基于卷积神经网络下昆虫种类图像识别应用研究

Research on application of insect species image recognition based on convolutional neural network

昆虫种类图像识别是农业智能化识别虫害的重要方式,精准高效识别昆虫种类是进行针对性防治虫害的前提.利用昆虫数据集ArTaxOr及Insect_det,基于卷积神经网络下图像分类如MobileNet,ResNet及目标检测(FasterRCNN)、Yolo技术,运用迁移学习进行模型训练,并对比分析训练结果,获取最优昆虫种类图像识别模型.将构建的最优模型采用EasyEdge平台进行部署,从而实现了模型到端的全流程开发模式,为后续昆虫种类图像识别场景化应用研究提供依据参考.

Image recognition of insect species is an important way of agricultural intelligent identification of pests,and accurate and efficient identification of insect species is the premise of targeted pest control.In this paper,we use the insect datasets ArTaxOr and Insect_det,based on image classification under convolutional neural network,such as MobileNet and ResNet,and target detection under convolutional neural network,such as FasterRCNN and YOLO technology,uses transfer learning to train the model,compares and analyzes the training results to obtain the optimal image recognition model of insect species.The constructed optimal model is deployed on EasyEdge platform,thus realizing the whole process development mode from model to end,which provides a basis and reference for the follow-up research on the scene application of insect species image recognition.