This project focuses on seed type classification using computer vision and deep learning techniques. The goal is to identify the type of seed from an image.
The dataset is manually collected and consists of images of real seeds belonging to specific vegetable categories.
The dataset includes the following seed classes:
- Bitter Melon
- Bottle Gourd
- Carrot
- Cauliflower
- Chili
- Coriander Leaves
- Cucumber
- Hyacinth Bean
- Malabar Spinach
- Onion
- Radish
- Spinach
- Tomato
- Water Spinach
- Create a manually collected dataset of different vegetable seeds.
- Train deep learning models to classify seed types from images.
- Compare model performance in terms of accuracy and efficiency.
- ResNet50V2
- MobileNetV3-Large
- EfficientNetB0
- Data Source: Manually collected
- Image Type: Real seed images
- Task Type: Multi-class image classification