Soyachans

Data Description: Good and Bad Soybean (Glycine max) Classification

1. Overview

The dataset consists of over 500 images of soybean (Glycine max) samples, categorized into "good" and "bad" classes. The objective is to develop a classification model to distinguish between high-quality and defective soybeans based on visual features.

2. Data Collection

Total Samples: 500+ images

Categories: Good soybeans, Bad soybeans

Camera Used: Xiaomi 11i mobile camera

Lighting Conditions: Natural daylight

Background: White

3. Image Characteristics

Resolution: High-resolution images ensuring clarity for classification

Color Balance: White background helps in minimizing noise and improving segmentation

Consistency: Captured under uniform lighting conditions for reliable analysis

4. Good Soybean Characteristics

Uniform shape and size

Smooth and clean surface

Consistent golden-yellow color

No visible cracks, shriveled texture, or discoloration

5. Bad Soybean Characteristics

Irregular or damaged shape

Presence of cracks, holes, or shriveled texture

Dark spots, fungal growth, or discoloration

Deformed or broken seeds

6. Potential Applications

Automated quality control in soybean processing

Agricultural research and seed selection

Development of AI-driven classification models

This dataset serves as a robust foundation for training machine learning models to classify good and bad soybeans accurately. Let me know if you need further refinements or additional details!