Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing

Abstract

The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

Application of Vision Technology and Artificial Intelligence in Smart Farming

“…Guo et al [3] proposed an improved YOLOv5 object detection model, integrating the coordinate attention module and the deformable convolution module for accurately detecting mature Zanthoxylum on a mobile picking platform, addressing the issues of irregular shape and occlusion caused by branches and leaves. Li et al [4] proposed a lightweight wheat growth stage detection model and a dynamic migration algorithm, which utilizes edge computing to migrate the detection model to the wireless network edge server for processing, improving efficiency significantly compared to the local implementation. By accurately monitoring the growth trends of animals and plants through deep learning and computer vision technologies, they can effectively improve production efficiency.…”