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Improving Deep Learning for Identifying Crop Diseases in Real-World Conditions
Jenn Hoskins
21st July, 2024
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Image Source: Natural Science News, 2024
Key Findings
- Researchers at Northwest A&F University found that CNN models are less accurate in identifying crop diseases in real-world field conditions compared to controlled lab settings
- The study showed that disease identification accuracy dropped from 98.22% in labs to 71.55% in fields
- Different CNN models had similar accuracy, suggesting that improving data quality and volume is more important than developing new model architectures
AgricultureBiotechPlant Science
References
Main Study
1) Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop-specific approaches.
Published 19th July, 2024
https://doi.org/10.1002/ps.8317
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