Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application

Computer vision is a key technique to make agricultural machinery smart. Deep neural network has achieved great success in computer vision. How to use it at a small size, low cost, low power consumption device with high accuracy and speed on strawberry harvesting machinery has drawn much research attention. Since the infield situation has reduced number of objects and that they are easier to be distinguished from the background compared to other computer vision datasets, the huge neural network structure can be simplified in order to speed up the detection inference without penalizing the detection accuracy. In this research, a new deep neural network called RTSD-Net is proposed based on stat-of-art light-weighted YOLOv4-tiny with reduced layers and modified structure for real-time strawberry detection under infield condition. The original CSPNet was replaced by 2 types of CSPNet designed with reduced parameters and a simplified structure and 4 new network structures are designed by combining these 2 types. The performances of the 4 networks were evaluated. It was observed that the number of parameters of these 4 networks and the detection speed of the model is negatively correlated. Simplified structure and reduced parameters can contribute to faster operational speed. The last one was selected and named as RTSD-Net. Comparing with YOLOv4 tiny, the accuracy of RTSD-Net is only reduced by 0.62% but the speed is increased by 25FP5, which is 25.93% higher than that of YOLOv4-tiny. Embedded system Jetson Nano was selected as the evaluation platform to evaluate the RTSD-Net's performance for edge computing. The original Open Neural Network Exchange (ONNX) model was loaded on Jetson Nano and the speed of RTSD-Net was 13.1FPS, which is 19.0% higher than that of YOLOv4-tiny. After speeded up by TensorRT method, the transformed model reached 25.20fps, which is twice as fast as the ONNX model, and 15% faster than the YOLOv4-tiny model. After speeding up, the efficiency of RTSD-Net is enough for computer vision based strawberry detection and harvesting. In summary, the proposed RTSD-Net has good potential in smart strawberry harvesting machinery and the idea of redesigning neural structure and reducing parameters to speed up the detection rate of deep neural network is expected to have good application in edge computing.

Automated summary

In this study, a new deep neural network called RTSD-Net is proposed for real-time strawberry detection on harvesting machinery. By simplifying the structure and reducing parameters, the detection speed can be increased without sacrificing accuracy. After evaluation and acceleration, RTSD-Net exhibits good performance in edge computing.