Shelf life determination of Hypophthalmicthys molitrix ( Silver Carp )
Description
This project, titled "Good and Bad Classification of Silver Carp(Hypophthalmichthys molitrix) is designed to develop an image classification system that distinguishes between healthy (good) and unhealthy (bad) Silver Carp fish (Hypophthalmichthys molitrix). The dataset consists of approximately 2000 images, evenly distributed between good and bad samples. All images were captured using a Realme 5i mobile camera, providing high-resolution visual data suitable for machine learning applications. The fish were photographed against a black background in daylight conditions to ensure consistency, clarity, and accurate feature capture. Dataset Composition Good Samples (Healthy) The dataset includes approximately 1000 images of healthy Silver Carp fish. These images show fish with: Bright, shiny, and intact scales Clear, transparent eyes Proper body shape without deformities Natural coloration and smooth texture These samples represent the positive class and help train the model to recognize healthy fish conditions. Bad Samples (Unhealthy) The dataset also contains approximately 1000 images of unhealthy Silver Carp fish. These fish may exhibit: Dull or discolored scales Cloudy or damaged eyes Physical deformities Visible injuries or infections Poor overall physical condition These images represent the negative class, enabling the model to identify unhealthy fish accurately. Data Collection Setup All images were captured using a Realme 5i smartphone camera, known for its reliable image quality and resolution. A black background was used intentionally to: Enhance contrast between the fish and the background Reduce noise and unwanted visual distractions Highlight important visual features such as scales, eyes, and body structure Images were taken under natural daylight conditions, ensuring consistent illumination and accurate representation of color and texture. Image Characteristics The dataset includes variations in: Fish size Body orientation Color intensity Health condition This diversity improves the robustness of the machine learning model and ensures better performance in real-world scenarios. Data Annotation Each image is carefully labeled as either: "Good" (Healthy) "Bad" (Unhealthy) These labels serve as the ground truth, allowing the machine learning model to learn the differences between healthy and unhealthy fish accurately.