基于Faster R-CNN的野外环境中蝗虫快速识别

蝗虫是常见的害虫之一，对农作物和生态系统具有很大的危害，采用常规的方法对蝗虫进行监测存在一定局限性，为了有效应用海量野外影像数据实现对蝗虫实时监测，本文建立了一种基于深度学习网络的蝗虫自动识别模型。利用手机模拟摄像头获取的内蒙古锡林浩特附近草原的280张蝗虫的RGB图像，采用深度学习算法中的Faster R-CNN（Faster Region-based Convolutional Neural Network）网络结构建立了蝗虫识别模型。经验证该模型的精确度为0.756，可以较准确地将蝗虫从野外复杂环境中识别出来，与以往同类研究相比，在识别结果和实用性方面均有较大的进步。该模型是建立蝗虫实时监测系统的基础，可以为蝗虫的防治提供辅助信息，同时该网络结构还可以应用于其他害虫的识别，具有较强的推广性，拓宽了深度学习算法的应用领域。

Rapid identification of locust on fields based on Faster R-CNN

Locust is the stubborn pest insects which can damage crops and ecosystems.Traditional methods for monitoring locust have many disadvantages.To effectively apply massive field image data to achieve real-time monitoring of locusts, a locust automatic identification model based on a deep learning network was established in this study.Firstly, 280 locust RGB images photographed by the mobile phone camera in a complex field environment from the grasslands of Xilinhot, Inner Mongolia were obtained.Then the Faster R-CNN network structure which performs better in recognition was used.The accuracy of this model is 0.756.The model performs well on locust detection and outperforms the previous methods in the identify results and practicality.The model can accurately identify the locust from the complex environment on fields, which provide auxiliary information for the control of locusts.It is a basis for establishing a real-time monitoring system for monitoring locusts.At the same time, the network structure can also be applied to other pests and diseases' monitor.In addition, the model broadens the application field of deep learning algorithms.