An energy-efficient AkidaNet for morphologically similar weeds and crops recognition at the Edge

Wild radish weeds have always been a persistent problem in agriculture due to their quick and undesirable spread. Therefore, the accurate identification and effective control of wild radish in canola crops at early growth stage play an indispensable role in reducing herbicide rates and enhancing agricultural productivity. In this paper, an energy efficient and lightweight AkidaNet model is developed to accurately identify broad-leaf weeds and crops with similar morphology at four different growth stages. Experiments performed on a published bccr-segset dataset show that our proposed method achieves competitive performance, a classification accuracy of 99.73%, compared to several well-known CNNs architectures. Next, we quantized and converted the model into a Spiking Neural Network for implementation on a spike-based neuromorphic hardware device. The converted model is not only superior in low-latency and low-power consumption but also retains a similar accuracy to the original model. We also employ Grad-CAM to validate whether our model focuses on important features in plant images to identify wild radish weeds in crops.