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Sensors 2015, 15(3), 6250-6269; doi:10.3390/s150306250

The Design and Implementation of the Leaf Area Index Sensor

1
College of Global Change and Earth System Science, Beijing Normal University, No.19, XinJieKou Wai Street, HaiDian District, Beijing 100875, China
2
Joint Center for Global Change Studies, Beijing 100875, China
3
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, No. 20 Nouth, DaTun Road, ChaoYang District, Beijing 100101, China
4
Chinese Research Academy of Environment Sciences, No.8, DaYangFang, AnWai, ChaoYang District, Beijing 100012, China
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 14 November 2014 / Revised: 28 February 2015 / Accepted: 4 March 2015 / Published: 13 March 2015
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
View Full-Text   |   Download PDF [2662 KB, uploaded 23 March 2015]   |  

Abstract

The quick and accurate acquisition of crop growth parameters on a large scale is important for agricultural management and food security. The combination of photographic and wireless sensor network (WSN) techniques can be used to collect agricultural information, such as leaf area index (LAI), over long distances and in real time. Such acquisition not only provides farmers with photographs of crops and suggestions for farmland management, but also the collected quantitative parameters, such as LAI, can be used to support large scale research in ecology, hydrology, remote sensing, etc. The present research developed a Leaf Area Index Sensor (LAIS) to continuously monitor the growth of crops in several sampling points, and applied 3G/WIFI communication technology to remotely collect (and remotely setup and upgrade) crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The research also constructed a database of images and other information relating to crop management. The leaf length and width method (LAILLW) can accurately measure LAI through direct field harvest. The LAIS has been tested in several exemplary applications, and validation with LAI from LAILLW. The LAI acquired by LAIS had been proved reliable. View Full-Text
Keywords: leaf area index; wireless sensor network; remote upgrade; validation leaf area index; wireless sensor network; remote upgrade; validation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, X.; Liu, Q.; Yang, R.; Zhang, H.; Zhang, J.; Cai, E. The Design and Implementation of the Leaf Area Index Sensor. Sensors 2015, 15, 6250-6269.

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