Good and bad classification of apple
Description
Sure! Here's a concise data description within 3000 characters for a project titled "Good and Bad Classification of Apples": --- Project Title: Good and Bad Classification of Apples Data Description: The dataset used in this project is centered around the classification of apples into two categories: good (fit for sale/consumption) and bad (damaged, rotten, or otherwise unfit). The dataset comprises images of apples collected under controlled as well as natural conditions, and optionally, corresponding annotations or metadata. 1. Data Types: Image Data: The primary data consists of RGB images of individual apples. Labels: Each image is labeled as either “good” or “bad”. Optional Metadata (if available): Time of capture Lighting condition Apple variety Temperature or humidity readings at the time of image capture 2. Image Characteristics: Resolution: Images range from 224x224 to 512x512 pixels. Background: Mixture of plain (controlled lab settings) and complex (orchard or market environments). Lighting: Includes both natural and artificial lighting. Angle and Orientation: Varies to simulate real-world usage scenarios in sorting systems. 3. Good Apples: Visually appealing No visible bruises, rot, or mold Uniform shape and color Examples might show apples with minimal surface blemishes or minor imperfections 4. Bad Apples: Presence of: Mold Bruising Cuts or cracks Discoloration or rot Some may be partially decomposed Often irregular in shape or visibly damaged 5. Sources: Agricultural research datasets Custom image captures from farms or marketplaces Open-source image repositories with suitable licensing (e.g., Creative Commons) 6. Data Split: Training set: 70% Validation set: 15% Test set: 15% Stratified to ensure balanced class representation across splits 7. Preprocessing: Image resizing and normalization Data augmentation (flipping, rotation, brightness/contrast adjustments) to increase model robustness Optional noise filtering and background removal to improve focus on the apple surface 8. Use Cases: Automated sorting systems in agriculture Quality control for fruit suppliers and supermarkets Educational tools for machine learning in agricultural contexts --- Let me know if you’d like to include technical details about models or preprocessing pipelines as well.