Automatic Crop Disease Recognition Using Image and Weather Data
Combining leaf image features and meteorological data for improved crop disease recognition
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Detail description
An automatic crop disease recognition method combines leaf image features—color, shape, and texture—with meteorological data including seasonality, location, time, and environmental conditions. Evaluated on three cucumber disease datasets—downy mildew, blight, and anthracnose—the method achieved a 91.08% recognition accuracy, demonstrating improved performance through integrated data analysis.
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Contribution detail info
- Project
Smart-AKIS
European Agricultural Knowledge and Innovation Systems (AKIS) towards innovation-driven research in Smart Farming Technology
- Location
- Europe
- Authors
- smart-AKIS
- Purpose
- Dissemination
- File type
- document
- Created on
- Jan 03, 2018
- Origin language
- English
- Official project website
- Smart-AKIS
- License
- CC BY
- Keywords
- Themes