| In this paper, the remotely-controlled helicopter (Herakles II) was selected as the monitoring platform and digital camera (EOS 30D) was used to collect canopy data of rice. And the scanner was adopted as the digital image sensor to collect information of leaf, the method and model of diagnosing the status of rice was established based on image processing.(1) In this study, the remotely-controlled helicopter (Herakles II) was selected as the monitoring platform and digital camera (EOS 30D) was used to collect data.These two are both produced from Japan. The digital image got from the remotely-controlled helicopter was quite well, the boundary between different fields was clear and the variety of light was obvious. It would provide enough datas to extract information for later studying from these images.(2) According to the analysis of relations between value of rice leaf SPAD, leaf percentage nitrogen contents and plant percentage nitrogen contents, the effective color parameter had been abstraced as B,b,b/(r+g),b/r,b/g. It was suggested that the third leaf from the top was the most ideal indicator and it was choosen as the object researched. Finally the method and model of diagnosing the status of rice was established and the accuracy were as follows: NO: 74.9%; N1: 52%; N2: 84.7%; N3: 75%.(3) It is suggested that there was a good relation between the data of G, value of rice leaf SPAD, leaf percentage nitrogen contents and plant percentage nitrogen contents. The correlative relationship between leaf spectral characteristics and hyperspectral remote sensing was studied. It was indicated that the diagnose of nitrogen at different leaf positon based on the color characteristic values is feasible. The DGCI was introduced to certify that the data of G and DGCI could express the different status of rice on the jointing stage. Finally the method and model of diagnosing the status of rice was established and the accuracy were as follows: NO: 91.6%; N1: 70.83%; N2: 86.7%; N3: 95%.
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