josemenber/image-based-crop-anomaly-detection
A Convolutional Neural Network approach for image-based anomaly detection in smart agriculture
This project helps agriculturalists, agronomists, and farm managers quickly identify crop anomalies or diseases in their fields. By inputting aerial or field images of crops, it processes them to classify and pinpoint areas showing signs of distress or unusual growth. The output is a clear classification of whether an anomaly is present, enabling timely intervention to protect crop health and yield.
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Use this if you need an automated, accurate system to detect crop anomalies from images, especially in smart agriculture settings where real-time monitoring is beneficial.
Not ideal if you require anomaly detection at a pixel-level granularity, as this system is designed for image-level classification.
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17
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2
Language
Python
License
Apache-2.0
Category
Last pushed
Nov 21, 2021
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