(1) Field Crop Disease Diagnosis: Peppers Data has characteristics that: • The dataset contains images showing the different stages and types of diseases affecting pepper plants. • Provides the information needed to understand the symptoms, progression, and impact of various diseases on pepper crops. (2) Field Crop Disease Diagnosis: Peppers 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 pepper 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 pepper 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 pepper plants occur and spread can help identify effective management practices. • Environmental Impact Studies: Provides data to study how environmental factors affect disease occurrence and severity in pepper plants, helping develop preventive measures.

Meta Data

Domain	Health	Zoodata formats	Image
Zoodata volume	1000 items	Registration date	2024.08.01
Zoodata type	Synthetic data	Existence of labeling	Exist
Labeling type	Disease	Labeling formats	json

Very good

Outstanding

100

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.

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