摘要：水稻生长监测是现代农业研究的热点之一。在作物生产管理中，生长信息的实时、快速、准确获取极为重要，直接影响作物产量和品质的形成。传统的作物生长信息的获取主要依靠田间植株取样和室内化学分析实验，费时费力且破坏植株，难以满足快速、无损获取作物生长信息的需求。光谱监测技术因其快速、实时、无损等优点，正逐渐成为作物生产精确管理实施过程中信息获取的重要手段。CGMD402作物生长监测诊断仪，由南京农业大学国家信息农业工程技术中心研制，该仪器体积小、重量轻、成本低，而且集成度高、实用性较强，适用于大田试验。本研究设置不同品种、不同氮素水平的水稻田间试验，于移栽后定期使用CGMD402获取冠层归一化植被指数（Normalized difference vegetation index, NDVI）和比值植被指数（Ratio vegetation index, RVI），同步破坏性取样获取叶干重（Leaf draw weight, LDW）、叶面积指数（Leaf area index, LAI）及叶片氮含量（Leaf nitrogen content, LNC）等农学参数，建立基于NDVI和RVI的水稻生长指标光谱监测模型。结果表明，CGMD402作物生长监测诊断仪与水稻生长信息具有良好的相关性，测量的NDVI与LDW、LAI、LNC之间的拟合方程，决定系数R2分别为0.6140、0.6625、0.6328， RMSE分别为0.15、0.20、0.13； RVI与LDW、LAI、LNC之间的拟合方程， R2分别为0.6173、0.6565、0.6554， RMSE分别为0.18、0.18、0.12。同时，为研究仪器的精确性，于水稻分蘖期、分蘖末期、拔节初期、拔节期、孕穗期、抽穗期使用ASD HandHeld2便携式地物光谱仪同步测量光谱植被指数，并与ASD HandHeld2便携式地物光谱仪所测得的NDVI、RVI进行回归分析，R2分别为0.7190、0.7419。表明CGMD 402作物生长监测诊断仪对水稻生长具有良好的监测能力，能够对水稻农学指标进行定量估测，且具有较高的稳定性和精确性，为水稻生产中氮肥精确管理提供技术支持。29011
毕业论文关键词：CGMD402；光谱监测；农学参数；光谱植被指数；模型
SPECTRUM MONITORING MODEL RESEARCH ON RICE GROWTH INDEX BASING ON CGMD402
Abstract：Rice growth monitoring is one of the hotspots in modern agriculture. In crop production and management, it is important to get real-time, rapid and accurate access to crop information. It directly affects the formation of the crop yield and the quality. The traditional information acquisition of crop growth is basically depending on sampling in the field and laboratory analysis test, which is time-consuming and easy to destroy plants. The traditional crop growth information acquisition mainly depends on the field sampling and indoor chemical analysis experiment, which is time-consuming and laborious, and it is difficult to meet the needs of rapid and non-destructive crop growth information. Because of its advantages of fast, real-time, non-destructive and so on, it is becoming an important means of information acquisition in the process of precision management of crop production. CGMD402 crop growth monitoring instrument developed by Nanjing Agricultural University National Agricultural Information Engineering Technology Center, which has the advantages of small size, light weight, low cost, and high integration, strong practicability, suitable for field test.The purpose of this study is to research on quantitative relation between rice growth index and CGMD 402 crop growth monitoring diagnostic instrument output vegetation index, the rice field of different varieties and different nitrogen levels, to regularly use CGMD402 to obtain canopy after transplanting the normalized difference vegetation index (Normalized difference vegetation index, NDVI) and ratio vegetation index (Ratio vegetation index, RVI), synchronous destructive sampling for leaf dry weight (Leaf draw, weight, LDW), leaf area index (Leaf area, index, LAI) and leaf nitrogen content (Leaf nitrogen, content, LNC) and other agronomic parameters, the establishment of NDVI and RVI of rice growth monitoring model based on spectral index. The results show that the CGMD402 crop growth monitoring and diagnosis instrument has a good correlation with rice growth information, normalized difference vegetation index (NDVI) measurement and regression equations between LDW, LAI, LNC, R2 were 0.6140, 0.6625, 0.6328, RMSE were 0.15, 0.20, 0.13; the fitting equation between RVI and LDW, and LAI LNC and R2 were 0.6173, 0.6565, 0.6554, RMSE were 0.18, 0.18, 0.12. At the same time, for the accuracy of the instrument, at the tillering stage, tillering stage, jointing stage and jointing stage, booting stage, heading stage, using the ASD HandHeld2 portable spectrometer synchronous measurement of spectral vegetation index, and the ASD HandHeld2 portable spectrometer measured NDVI, RVI regression analysis, the coefficient of determination (R2) respectively. 0.7190, 0.7419，Show CGMD 402 crop growth monitoring and diagnosis instrument for rice growth have good monitoring ability, can carry on the quantitative estimates of rice agriculture index, and has high stability and precision, to provide technical support for the nitrogen precise management in rice production. 基于CGMD402的水稻生长指标光谱监测模型研究:http://www.youerw.com/shengwu/lunwen_24087.html