论文标题
使用基于ROS的自主引导的无人机自动化小麦疾病检测
Automated Wheat Disease Detection using a ROS-based Autonomous Guided UAV
论文作者
论文摘要
随着世界人口的增加，必须修改粮食资源，以提高生产力，抵抗力和可靠性。小麦是世界上最重要的食品资源之一，主要是因为各种基于小麦的产品。小麦作物受到三种主要疾病的威胁，这些疾病会导致大量的农作物产量损害。可以通过在正确的时间使用农药来消除这些疾病。虽然手动喷洒农药的任务是繁重且昂贵的，但农业机器人技术可以通过提高速度和减少化学物质量来帮助农民。在这项工作中，已经在无人机上实现了一个智能自主系统，以自动监视小麦场的任务。首先，一种基于图像的深度学习方法用于检测和分类感染了疾病的小麦植物。为了找到最佳方法，已经研究了不同的方法。由于缺乏公共小麦滴定数据集，因此已经创建了自定义数据集。其次，使用机器人操作系统和凉亭环境中的模拟提出了有效的映射和导航系统。 2D同时定位和映射算法用于借助基于边境的探索方法自动映射工作空间。
With the increase in world population, food resources have to be modified to be more productive, resistive, and reliable. Wheat is one of the most important food resources in the world, mainly because of the variety of wheat-based products. Wheat crops are threatened by three main types of diseases which cause large amounts of annual damage in crop yield. These diseases can be eliminated by using pesticides at the right time. While the task of manually spraying pesticides is burdensome and expensive, agricultural robotics can aid farmers by increasing the speed and decreasing the amount of chemicals. In this work, a smart autonomous system has been implemented on an unmanned aerial vehicle to automate the task of monitoring wheat fields. First, an image-based deep learning approach is used to detect and classify disease-infected wheat plants. To find the most optimal method, different approaches have been studied. Because of the lack of a public wheat-disease dataset, a custom dataset has been created and labeled. Second, an efficient mapping and navigation system is presented using a simulation in the robot operating system and Gazebo environments. A 2D simultaneous localization and mapping algorithm is used for mapping the workspace autonomously with the help of a frontier-based exploration method.
