MOYA FISH
Description
### Data Description: Good and Bad Classification of Moya Fish (Mola Mola) *Data Collection Overview:* The dataset consists of over 500 images of Moya fish (Mola Mola), captured for the purpose of classifying them into two categories: "Good" and "Bad." The images were taken using a Redmi 9 Power mobile camera, ensuring a consistent image quality. All images were captured against a black background in daylight conditions, which provides uniform lighting and minimizes distractions, enhancing the focus on the fish's features. *Camera and Environmental Conditions:* - *Camera Used:* Redmi 9 Power mobile camera. - *Resolution:* The images are expected to be of high resolution, typically around 48 MP, given the camera specifications. - *Background:* The black background ensures high contrast between the fish and its surroundings, making it easier to distinguish and analyze the features of the fish. - *Lighting:* Daylight conditions were used to illuminate the fish naturally, avoiding harsh shadows or artificial lighting effects that could alter the fish's appearance. *Dataset Composition:* - *Total Images:* The dataset includes more than 500 images, with an approximately equal distribution between "Good" and "Bad" classifications. - *Good Moya Fish:* These are images where the fish are healthy, intact, and exhibit ideal physical characteristics such as a smooth body surface, symmetrical fins, and clear eyes. - *Bad Moya Fish:* These images capture fish that may have deformities, damage, disease, or any physical imperfections that deviate from the ideal standards. This could include discoloration, missing parts, abnormal growths, or signs of infection. *Image Attributes:* - *Format:* The images are likely in a standard format such as JPEG or PNG, commonly supported by mobile cameras. - *Color Space:* The images are captured in color (RGB), which is crucial for distinguishing subtle differences in fish coloration that might indicate good or bad quality. - *Orientation:* Images are expected to be consistently oriented to present the fish in a similar perspective across the dataset, facilitating easier comparison and classification. *Use Case and Applications:* This dataset is valuable for developing machine learning models that can automatically classify Moya fish into "Good" or "Bad" categories. Such models can be used in aquaculture, fisheries management, and quality control processes to ensure only the highest quality fish are selected for further processing or sale. *Challenges:* - *Variability in Fish Appearance:* Despite the controlled conditions, natural variability in the fish’s appearance may pose challenges in classification, necessitating a robust model that can handle such variations. This dataset serves as a comprehensive resource for developing, testing, and validating classification algorithms aimed at assessing the quality of Moya fish, contributing to advancements in automated fish quality assessment systems.