Soyachans
Description
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!