Good and Bad classification of tok palong leavs(Sour spinach)
Description
### Data Overview: 1. *Sample Size*: - Total Samples: 1,000 - Good Samples: 500 - Bad Samples: 500 2. *Features*: - *Visual Characteristics*: Images or attributes describing the visual appearance of the leaves, such as color, size, shape, and presence of spots or discoloration. - *Texture*: Surface texture details, including smoothness or roughness, which may be critical in determining the quality. - *Chemical Composition*: Levels of key chemical components (if available) that might indicate spoilage or nutrient content. - *Environmental Factors*: Data on the conditions in which the leaves were grown, such as soil type, water quality, and exposure to pests or diseases. - *Harvesting Data*: Information on when and how the leaves were harvested, which could affect their quality. ### Classification Criteria: - *Good Leaves*: Leaves that are healthy, vibrant in color, free from significant blemishes, and have the expected chemical composition and texture indicative of freshness. - *Bad Leaves*: Leaves that show signs of spoilage, such as discoloration, wilting, presence of mold, or other physical defects, and any deviations in chemical composition suggesting degradation. ### Data Collection Method: - *Manual Inspection*: Expert evaluation of each leaf sample to determine its classification. - *Automated Imaging*: Use of high-resolution cameras and image processing algorithms to capture and analyze visual features. - *Chemical Analysis*: Laboratory tests to measure the chemical composition of the leaves. - *Environmental and Harvest Records*: Logs maintained by growers and harvesters to provide context to the growing and harvesting conditions. ### Purpose: The goal of this project is to develop a robust machine learning model capable of accurately classifying Tok Palong leaves into good or bad categories based on the provided features. This classification will assist farmers, suppliers, and retailers in ensuring the quality of their produce, minimizing waste, and maximizing consumer satisfaction. ### Applications: - *Quality Control*: Automated inspection systems at harvest or before distribution. - *Supply Chain Optimization*: Better sorting and handling processes based on leaf quality. - *Consumer Assurance*: Providing end-users with high-quality produce, enhancing trust and satisfaction. ### Data Processing and Model Development: 1. *Preprocessing*: Cleaning and normalizing the data to ensure consistency and accuracy. 2. *Feature Engineering*: Extracting and selecting the most relevant features for classification. 3. *Model Training*: Using various machine learning algorithms to train and validate models on the dataset. 4. *Evaluation*: Assessing