Good and bad classification of Betel leaf (Piper Betel)
Description
## Data Description for Good and Bad Classification of Betel Leaf (Piper Betel) ### Introduction The dataset comprises 1000 samples of Marsh Barbel Leaves, specifically of the Betel leaf (Piper Betel) variety. This dataset is evenly divided into two categories: 500 good samples and 500 bad samples. The goal of this project is to develop a classification model that can accurately distinguish between good and bad Betel leaves based on the provided features. ### Dataset Composition 1. *Total Samples*: 1000 - *Good Samples*: 500 - *Bad Samples*: 500 ### Features The dataset includes various features that are crucial for determining the quality of Betel leaves. These features can be broadly categorized into visual, physical, and chemical characteristics. Below is a detailed description of the features included: 1. *Visual Characteristics* - *Color*: The RGB values representing the color of the leaf. - *Texture*: Numerical representation of the leaf's texture, obtained through image processing techniques. - *Shape*: Features related to the shape, such as area, perimeter, and aspect ratio. - *Defects*: Binary or categorical indicators of visual defects like spots, discoloration, or holes. 2. *Physical Characteristics* - *Size*: Measured dimensions of the leaf, such as length and width. - *Weight*: The mass of the leaf, recorded in grams. 3. *Chemical Characteristics* - *Moisture Content*: Percentage of water content in the leaf. - *Chlorophyll Level*: Measured chlorophyll content, indicating the leaf's health. - *Nutrient Levels*: Concentration of essential nutrients like nitrogen, phosphorus, and potassium. ### Data Collection The data has been collected from various sources, including agricultural research centers, farms, and markets. Each sample has been meticulously labeled by experts to ensure accuracy. Advanced imaging techniques and sensors were employed to capture the visual and physical characteristics, while laboratory analyses were conducted to determine the chemical properties. ### Data Format The dataset is provided in a tabular format, with each row representing a single leaf sample. The columns correspond to the different features and the label (good or bad). Below is a sample structure of the dataset: | Sample ID | Color_R | Color_G | Color_B | Texture | Shape_Area | Shape_Perimeter | Shape_Aspect_Ratio | Defects | Length | Width | Weight | Moisture_Content | Chlorophyll_Level | Nitrogen_Level | Phosphorus_Level | Potassium_Level | Label | |-----------|---------|---------|---------|---------|-------------|------- ### Usage This dataset is intended for use in developing machine learning models for the classification of Betel leaves. Researchers and developers can employ various algorithms, such as decision trees, support vector machines, or neural networks, to train and validate models. The balanced nature of the dataset ensures that models can be trained without bias towards any particular class