Lemon Quality Dataset
A valuable resource for researchers and developers in the field of fruit quality classification, agricultural automation, and smart farming systems, providing a comprehensive and detailed collection of images of lemons from various angles and sizes, along with visual features that distinguish between good and bad quality.
- Labeling Type: quality
- Data Format: Image
- Data Type: Synthetic Data
Main Product
Data Quantity (Samples)
Total Price
$ 4,700
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About Dataset
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.Meta Data
Normal
Outstanding
Data Samples 5
Utility
| Downstream Classification (▲) | KID (▼) | One Class Classification (▼) | |
|---|---|---|---|
| Total | 0 | 0 | 0 |
| Suitability | OK | OK | OK |
The higher the value, the better (▲)
Model Performance
Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models. Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.
The closer to zero or the lower the value, the better (▼)
Quality
KID (Kernel Inception Distance) is a metric used to evaluate the similarity between generated images and real images. It compares the differences between the two sample distributions using Kernel Mean Embedding, without assuming a normal distribution. Interpretation: A lower KID score suggests that generated images are more similar to real images, with a score close to 0 being ideal. Specifically, a score below 0.01 indicates very high similarity.
Privacy
| LPIPS (▲) | SSIM (▼) | |
|---|---|---|
| Total | 0 | 0 |
| Suitability | OK | OK |
The higher the value, the better (▲)
Perceptual Similarity
Learned Perceptual Image Patch Similarity (LPIPS) is a metric used to measure the visual similarity between two images by utilizing neural networks to extract key features and calculate the distance between them. High LPIPS value: Indicates high similarity between images, raising the risk of information leakage. Low LPIPS value: Suggests that synthetic images are perceptually different from real images, indicating a lower risk of sensitive information leakage.
The closer to zero or the lower the value, the better (▼)
Structural Similarity
The Structural Similarity Index Measure (SSIM) is a metric used to assess the similarity between two images. It is primarily used to compare the quality of a restored or compressed image with the original image. SSIM measures visual similarity by considering brightness, contrast, and structure.
High SSIM value (0.9 or above): Indicates that the synthetic image is very similar to the real image, which may increase the risk of information leakage.
Low SSIM value (0.6 or below): Indicates low similarity and reduced risk of leakage.
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