BLACKBEANS CURRY

Lighting Conditions: Natural daylight.

Background

White background for clear contrast. 3. Image Characteristics Resolution: High-resolution images ensuring detailed visibility of bean surface texture. Orientation: Beans placed flat, allowing clear identification of surface features.

Focus: Sharp images highlighting key attributes such as color, texture, and defects. Color Representation: The dataset captures variations in black shades, distinguishing fresh (good) beans from aged or damaged (bad) ones. 4. Classification Criteria Good Black Beans: Deep black or dark brown color with a smooth, glossy surface.

Uniform size and shape without cracks. Hard texture, indicating freshness. Bad Black Beans: Discoloration, including faded, dull, or reddish shades.

Wrinkles, cracks, or damaged outer shells. Mold, insect damage, or shriveled appearance. Irregular shape or size variations. 5.

Application and Use Cases Machine learning and deep learning models for automated quality classification. Quality control in agriculture, food processing, and storage. AI-based grading systems for commercial black bean sorting.

Research on bean spoilage and preservation techniques. 6. Potential Preprocessing Steps Image Resizing: Standardizing dimensions for model training. Augmentation: Adjusting brightness, contrast, and rotation for dataset robustness.

Background

Processing: Ensuring focus on beans by segmenting out the white background. Feature Extraction: Analyzing color, shape, and texture for enhanced classification accuracy. This dataset provides a valuable foundation for AI-driven agricultural quality assessment, enabling automated classification of good and bad black beans with precision.