papaya curry
Description
Papaya Curry Classification Dataset Description 1. Overview The dataset consists of over 500 images of papaya curry (Carica papaya), classified into two categories: good and bad. The primary goal is to develop an automated classification model that distinguishes between high-quality and defective papaya curry based on visual characteristics. 2. Data Collection Camera Used: Redmi 9 Power Lighting Conditions: Natural daylight Background: White Total Samples: 500+ Classification Categories: Good Papaya Curry: Fresh, well-cooked, properly textured, and visually appealing curry with appropriate color and consistency. Bad Papaya Curry: Overcooked, undercooked, spoiled, discolored, watery, or containing unwanted particles. 3. Image Properties Resolution: Varies based on the Redmi 9 Power’s camera settings (typically 48 MP for high-quality images). File Format: Likely JPEG or PNG Angle & Focus: Close-up shots focusing on texture, color, and consistency. 4. Features for Classification Color Variations: Fresh papaya curry appears vibrant orange-yellow, whereas bad samples may show dark spots, excessive oil separation, or an unnatural hue. Texture & Consistency: Good curry has a smooth, uniform consistency, while bad curry may appear lumpy, watery, or excessively dry. Presence of Spoilage: Indicators like mold, excessive oil separation, or unnatural glossiness may signify a bad sample. Foreign Particles: Contamination by burnt elements or external particles can impact classification. 5. Potential Applications Quality control in food processing. AI-based food safety assessment. Automated inspection systems for restaurants and food suppliers. This dataset provides a structured approach for training machine learning models to distinguish between good and bad papaya curry, ensuring improved food quality assessment.