Summary of Edge-ai For Agriculture: Lightweight Vision Models For Disease Detection in Resource-limited Settings, by Harsh Joshi
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings
by Harsh Joshi
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed computer vision pipeline is designed to assist farmers in detecting orange diseases with minimal resources. The system integrates advanced object detection, classification, and segmentation models optimized for edge devices, ensuring functionality in resource-limited environments. State-of-the-art models such as Vision Transformer and YOLOv8-S were evaluated for accuracy, computational efficiency, and generalization capabilities. Notable findings include the Vision Transformer achieving 96% accuracy in orange species classification and YOLOv8-S demonstrating exceptional object detection performance with minimal overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Farmers need help detecting diseases on their oranges! Researchers created a special computer system that uses machine learning to do this job. The system is lightweight, so it can run on simple devices like smartphones or tablets, making it perfect for farmers who don’t have access to fancy computers. They tested different models and found that some worked really well – one even got 96% of the time! This means that with a little help from technology, farmers can better care for their oranges and grow more food. |
Keywords
» Artificial intelligence » Classification » Generalization » Machine learning » Object detection » Vision transformer
