Učinkovita detekcija objektov za spremljanje pridelka v natančnem kmetijstvu

This thesis presents an automated approach to designing energy-efficient neural network architecture for wheat head detection in precision agriculture. By leveraging neural architecture search (NAS) on the YOLOv8n model, we developed optimized architecture tailored for deployment on edge devices such as the NVIDIA Jetson Nano and Raspberry Pi with OAK-D. Our best model reduced computational complexity by 37.0% GFLOPs and the number of parameters by 61.3% with negligible drop in detection accuracy (mAP@50). Furthermore, it achieved 28.1% improvement in FPS and 18.5% improvement in energy efficiency on NVIDIA Jetson Nano with ONNX runtime. Using TensorRT FP16 runtime it achieved 18.78% improvement in FPS and 39.34% improvement in energy efficiency. To asses the generalizability of the NAS approach we perform another search on plant seelding dataset yielding to 15.2% faster model (ONNX) with only negligible drop of 1.45% in accuracy.