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 ExternalCite 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
Source: External (2026)
Indexed by IoTDataset.com on Jan 06, 2026
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