About Project

Waste classification and plant disease identification are critical for sustainability and agriculture. Manual sorting is time-consuming, error-prone, and inconsistent. Automating these tasks requires accurate object detection and segmentation, even in noisy, real-world environments with class imbalance and complex backgrounds.

Problem Statement

Waste classification and plant disease identification are critical for sustainability and agriculture. Manual sorting is time-consuming, error-prone, and inconsistent. Automating these tasks requires accurate object detection and segmentation, even in noisy, real-world environments with class imbalance and complex backgrounds.

Objective

To develop and deploy a robust deep learning system using YOLOv8 for:

• Trash Object Detection: Detect and classify cardboard, glass, metal, and plastic waste
• Plant Disease Instance Segmentation: Identify diseased regions in plant leaves with pixel-level precision
• Live Prediction: Real-time object detection using webcam input
• Deployment: Integrated with Django and FastAPI for full-stack deployment

Proposed Solution

• Trained YOLOv8n on a trash dataset with 1,783 training images
• Classes: cardboard (0), glass (1), metal (2)
• Used pretrained weights and AdamW optimizer
• Evaluation Metrics:
• mAP@0.5: 98.9%
• mAP@0.5–0.95: 88.4%
• Precision: 97.3%
• Recall: 96.3%

Technologies Used

• Ultralytics YOLOv8
• PyTorch
• OpenCV

• ONNX Export for model deployment
• FastAPI + Django for live inference with webcam input

• Google Colab (Tesla T4 GPU)

• Python
• YAML
• JSON

Challenges Faced

• CPU-based training on local system led to slow and interrupted runs
• Initial plant disease segmentation model showed low generalization on unseen data
• Webcam-based live deployment faced performance drops (<40% accuracy)
• Heavy model load time; skipped deeper tuning due to time constraints

Methodology

• Verified image-label pairs for consistency
• Converted annotations to YOLO format
• Ensured uniform image size:
  • 640×640 for trash detection
  • 256×256 for plant segmentation

• Used modular Python functions for dataset loading and training configuration
• Monitored key training metrics:
  • Precision
  • Recall
  • mAP
  • Box/Classification Loss
• Trained both detection and segmentation variants of YOLOv8

• Plotted confidence score histograms
• Analyzed confusion matrices and false positive rates
• Tracked mAP evolution across epochs and different augmentation strategies

• Integrated YOLOv8 model with FastAPI for backend inference
• Connected to a Django frontend that captures webcam images and displays results
• Exported the final trained model in ONNX format for cross-platform deployment

Result / Outcome

Task	mAP@50	mAP@50–95	Precision	Recall
Trash Detection	0.989	0.884	0.973	0.963
Plant Disease Segmentation	0.504	0.176	0.593	0.368

• Mosaic augmentation significantly improved detection accuracy
• Trash detection model achieved real-time performance (2.2 ms/image)
• Segmentation model underperformed due to limited training scope