@ArindamHore-Student ?

[venkat-2811]

Hello Aryan-Chharia , assign me the project.