基于TensorRT的植物叶片病害实时检测分类模型优化

Optimization of Real-time Detection and Classification Model for Plant Leaf Diseases Based on TensorRT

为了提高边缘计算设备对植物叶片病害检测的识别速率,本研究采用卷积神经网络搭建了植物叶片目标识别模型和植物叶片病害分类模型,并且使用OpenCV将两个模型整合成植物叶片病害检测系统.通过SSD(single shot multibox detector)算法对植物叶片的目标区域进行定位并裁剪,再利用植物叶片病害分类模型对裁剪的植物叶片区域进行病害分类.同时,通过TensorRT加速推理对分类模型进行优化处理,以及在同一台主机设备和Jetson Nano计算平台上,对优化前后的模型进行了对比实验.实验表明,在同一主机设备上优化后的植物分类模型识别速率提升22倍.同时,优化后的分类模型使植物叶片病害检测系统识别速率提升7倍.而将优化后的系统部署在Jetson Nano计算平台上,对比优化前的植物叶片病害检测速率提升10倍,实现了实时的植物叶片病害检测.

In order to improve the recognition rate of plant leaf disease detection by edge computing devices,this study uses a convolutional neural network to build a plant leaf target recognition model and a plant leaf disease classification model and adopts OpenCV to integrate the two models into a plant leaf disease detection system.The target areas of plant leaves are positioned and clipped by the single shot multibox detector(SSD)algorithm,and then the plant leaf disease classification model is used to classify the clipped plant leaf areas according to diseases.At the same time,the classification model is optimized by TensorRT accelerated inference.In addition,on the same host device and Jetson Nano computing platform,a comparative experiment is carried out on the model before and after optimization.The experiment shows that the recognition rate of the optimized plant classification model on the same host device increases by 22 times.At the same time,the optimized classification model improves the recognition rate of the plant leaf disease detection system by seven times.Furthermore,the optimized system is deployed on the Jetson Nano computing platform,and the detection rate of plant leaf diseases is increased by 10 times compared with that before optimization,which thus realizes real-time plant leaf disease detection.