Dry Bean Dataset

The Dry Bean Dataset consists of 13,611 high-resolution images of grains from seven distinct dry bean varieties. This dataset is designed for research in seed classification, machine learning, and computer vision applications. It includes comprehensive data on 16 distinct features—12 dimensions and 4 shape forms—extracted from each grain, making it ideal for training algorithms focused on agricultural automation, quality control, and image recognition.

Dataset Features:

• Total Images: 13,611 high-quality images
• Varieties: 7 different registered dry bean types
• Key Features: 12 dimensions (e.g., length, width, area) and 4 shape forms (e.g., roundness, convexity)
• Data Extraction: Images were processed using computer vision techniques, which include segmentation and feature extraction, ensuring precise data for model training.
• Applications: Suitable for machine learning models focused on seed classification, agricultural product sorting, and quality assessment based on visual attributes.

Relevance for Agricultural Research
This dataset provides valuable insights into bean seed classification based on visual characteristics, essential for research in the agricultural sector. It supports projects related to crop analysis, automated sorting, and quality grading.

How It Can Be Used:

• Machine Learning Models: Train algorithms for classification and segmentation tasks.
• Computer Vision: Use the dataset for feature extraction, object detection, and image recognition models.
• Agricultural Automation: Implement in systems for automating crop sorting and grading based on seed properties.