Zucchini Disease Diagnosis Dataset
Main product
Data Quantity
Optional product
Total Price
USD 0 VAT included
- Main productPremium
- Data QuantityBasic
- Optional productNot selected
- DownloadNo data
- All products are priced including VAT.
- Premium datasets are custom-made and take approximately two weeks from the date of application to improve quality.
- Common datasets can be checked on My Page after purchase.
Basic Price
USD 25,000 VAT included
Zucchini Disease Diagnosis Data
- Labeling type: Disease
- Data Format: Image
Related Tags:
Use cases of research datasets
About Dataset
1) Data Introduction
• This dataset is an image dataset about zucchini downy mildew and zucchini powdery mildew, which are diseases that occur in zucchini crops.2) Data Utilization
(1) Field Crop Disease Diagnosis: zucchini Data has characteristics that: • The dataset contains images showing different stages and types of diseases affecting zucchini. • Provides the information needed to understand the symptoms, progression, and impact of various diseases on zucchini crops. (2) Field Crop Disease Diagnosis: zucchini Data can be used to: • Develop Disease Recognition and Classification Models: AI models can be trained to recognize and classify different types of diseases affecting zucchini plants. For example, analyzing images from agricultural monitoring systems can identify specific symptoms, enabling early diagnosis and treatment. • Plant Health Monitoring: Analyzing disease patterns in zucchini plants can help develop systems to monitor plant health, enabling early detection and management of diseases. • Disease Control and Prevention: Analyzing the conditions under which diseases in zucchini plants occur and spread can help identify effective management methods. • Environmental Impact Studies: Provides data to study how environmental factors affect disease occurrence and severity in zucchini plants, helping develop preventative measures.Meta Data
Very good
Outstanding
Data Samples 4
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.
Premium Report Information
Premium dataset sample
