Continual observation on crop leaf area index using wireless sensors network

Abstract

Crop structural parameter, i.e. leaf area index(LAI), is the main factor that can effect the solar energy re-assignment in the canopy. An automatic measuring system which is designed on the basis of wireless sensors network(WSN) is present in this paper. The system is comprised of two types of node. One is the measurement nodes which measured solar irradiance and were deployed beneath and above the canopy respectively, and another is a sink node which was used to collect data from the other measurement nodes. The measurement nodes also have ability to repeater data from one node to another and finally transfer signal to the sink node. Then the collected data of sink node are transferred to the data center through GPRS network. Using the field data collected by WSN, canopy structural parameters can be calculated using the direct transmittance which is the ratio of sun radiation captured by the measurement node beneath and above the canopy on different sun altitude angles. The proposed WSN measurement systems which is consisted of about 45 measurement node was deployed in the Heihe watershed to continually observe the crop canopy structural parameters from 25 June to 24 August 2012. To validate the performance of the WSN measured crop structural parameters, the LAI values were also measured by LAI2000. The field preliminary validation results show that the designed system can capture the varies of solar direct canopy transmittance on different time in a day, which is the basis to calculate the target canopy structural parameters. The validation results reveal that the measured LAI values derived from our propose measurement system have acceptable correlation coefficient(R2 from 0.27 to 0.96 and averaged value 0.42) with those derived from LAI2000. So it is a promising way in the agriculture application to utilize the proposed system and thus will be an efficient way to measure the crop structural parameters in the large spatial region and on the long time series.