Abstract
"Automated crop classification using morphological features captured by optical sensors for smart processing plants (Cammeo & Osmancik varieties)."
Description
Using IoT Optical Sensors, this dataset captures 3,810 instances of rice grains across two major varieties (Cammeo and Osmancik).
Morphological Features:
- Area & Perimeter: Physical size metrics in pixels.
- Major/Minor Axis Length: Geometric shape data.
- Eccentricity: Measures how round the ellipse is.
- Convex Area: Pixel count of the smallest convex shell.
Use Case:
Building AI-powered sorting machines for smart agriculture logistics and quality control.
Source: UCI Machine Learning Repository (ID: 545) - Rice (Cammeo and Osmancik).
📊 View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on External.
Preview on External
Cite This Dataset
External (2026). Smart Agriculture: Rice Variety Sensor Image Data. [Dataset]. External. https://archive.ics.uci.edu/static/public/545/rice+cammeo+and+osmancik.zip
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Original source: External (2026). Visit official page for more details.
Indexed by IoTDataset.com on Jan 06, 2026
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