BLACKBEANS CURRY

Dataset Description: Black Bean (Cicer arietinum) Good and Bad Classification 1. Overview This dataset consists of images of black beans (Cicer arietinum) captured for classification into "Good" and "Bad" categories. The dataset is designed for automated quality assessment based on visual properties. The images were collected under standardized conditions to ensure consistency and accuracy in classification. 2. Data Collection Total Samples: 500+ black beans (Good and Bad). Camera Used: iPhone 15 mobile camera. Lighting Conditions: Natural daylight. Background: White background for clear contrast. 3. Image Characteristics Resolution: High-resolution images ensuring detailed visibility of bean surface texture. Orientation: Beans placed flat, allowing clear identification of surface features. Focus: Sharp images highlighting key attributes such as color, texture, and defects. Color Representation: The dataset captures variations in black shades, distinguishing fresh (good) beans from aged or damaged (bad) ones. 4. Classification Criteria Good Black Beans: Deep black or dark brown color with a smooth, glossy surface. Uniform size and shape without cracks. Hard texture, indicating freshness. Bad Black Beans: Discoloration, including faded, dull, or reddish shades. Wrinkles, cracks, or damaged outer shells. Mold, insect damage, or shriveled appearance. Irregular shape or size variations. 5. Application and Use Cases Machine learning and deep learning models for automated quality classification. Quality control in agriculture, food processing, and storage. AI-based grading systems for commercial black bean sorting. Research on bean spoilage and preservation techniques. 6. Potential Preprocessing Steps Image Resizing: Standardizing dimensions for model training. Augmentation: Adjusting brightness, contrast, and rotation for dataset robustness. Background Processing: Ensuring focus on beans by segmenting out the white background. Feature Extraction: Analyzing color, shape, and texture for enhanced classification accuracy. This dataset provides a valuable foundation for AI-driven agricultural quality assessment, enabling automated classification of good and bad black beans with precision.数据集描述：黑豆（Cicer arietinum）优劣分类数据集 1. 概述 本数据集包含用于“优质”与“劣质”分类任务的黑豆（Cicer arietinum）图像，旨在基于视觉特征实现自动化品质评估。所有图像均在标准化条件下采集，以确保分类结果的一致性与准确性。 2. 数据采集 总样本量：500余粒优质与劣质黑豆 拍摄设备：iPhone 15智能手机摄像头 光照条件：自然日光 背景：纯白背景以实现清晰对比度 3. 图像特性 分辨率：高分辨率图像，可清晰呈现豆粒表面纹理细节 摆放姿态：黑豆平置，便于清晰识别表面特征 对焦：图像清晰锐利，可突出颜色、纹理与缺陷等关键属性 色彩表现：数据集覆盖黑色调的多种变体，可区分新鲜（优质）黑豆与陈化或受损（劣质）黑豆 4. 分类标准 优质黑豆： 颜色呈深黑或深棕，表面光滑且富有光泽 尺寸与形状均匀，无裂纹 质地坚硬，体现新鲜度 劣质黑豆： 存在褪色、暗沉或泛红等变色现象 表面褶皱、开裂或外壳受损 带有霉变、虫蛀或干瘪外观 形状不规则或尺寸不均 5. 应用场景 可用于构建实现自动化品质分类的机器学习与深度学习模型 可应用于农业、食品加工与仓储环节的品质管控 可搭建基于人工智能的分级系统，用于商业黑豆分拣 可用于黑豆变质与保鲜技术的相关研究 6. 可选预处理步骤 图像尺寸标准化：统一图像维度以适配模型训练 数据增强：调整亮度、对比度与旋转角度，提升数据集鲁棒性 背景分割处理：通过分割去除纯白背景，聚焦目标豆粒 特征提取：分析颜色、形状与纹理特征，提升分类精度 本数据集为人工智能驱动的农业品质评估提供了宝贵基础，可精准实现优质与劣质黑豆的自动化分类。