Areca-nut

The Areca Nut Quality Sorting Dataset is a specialized collection of high-resolution images designed for the development and training of AI models to automate the quality sorting of areca nuts. Sorting areca nuts into categories such as 1st Chali, 2nd Chali, 3rd Chali, 1st Gotu, 2nd Gotu, and Kole is traditionally a labor-intensive process, often taking over 3 hours to manually sort a single quintal (100 kg). This dataset aims to address this bottleneck and enable automated quality sorting, thereby reducing labor costs and enhancing productivity.

Dataset Features

• High-Resolution Images: Captured in real-world farming and warehouse environments to ensure data realism and applicability.
• Categorized Labels: Each areca nut image is labeled with its corresponding quality category:
  • 1st Chali
  • 2nd Chali
  • 3rd Chali
  • 1st Gotu
  • 2nd Gotu
  • Kole
• Diverse Samples: Includes areca nuts of varying shapes, sizes, and surface textures to account for sorting challenges across regions.
• Machine Learning Ready: Pre-processed and formatted for use in AI algorithms for image recognition, classification, and sorting tasks.

Applications

• Automated Areca Nut Sorting: Develop AI-powered systems to replace manual labor for quality sorting.
• Quality Assurance: Ensure consistent grading standards for areca nuts.
• Economic Benefits: Enhance pricing by providing sorted areca nuts directly to wholesalers and processors.

Why This Dataset is Essential

In regions like Karnataka, India, where areca nut farming is a significant industry, farmers face challenges in quality sorting due to labor shortages and the high costs of manual sorting. Automating this process can save time, reduce costs, and increase the profitability of areca nut farming.