RULI FISH
Description
The dataset consists of over 500 images of Ruli fish (Labeo rohita), categorized into two classes: "good" and "bad." These images were captured using a Samsung F62 mobile camera against a black background under daylight conditions. Data Description: 1. Classes: Good: Represents healthy Ruli fish exhibiting desirable characteristics such as vibrant color, uniform size, proper body shape, and absence of visible signs of damage, disease, or decay. Bad: Includes Ruli fish with undesirable traits such as discoloration, deformities, injuries, signs of disease, decay, or physical damage. 2. Image Collection: The dataset contains over 500 images, representing a balanced mix of good and bad Ruli fish. Images were captured under consistent daylight conditions to ensure uniform illumination and natural appearance of the fish. A black background was used to improve the contrast and clarity of the fish, isolating it from the environment. 3. Data Source: Images were taken using a Samsung F62 mobile camera, providing consistent resolution and image quality throughout the dataset. Daylight was utilized to enhance the visibility of fish details while minimizing the need for artificial lighting. 4. Annotation: Each image is labeled according to its classification (good or bad), facilitating supervised learning tasks. Annotations may include additional details such as bounding boxes or segmentation masks to highlight the fish area for object detection or segmentation tasks. 5. Data Preprocessing: Preprocessing steps such as resizing, normalization, cropping, or background enhancement may have been applied to prepare the dataset for machine learning tasks. Metadata such as camera settings, image resolution, and lighting conditions may be included to support further analysis or model optimization. 6. Data Distribution: The dataset ensures a balanced distribution between good and bad categories to prevent bias and improve model performance. Images were randomized during collection and organization to reduce any potential patterns or overfitting in the model. 7. Potential Applications: The dataset can be utilized in various machine learning tasks, including image classification, object detection, and segmentation. Applications include automated fish quality assessment, fish sorting systems for markets, disease detection, and supply chain management in aquaculture. 8. Limitations: Variability may exist due to slight differences in camera angles, lighting conditions, or fish orientation during image capture. The dataset focuses exclusively on Labeo rohita and may not generalize well to other fish species or environmental conditions.