BEANS

The dataset consists of over 500 images of beans (Phaseolus vulgaris L), categorized into two classes: "good" and "bad." These images were captured using a Realme Narzo 20 Pro mobile camera against a black background under daylight conditions. **Data Description:** 1. **Classes:** - Good: Represents healthy beans showcasing desirable characteristics such as uniform color, shape, and absence of defects, blemishes, or signs of damage or disease. - Bad: Encompasses beans exhibiting signs of damage, disease, or other undesirable traits such as discoloration, mold, deformities, or pest infestation. 2. **Image Collection:** - The dataset comprises over 500 images, with a substantial number representing both good and bad instances of beans. - Images were captured under consistent daylight conditions to ensure uniformity and minimize environmental variability. - A black background was utilized to enhance bean visibility and isolate the subject. 3. **Data Source:** - Images were captured using a Realme Narzo 20 Pro mobile camera, ensuring consistent image quality and resolution across the dataset. - Daylight conditions were chosen to provide natural lighting, reducing artificial effects on bean appearance. 4. **Annotation:** - Each image is labeled according to its class (good or bad), facilitating supervised learning tasks. - Annotations may include bounding boxes or masks outlining the bean area to aid in localization tasks. 5. **Data Preprocessing:** - Preprocessing techniques such as resizing, normalization, and background removal may have been applied to the images to enhance model performance and reduce computational complexity. - Metadata such as image resolution, format, and capture settings may accompany the dataset for reference. 6. **Data Distribution:** - The dataset maintains a balanced distribution between good and bad beans, ensuring equal representation of both classes. - Randomization techniques may have been employed during data collection and organization to mitigate biases in model training. 7. **Potential Applications:** - The dataset can be utilized for various machine learning tasks, including classification, object detection, and image segmentation, particularly in agricultural applications. - Applications may include automated sorting systems for bean quality control, disease detection, and yield optimization. 8. **Limitations:** - Despite efforts to ensure data consistency and quality, variations in lighting conditions, camera angles, and bean orientation may introduce some degree of variability. - The dataset primarily focuses on beans of the Phaseolus vulgaris L variety and may not generalize well to other bean varieties or environmental conditions.本数据集包含超过500张菜豆（Phaseolus vulgaris L）图像，分为“优质”与“劣质”两个类别。所有图像均使用Realme Narzo 20 Pro智能手机摄像头在日光环境下以黑色背景拍摄。 **数据说明：** 1. **类别：** - 优质：指表现出优良性状的健康菜豆，具备色泽均匀、形状规整的特点，且无缺陷、瑕疵、损伤或病害迹象。 - 劣质：指存在损伤、病害或其他不良性状的菜豆，包括变色、霉变、畸形或虫害侵染等情况。 2. **图像采集：** - 本数据集包含超过500张图像，优质与劣质菜豆样本的数量均较为可观。 - 所有图像均在一致的日光环境下拍摄，以保证数据统一性，尽可能降低环境变量带来的影响。 - 采用黑色背景以提升菜豆的辨识度，并实现主体与背景的分离。 3. **数据来源：** - 图像均通过Realme Narzo 20 Pro智能手机摄像头拍摄，确保数据集内所有图像的质量与分辨率保持一致。 - 选择日光环境以提供自然光照，减少人工光照对菜豆外观表现的干扰。 4. **标注：** - 每张图像均按其所属类别（优质或劣质）进行标注，可用于监督学习任务。 - 标注内容可包含勾勒菜豆区域的边界框或掩码，以支持目标定位类任务。 5. **数据预处理：** - 为提升模型性能并降低计算复杂度，可能已对图像应用调整尺寸、归一化及背景移除等预处理技术。 - 数据集可附带图像分辨率、格式及拍摄设置等元数据以供参考。 6. **数据分布：** - 本数据集在优质与劣质菜豆类别间保持均衡分布，确保两类样本的占比一致。 - 在数据采集与整理过程中可能采用了随机化技术，以缓解模型训练过程中出现的偏差。 7. **潜在应用场景：** - 本数据集可应用于多种机器学习任务，包括分类、目标检测与图像分割，尤其适用于农业相关场景。 - 具体应用可包括菜豆品质检测自动化分拣系统、病害检测及产量优化等方向。 8. **局限性：** - 尽管已尽力保证数据的一致性与质量，但光照条件、拍摄角度及菜豆摆放角度的差异仍可能引入一定程度的变量。 - 本数据集主要聚焦于菜豆（Phaseolus vulgaris L）品种，可能无法很好地泛化至其他菜豆品种或环境条件。