PLANTVISION AI
Deep learning plant species classification system achieving 95% accuracy across 25 species and 40,000+ images.
PROJECT OVERVIEW
PlantVision AI is a personal research project that leverages deep learning to accurately classify plant species from images. Using PyTorch and advanced computer vision techniques, this system was developed to demonstrate the practical application of neural networks in botanical identification.
The project involved processing a large-scale dataset of over 40,000 plant images across 25 different species, implementing state-of-the-art deep learning architectures, and achieving a remarkable 95% classification accuracy.
PROJECT OBJECTIVES🎯
Model Accuracy
Create a highly accurate plant species classification model using deep learning techniques
Large-Scale Processing
Process and train on a large-scale dataset of 40,000+ plant images
GPU Pipeline
Implement GPU-accelerated training pipeline for efficient model development
Architecture Optimization
Optimize neural network architecture for maximum classification performance
TECHNICAL APPROACH
Custom CNN Architecture
Designed and implemented a custom convolutional neural network architecture optimized for plant species classification. The architecture features multiple convolutional layers, batch normalization, and dropout for robust performance.
Transfer Learning
Leveraged transfer learning techniques using pre-trained models as a foundation, fine-tuning them on the plant species dataset to achieve higher accuracy with less training time.
Data Preprocessing Pipeline
Developed a comprehensive data preprocessing pipeline including image augmentation, normalization, and validation split to ensure robust model training and evaluation.
GPU Computing
Implemented GPU-accelerated training using CUDA to significantly reduce training time and enable experimentation with larger models and datasets.
KEY ACHIEVEMENTS
95% Classification Accuracy
Achieved 95% accuracy in identifying 25 different plant species, demonstrating the effectiveness of the deep learning approach
Large-Scale Dataset Processing
Successfully processed and trained on 40,000+ plant images with efficient data augmentation
Scalable Solution
Created a scalable plant species identification system that can be extended to additional species
Robust Validation Methodology
Developed comprehensive model validation techniques to ensure generalization performance