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基于自主车辆与 WSN 的温室玫瑰环境监测及数据分析。

Environment Monitoring of Rose Crops Greenhouse Based on Autonomous Vehicles with a WSN and Data Analysis.

机构信息

Department of Computer Science and Automatics, University of Salamanca, 37008 Salamanca, Spain.

Department of Applied Sciences, Universidad Técnica del Norte, Ibarra 100150, Ecuador.

出版信息

Sensors (Basel). 2020 Oct 19;20(20):5905. doi: 10.3390/s20205905.

DOI:10.3390/s20205905
PMID:33086727
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7589953/
Abstract

This work presents a monitoring system for the environmental conditions of rose flower-cultivation in greenhouses. Its main objective is to improve the quality of the crops while regulating the production time. To this end, a system consisting of autonomous quadruped vehicles connected with a wireless sensor network (WSN) is developed, which supports the decision-making on type of action to be carried out in a greenhouse to maintain the appropriate environmental conditions for rose cultivation. A data analysis process was carried out, aimed at designing an in-situ intelligent system able to make proper decisions regarding the cultivation process. This process involves stages for balancing data, prototype selection, and supervised classification. The proposed system produces a significant reduction of data in the training set obtained by the WSN while reaching a high classification performance in real conditions-amounting to 90 % and 97.5%, respectively. As a remarkable outcome, it is also provided an approach to ensure correct planning and selection of routes for the autonomous vehicle through the global positioning system.

摘要

本工作提出了一种温室玫瑰种植环境条件监测系统。其主要目的是在调节生产时间的同时提高作物的质量。为此，开发了一个由与无线传感器网络（WSN）连接的自主四足车辆组成的系统，该系统支持在温室中进行决策，以维持玫瑰种植的适当环境条件。进行了数据分析过程，旨在设计一个能够对种植过程做出适当决策的现场智能系统。该过程涉及数据平衡、原型选择和监督分类阶段。所提出的系统在达到 90%和 97.5%的高分类性能的同时，显著减少了 WSN 获得的训练集的数据量。作为一个显著的成果，还提供了一种通过全球定位系统确保自主车辆正确规划和选择路线的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/86bda5c50792/sensors-20-05905-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/0015b7b5c474/sensors-20-05905-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/12870f2be776/sensors-20-05905-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/4173d4de4181/sensors-20-05905-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/4736dc7aabf4/sensors-20-05905-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/165c2af1021b/sensors-20-05905-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/3a70000d3b52/sensors-20-05905-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/58b65e4be92d/sensors-20-05905-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/f114b8694f11/sensors-20-05905-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/60ab19b1a401/sensors-20-05905-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/86bda5c50792/sensors-20-05905-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/0015b7b5c474/sensors-20-05905-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/12870f2be776/sensors-20-05905-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/4173d4de4181/sensors-20-05905-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/4736dc7aabf4/sensors-20-05905-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/165c2af1021b/sensors-20-05905-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/3a70000d3b52/sensors-20-05905-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/58b65e4be92d/sensors-20-05905-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/f114b8694f11/sensors-20-05905-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/60ab19b1a401/sensors-20-05905-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/7589953/86bda5c50792/sensors-20-05905-g010.jpg

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