Description:

This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.

Key Features:

• A_id: Unique identifier for each fruit
• Size: Size of the fruit
• Weight: Weight of the fruit
• Sweetness: Degree of sweetness of the fruit
• Crunchiness: Texture indicating the crunchiness of the fruit
• Juiciness: Level of juiciness of the fruit
• Ripeness: Stage of ripeness of the fruit
• Acidity: Acidity level of the fruit
• Quality: Overall quality of the fruit

Potential Use Cases:

• Fruit Classification: Develop a classification model to categorize fruits based on their features.
• Quality Prediction: Build a model to predict the quality rating of fruits using various attributes.

The dataset was generously provided by an American agriculture company. The data has been scaled and cleaned for ease of use.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂 Thank you

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

1. 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.

1. 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

1. Bad Apples:

Presence of:

Mold

Bruising

Cuts or cracks

Discoloration or rot

Some may be partially decomposed

Often irregular in shape or visibly damaged

1. Sources:

Agricultural research datasets

Custom image captures from farms or marketplaces

Open-source image repositories with suitable licensing (e.g., Creative Commons)

1. Data Split:

Training set: 70%

Validation set: 15%

Test set: 15%

Stratified to ensure balanced class representation across splits

1. 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

1. 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.

This dataset has labelled images of bad and good of Papaya (Carica Papaya). Each class contains 500 images.

1) Data Introduction • The Lemon Quality Dataset is a binary classification computer vision image dataset designed to automatically assess the quality of lemons. It includes images of both good-quality and defective lemons, as well as some images that contain only the concrete background without any lemons, making it also suitable for background detection and segmentation tasks.

2) Data Utilization (1) Characteristics of the Lemon Quality Dataset: • The lemon images were captured under natural lighting, from various angles and sizes, and contain visual features that clearly distinguish between good and bad quality. • Since the lemons were all photographed on a uniform concrete background, the dataset contains minimal noise, making it well-suited for object detection and fine-grained image segmentation models.

(2) Applications of the Lemon Quality Dataset: • Fruit quality classification model development: The dataset can be used to train deep learning models that automatically classify lemons as fresh or damaged based on their visual characteristics. • Research in agricultural automation and smart farming systems: It can serve as training data for AI-powered applications such as fruit-sorting robots, quality-based packaging systems, and other agri-food processing automation technologies.