Microplastics Detection

The Microplastics Detection Model is a machine learning-based system designed to identify and classify microplastic particles in environmental samples such as water and marine ecosystems. By analyzing spectral, chemical, and microscopic imaging data, the model enhances the detection and quantification of microplastics, supporting environmental monitoring and pollution control efforts.

Key Features:
Data Processing & Feature Engineering: Preprocesses microscopic and spectroscopic data, removing noise and enhancing image clarity.
Machine Learning & Deep Learning Algorithms: Utilizes CNNs (Convolutional Neural Networks), Random Forest, and SVM for microplastic classification.
Model Evaluation: Assesses accuracy using precision, recall, F1-score, and confusion matrix.
Deployment: Integrated with Flask, FastAPI, or cloud-based systems for real-time detection and monitoring.

The Microplastics Detection Model is a machine learning-based system designed to identify and classify microplastic particles in environmental samples such as water and marine ecosystems. By analyzing spectral, chemical, and microscopic imaging data, the model enhances the detection and quantification of microplastics, supporting environmental monitoring and pollution control efforts.

Key Features:
Data Processing & Feature Engineering: Preprocesses microscopic and spectroscopic data, removing noise and enhancing image clarity.
Machine Learning & Deep Learning Algorithms: Utilizes CNNs (Convolutional Neural Networks), Random Forest, and SVM for microplastic classification.
Model Evaluation: Assesses accuracy using precision, recall, F1-score, and confusion matrix.
Deployment: Integrated with Flask, FastAPI, or cloud-based systems for real-time detection and monitoring.