The Design and Implementation of the Leaf Area Index Sensor

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.

Keywords

• leaf area index
• wireless sensor network
• remote upgrade
• validation