MANGO PICKLE
Description
Project Title: Good and Bad Classification of Mango Pickle (Mangifera indica) Using Redmi 10 Prime Mobile Camera Description: The project, "Good and Bad Classification of Mango Pickle (Mangifera indica)," aims to develop a machine learning model capable of distinguishing between good and bad samples of mango pickle. A dataset of more than 500 images, evenly divided between good and bad mango pickle samples, has been created. The images were captured using a Redmi 10 Prime mobile camera, providing high-resolution visual data. A white background was used to highlight the pickle, and images were taken in natural daylight to ensure optimal lighting conditions for accurate analysis. Dataset Composition: Good Samples (High-Quality Mango Pickle): The dataset includes more than 250 images of high-quality mango pickle. These samples exhibit the desired characteristics of a good pickle, such as vibrant color, proper texture, correct oil and spice distribution, and absence of spoilage indicators like mold or off-putting discoloration. These images represent the positive class in the model, providing examples of what constitutes a well-made, high-quality mango pickle. Bad Samples (Low-Quality Mango Pickle): Another 250 images represent low-quality or spoiled mango pickle. These samples may show signs of spoilage such as mold, fermentation bubbles, discoloration, or improper texture (either too dry or too watery). These images make up the negative class, helping the model learn to identify pickle samples that do not meet quality standards. Data Collection Setup: The images were captured using the Redmi 10 Prime mobile camera, chosen for its ability to capture high-quality, detailed images. The use of a white background helped to create a clear, distraction-free environment, ensuring that the model focuses on the key features of the pickle. Natural daylight was used for illumination to bring out the true colors and textures, providing consistency and reducing shadows or reflections that could distort the image quality. Image Characteristics: The images in the dataset vary in terms of the appearance of the mango pickle, influenced by different ingredients, preparation methods, and levels of spoilage or aging. This variability allows the model to generalize well and perform effectively in different scenarios, whether identifying a batch of well-preserved pickles or detecting signs of spoilage. Data Annotation: Each image has been manually labeled as either "good" or "bad" based on expert observation of the pickle’s quality. These labels serve as the ground truth, allowing the model to learn the differences between high-quality and low-quality pickle samples. Data Preprocessing: The dataset is processed through several steps to ensure that the machine learning model receives clean, standardized inputs: Resizing: All images are resized to a uniform dimension to ensure consistency across the dataset.