Diagnoses of Rice Nitrogen Status Based on Characteristics of Scanning Leaf
ZHU Jin-xia1, DENG Jin-song1，2*, SHI Yuan-yuan1, CHEN Zhu-lu1, HAN Ning1, WANG Ke1, 2
1. Institute of Agricultural Remote Sensing and Information Technology Application，Zhejiang University，Hangzhou 310029，China 2. Key Laboratory of Zhejiang Province Agricultural Remote Sensing and Information System, Hangzhou 310029, China
Abstract：In the present research, the scanner was adopted as the digital image sensor, and a new method to diagnose the status of rice based on image processing technology was established. The main results are as follows: (1) According to the analysis of relations between leaf percentage nitrogen contents and color parameter, the sensitive color parameters were abstracted as B, b, b/(r+g), b/r and b/g. The leaf position (vertical spatial variation) effects on leaf chlorophyll contents were investigated, and the third fully expanded leaf was selected as the diagnosis leaf. (2) Field ground data such as ASD were collected simultaneously. Then study on the relationships between scanned leaf color characteristics and hyperspectral was carried out. The results indicated that the diagnosis of nitrogen status based on the scanned color characteristic is able to partly reflect the hyperspectral properties. (3) The leaf color and shape features were intergrated and the model of diagnosing the status of rice was established with calculated at YIQ color system. The distinct accuracy of nitrogen status was as follows: N0: 74.9％；N1: 52％；N2: 84.7％；N3: 75％. The preliminary study showed that the methodology has been proved successful in this study and provides the potential to monitor nitrogen status in a cost-effective and accurate way based on the scanned digital image. Although, some confusion exists, with rapidly increasing resolution of digital platform and development of digital image technology, it will be more convenient for larger farms that can afford to use mechanized systems for site-specific nutrient management. Moreover, deeper theory research and practice experiment should be needed in the future.
Key words：Color character;Principal component analysis;Synthesis character
祝锦霞1,邓劲松1，2*,石媛媛1,陈祝炉1,韩 凝1,王 珂1，2. 基于水稻扫描叶片图像特征的氮素营养诊断研究[J]. 光谱学与光谱分析, 2009, 29(08): 2171-2175.
ZHU Jin-xia1, DENG Jin-song1，2*, SHI Yuan-yuan1, CHEN Zhu-lu1, HAN Ning1, WANG Ke1, 2. Diagnoses of Rice Nitrogen Status Based on Characteristics of Scanning Leaf. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(08): 2171-2175.
[1] Scaife M A, Stevens K L. Commum. Soil Sci. Plant Anal., 1983, 14(9): 761. [2] Iida T N, Noguchi C K, Terao Ishii H. Journal of the Japanese Society of Agricultural Machinery, 2000, 62(2): 87. [3] Scharf P C, Lory J A. Agron, J, 2002, 94: 394. [4] Douglas E, Karcherand, Michael D R, et al. Crop Sci., 2003, 43: 943. [5] CHENG Jia-juan, JI Shou-wen, et al(陈佳娟, 纪寿文, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2000, 16(3): 115. [6] SUN Ming, LING Yun(孙 明, 凌 云). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2002, 33(5): 75. [7] WANG Yuan, HUANG Jing-feng, WANG Fu-ming, et al(王 渊, 黄敬峰, 王福明, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(2): 273. [8] LI He-sheng, et al(李合生，等). The Theory and Technology of Plant Physiology(植物生理生化实验原理和技术). Beijing: Higher Education Press(北京: 高等教育出版社), 2001. 123. [9] Bulanon D M, Kataoka T, Ota Y. Biosystems Engineering, 2002, 83(4): 405. [10] Tian L F, Slaughter D C. Computer & Election. in Agric., 1998, 21(3): 153. [11] YU Feng, ZHANG Bin, ZHANG Jun-feng(于 峰, 张 彬, 张峻峰). Journal of China Agricultural University(中国农业大学学报), 2007, 12(4): 67. [12] Charles-Edwards D, Stutzel H, Ferraris R, et al. Annals of Botany, 1987, 60: 421. [13] Anten N, Schieving F, Werger M. Oecologia, 1995, 101: 504. [14] Vouillot M, Devienne F. Annals of Botany, 1999, 83: 569. [15] Milroy S, Bange M, Sadras V. Annals of Botany, 2001, 87: 325. [16] ZHENG Yong-mei, ZHANG Jun, CHEN Xing-dan(郑咏梅，张 军，陈星旦). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2004，24(6):675.
