Research on agricultural IoT pest and disease image recognition algorithm based on STM32 - Botao XU; Xiang CHEN (物联网学报)
In modern agriculture IoT systems, edge computing is an indispensable component.In this context, it is feasible to deploy lightweight pest and disease image recognition tasks on edge devices.However, due to the constraints of device computation and storage capabilities, this task faces significant challenges.To address these challenges, an economically practical method was proposed for pest and disease image recognition on STM32 edge devices.Specifically, the MobileNetv2 structure was improved to better suit the characteristics of STM32, quantization-aware training technique was used to compresses the network, model portability was enhanced.Meanwhile, the X-CUBE-AI was used to arrange the model and evaluate the performance.Experimental results demonstrate that the proposed model not only ensures image classification accuracy but also reduces the Flash and RAM resource consumption on STM32 compared to other lightweight networks.