TinyML Approach for Beans Leaf Diseases Classification Using Sony Spresense
In this project, we the approach used by author in paper “Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs “to endow drones with a larger autonomy and intelligence thanks to the integration of a joint flight and mission control embedded system. Thanks to the adoption of a powerful lightweight processing architecture, a suitably designed ML inference engine and Edge Impulse, the system implemented an edge computing solution that enabled the achievement of sophisticated mission goals.
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