Abstract: In the current environment of intelligent agriculture, the identification and monitoring of crop growth process has been a challenging task. Based on this, a remote greenhouse visual monitoring system based on Internet of things is proposed, the system monitors environmental parameters such as temperature, humidity and light intensity of crops in greenhouse by LoRa wireless communication technology, which can monitor the growth of crops in time, and to achieve the greenhouse crops automatic ventilation, automatic lighting and other functions. Realtime monitoring of environmental information and control of environmental parameters in the greenhouse from the Qt host on the PC, and the crops are monitored by OV9726 camera. The information obtained is transmitted to S3C6410 centralized control module for processing, a combination of clonal selection algorithms and Naive Bayes classifier was used to identify the leaves. This system uses LoRa module to implement environmental monitoring, and the Linux operating system is transplanted to the centralized control module to prepare for the construction of the hardware and software platform of the visual system, the combined algorithm can make the recognition rate of crop leaves reach 95.3% and the recognition time reach 8.4 ms, which improves the precision of leaf recognition obviously, the validity of the equipment and algorithm used in this system is fully verified by experiments.

Key words: LoRa； blade identification, clonal selection algorithm, Naive Bayes, Qt, Linux operating system

摘要： 在当前智慧农业的大环境下，农作物生长过程的识别与监控问题一直是一项具有挑战性的任务，基于此提出一种基于物联网的远程温室视觉监控系统，系统通过LoRa无线通信技术监测温室内的温湿度、光照强度等环境参数，能够及时监测到农作物的生长状况，并实现自动通风、自动补光等功能。在PC端的Qt上位机实时监测温室内的环境信息并控制环境参数，通过OV9726摄像头对农作物进行监测，所获得的生长状态信息传输到S3C6410集中控制模块进行处理，结合克隆选择算法和朴素贝叶斯分类器对叶片进行识别处理。本系统采用LoRa模块进行自组网来实现环境监测，将Linux操作系统移植到集中控制模块，为视觉系统软硬件平台的搭建做准备工作，所使用的组合算法能够使得农作物叶片识别率达到95.3%，识别时间达到8.4 ms，对于叶片识别精度等方面有着明显的提升，经过实验充分验证本系统所使用的设备与算法的有效性。

关键词: LoRa, 叶片识别, 克隆选择算法, 朴素贝叶斯, Qt, Linux操作系统