| 基于物联网技术的仓储香蕉成熟度智能监测与预警系统设计 |
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| 引用本文： | 张竞超,云利军,王一博,王坤.基于物联网技术的仓储香蕉成熟度智能监测与预警系统设计[J].安徽农业大学学报,2019,46(4):745-750. |
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| 作者姓名： | 张竞超 云利军 王一博 王坤 |
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| 作者单位： | 云南师范大学信息学院,昆明,650500;云南师范大学信息学院,昆明650500;云南光电信息技术重点实验室,昆明650500 |
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| 基金项目： | 云南省应用基础研究计划重点项目（2018FA033）和云南师范大学研究生科研创新基金（yjs2018129）共同资助。 |
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| 摘 要： | 为了达到对香蕉成熟度的无损检测，减少由于过度存放及处理不及时造成的损失，基于物联网技术设计并实现了一套仓储香蕉成熟度智能监测与预警系统。该系统包括智能仓储数据采集终端、手持无线中继器和后台数据服务器3个部分，智能仓储数据采集终端将采集到的仓储香蕉的温度、湿度、二氧化碳和氨气等环境信息通过手持无线中继器传输到后台数据服务器，然后基于神经网络识别算法判断给出香蕉的成熟度状态和预警信息。测试结果表明，该系统可以有效地对仓储香蕉的成熟度状态进行监测和预警，提示用户进行及时的处理，从而大大降低香蕉的损失率，具有非常高的实用价值。
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| 关 键 词： | 无损检测 香蕉 成熟度 BP神经网络 STM32 GPRS |
Warehousing tobacco leaf mildew intelligent monitoring and early warning system |
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| ZHANG Jingchao,YUN LIjun,WANG Yibo and WANG Kun.Warehousing tobacco leaf mildew intelligent monitoring and early warning system[J].Journal of Anhui Agricultural University,2019,46(4):745-750. |
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| Authors: | ZHANG Jingchao YUN LIjun WANG Yibo and WANG Kun |
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| Institution: | School of Information, Yunnan Normal University, Kunming 650500,School of Information, Yunnan Normal University, Kunming 650500; Yunnan Key Laboratory of Opto-electronic Information Technology, Kunming 650500,School of Information, Yunnan Normal University, Kunming 650500 and School of Information, Yunnan Normal University, Kunming 650500 |
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| Abstract: | In order to achieve nondestructive testing of banana maturity and reduce losses caused by excessive storage and untimely processin, an intelligent monitoring and early warning system for storage banana maturity was designed and implemented based on Internet of Things technology.The system includes three parts:intelligent warehousing data collection terminal, handheld wireless repeater and background data server.The intelligent warehousing data collection terminal transmits the environmental information such as temperature, humidity, carbon dioxide and ammonia stored in the collected banana to the background data server through the handheld wireless repeater, and then determines the maturity status and warning information of the banana based on the neural network.The test results show that the system can effectively monitor and warn the maturity status of the stored bananas, prompting the users to deal with them in time, thus greatly reducing the loss rate of bananas, and has very high practical value. |
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| Keywords: | nondestructive testing banana maturity BP neural network STM32 GPRS |
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