This document presents an implementation of computer vision and machine learning techniques for precision trait analysis and yield prediction in soybean. The code demonstrates the use of deep learning models for soybean phenotyping, alongside sample data and pre-trained weights.

1. Code Implementation
2. Sample Images
3. Model Weights

Step-by-step Process:

1. Input soybean images
2. Preprocess images using OpenCV and numpy.
3. Pass images through a trained deep learning model for feature extraction and trait analysis.
4. Extract other phenotypic parameters using OpenCV.
5. Generate yield predictions.

Sample Diagrams:

To run the project, install the following dependencies:

pip install torch torchvision numpy opencv-python matplotlib

The complete codebase, including training scripts, and additional documentation, will be made open-source.