增密种植条件下基于主动冠层传感器GreenSeeker的春玉米氮营养无损诊断研究

针对当前东北地区玉米种植密度偏低、氮肥管理粗放及应用传统氮肥诊断方法存在的限制与不足等问题,以协同玉米增产与氮肥高效为目标,在黑龙江省哈尔滨市开展田间试验,设置2个种植密度、2个玉米品种和6个氮水平小区试验,应用新式主动冠层传感器GreenSeeker,于玉米V5~V10期动态取样监测,建立回归模型,分析增密种植条件下不同玉米品种氮营养诊断指标变化,探究种植密度与品种对主动冠层传感器实时氮营养指标估测模型的影响。结果表明,适量施氮有利于改善植株氮营养状况、促进玉米增产;氮、密互作共同影响籽粒产量,种植密度由D6增至D8,籽粒产量与施氮量线性平台拐点由196.0、200.4 kg N·hm^(-2)分别增加至208.8、209.4 kg N·hm^(-2);氮浓度稀释曲线Y=34.0X-0.37在黑龙江地区适用,氮营养指数(Nitrogen nutrition index,NNI)诊断阈值主要受种植密度影响;GreenSeeker植被指数(Normalized difference vegetation index,NDVI)与地上部生物量、植株吸氮量、叶面积指数相关性较好,决定系数达0.75~0.89;NNI与NDVI相关模型决定系数较低,仅为0.49~0.58;NDVI对植株氮浓度诊断结果不佳。NDVI可用来准确诊断玉米氮营养状况,但需综合考虑氮肥用量、密度与品种耐密性对诊断模型准确性的影响。研究将GreenSeeker传感器最新诊断技术引入玉米增密群体氮营养诊断,评估氮浓度稀释曲线适用性,相关结果可为玉米增密群体的氮精准管理提供理论依据,未来具备区域大面积应用潜力。

基于SPAD-502与GreenSeeker的冬小麦氮营养诊断研究

用GreenSeeker和SPAD-502测定了不同氮素处理的冬小麦冠层NDVI与叶片SPAD值,分析了它们与叶片全氮、叶绿素含量及产量间的关系。结果表明:冬小麦抽穗期SPAD值和NDVI值均与叶绿素含量呈极显著正相关;除抽穗期和返青期外,SPAD值与叶氮含量、叶绿素含量的相关系数在其余各生育期均达到显著或极显著水平;NDVI值与叶氮含量、叶绿素含量在拔节期、乳熟期的相关性同SPAD值一致;SPAD值可以进行叶绿素的诊断,NDVI值可以进行氮的诊断。氮营养诊断时期应该选择拔节期。通过回归建立了基于SPAD值、NDVI值的产量估测模型,可以通过SPAD值、NDVI值对冬小麦产量进行估算。

利用GreenSeeker法诊断春玉米氮素营养状况的研究

利用GreenSeeker对不同氮素处理春玉米冠层和叶片进行了测定,分析了植被指数(NDVI)和叶片叶绿素a(Chla)含量和氮素(N)含量的相关性。结果表明,不同氮素处理冠层NDVI变化与叶片叶绿素a含量在整个生育期内变化趋势一致。NDVI对叶绿素变化最敏感的时期是大喇叭口期。从不同叶位的变化趋势看,NDVI随CHla、N含量的变化而表现出明显的变化,三者之间具有显著的直线相关关系。利用GreenSeeker获取的NDVI值为生产中诊断春玉米叶绿素或氮素状况提供了有效手段。

基于Greenseeker光谱法的棉花氮素营养诊断研究

利用主动遥感光谱仪(Greenseeker)对新疆2个主栽棉花品种4个施氮量条件下2年生育期间的冠层归一化植被指数(NDVI)值进行测试,研究不同施氮量对棉花冠层NDVI值、植株氮素积累速率和叶片全氮含量的影响以及田间条件下简便、快速、非接触性的作物氮素营养状况诊断方法。结果表明:在棉花生育期内,冠层NDVI值呈单峰曲线变化,峰值出现在出苗后90d左右,以后逐渐降低;NDVI值和棉株氮素积累速率呈正相关关系,决定系数R^2在盛花期达到最大,为0.844.7;NDVI值变化趋势与叶片全氮含量呈正相关,决定系数R^2在盛花期达到0.7980。棉花冠层NDVI值随着施氮量增加而提高,不同氮素处理间NDVI值在盛花期和花铃期达极显著差异;主动遥感光谱仪在棉花生长的盛花期和花铃期可以预测棉花氮素养分状况。

主动作物冠层传感器GreenSeeker在马铃薯氮素营养诊断中应用的可行性分析

长期以来氮肥的施用在马铃薯生产中起到了重要作用。但是,随着氮肥施用量的增加,氮肥利用效率势必会降低并造成一定的环境问题。因此,根据马铃薯的需肥规律进行氮肥的施用,提高氮肥利用率已成为业界共识。然而,传统的氮肥推荐方法如土壤测试的方法费时费力、实时性差。随着遥感测试技术的发展,一些传感器应运而生,使基于光谱的植株氮营养快速检测成为可能。本文介绍了主动作物冠层传感器GreenSeeker对作物营养诊断的原理及其在玉米、小麦等作物上应用的国内外研究进展,并讨论其在马铃薯氮素营养诊断和精确施肥的重要性、可行性及应用前景。

利用GreenSeeker与SPAD监测棉蚜发生量

为探究快速获取棉蚜信息的方法,在人工计量棉田棉蚜发生量的基础上,采用SPAD-502叶绿素测定仪测定棉花植株叶绿素含量,GreenSeeker手持式高光谱仪同步测定棉花冠层光谱(NDVI、VI_2、REDrefc、NIRrefc),最后分析棉蚜发生量和棉花冠层光谱信息与棉花叶片SPAD值的相关关系。结果表明:(1)NDVI和NIRrefc均与棉蚜发生量呈极显著负相关,VI_2与棉蚜发生量呈极显著正相关。其中,VI_2值和有翅蚜的三次函数拟合效果最好(P=0.923 73)。(2)SPAD值与棉蚜发生量呈极显著负相关,其中,SPAD值和无翅蚜的对数函数拟合效果最好(P=0.854 79)。模型的构建在一定程度上为棉蚜的间接、无损和快速监测和管理提供理论基础和技术手段。

光谱仪在作物施氮推荐中的应用研究进展——以GreenSeeker光谱仪为例

随着光谱技术的发展,利用光谱仪进行作物的氮素营养诊断得到普遍研究应用。Green Seeker光谱仪是近年来在美国兴起的一种新型便携式氮肥推荐仪器,该仪器以其快速、无损、准确、非接触的特点为作物的氮素诊断和施肥推荐提供了一种新的方法思路,并在国内外开始研究应用。以Green Seeker光谱仪为例,对光谱仪在作物氮肥推荐中的研究进展进行综述,旨在让大家通过该仪器更好地了解光谱仪氮肥推荐的工作原理、研究进展,同时在使用中尽可能地避免影响该光谱仪测定值准确性的因素,使此技术更快地应用于生产实践,推动我国氮素诊断和推荐施肥技术的进步。

基于GreenSeeker的烟田管理分区研究

利用GPS在研究区确定20m间隔的取样点101个,在烟株移栽后40天和60天利用主动冠层光谱仪GreenSeeker测定各样点烟株冠层的归一化植被指数(NDVI),并运用克里格插值获取NDVI空间分布图,利用模糊c-均值聚类方法对烟田进行精准管理分区研究,同时利用植烟前土壤养分及烟叶产量数据对分区结果进行评价。结果表明:研究区域的最佳分区数是5个,且分区间各土壤养分含量及烟叶产量均达到显著或极显著性差异。说明利用GreenSeeker获取烟株的植被指数并进行管理分区的划分是有效可行的。

基于GreenSeeker的水稻氮素估测

为研究水稻植株氮素指标与GreenSeeker植被指数的定量关系,通过设置不同年份、不同氮肥水平的田间试验,于移栽后定期使用GreenSeeker获取冠层归一化差值植被指数(NDVI)和比值植被指数(RVI),并同步破坏性取样获取植株生物量和氮含量,分析不同品种和不同氮营养条件下氮素和植被指数(NDVI和RVI)变化规律,建立基于NDVI和RVI的氮素监测模型。结果表明,植株氮含量可以基于NDVI和RVI分时期进行估算,植株氮积累量可以被分阶段反演。利用GreenSeeker可以实现水稻氮素快速无损监测,为水稻氮肥精确管理提供技术支持。

Laboratory evaluation of the GreenSeeker^(TM) handheld optical sensor to variations in orientation and height above canopy

Handheld optical sensors recently have been introduced to the agricultural market.These handheld sensors are able to provide operators with Normalized Difference Vegetative Index(NDVI)data when cloud cover prevents acquisition of satellite or aerial images.This research addressed the sensitivity of the GreenSeeker handheld optical sensor to changes in orientation and height above a ryegrass canopy.Planter boxes were oriented both parallel and perpendicular to the light beam from the sensor head and heights of 30.5 cm(12”),61.0 cm(24”),91.5 cm(36”),122 cm(48”)and 152 cm(60”)were tested.Results indicated that the sensor was highly sensitive(P<0.0001)to both height above canopy and orientation of the sensor relative to the target.Operators should follow manufacturer’s recommendations on operating height range of 81-122 cm and orient the sensor head in-line with the target to obtain maximum signal response.

Wheat grain yield and nitrogen uptake prediction using atLeaf and GreenSeeker portable optical sensors at jointing growth stage

Rapid acquisition of information about nitrogen(N)uptake and grain yield is an essential step in making site-specific in-season fertilizer N management decisions.The objective of this study was to quantify and validate the relationships between N uptake and grain yield of wheat using in-season measurements with atLeaf chlorophyll meter and GreenSeeker optical sensor at Feekes 6 growth stage(jointing stage)of wheat.The relationships were developed using data generated from experiments with multi-rate fertilizer N treatments and conducted in two consecutive wheat seasons(2017/2018 and 2018/2019)at two locations in the western Nile Delta of Egypt.A power function based on atLeaf measurement at Feekes 6 stage of wheat could explain 55.3%and 53.3%variations in the N uptake at this stage and grain yield at maturity,respectively.Measurements with GreenSeeker were related with N uptake and yield of wheat through exponential function and could explain 68.5%and 60.6%of the variation in N uptake and grain yield,respectively.The developed models were validated on an independent data set from another field experiment on wheat.The normalized root mean square error for the relation between atLeaf measurements and N uptake and grain yield were fair,whereas the fits were good for measurements with GreenSeeker.This study reveals that atLeaf chlorophyll meter and GreenSeeker optical sensor can be successfully used for establishing site-specific N management strategies in wheat.

棉花不同水氮组合NDVI的差异性及产量效应研究

为建立最优水氮组合提供参考,设置4个氮肥处理、3个水分处理的大田试验,随机分组,分析了棉花不同水氮组合NDVI和棉花生长状况的差异性,建立以产量为因变量的水肥模型。结果表明,不同水氮组合的棉花冠层NDVI变化都呈"低-高-低"趋势。施氮量对棉花冠层NDVI的影响大于灌水;依据建立水氮效应的产量模型确定的最高施氮量和灌水量分别为298.47 kg/hm2和3 698.75 m3/hm2,理论棉花产量为6 597.02 kg/hm2。因此,合理的水氮组合既能减少水肥浪费,又能提高棉花产量。棉花冠层NDVI能够很好地反映棉花生长状况,可替代传统方法获取棉花生长指标。

农作物冠层光谱信息的施肥管理分区研究

随着地面遥感技术的不断发展,越来越多的农作物冠层光谱检测传感器被应用到了农业生产,其中应用较为广泛的就是Greenseeker植物光谱检测仪,利用Greenseeker植物光谱检测仪可以获取农作物冠层光谱信息归一化植被指数(NDVI)数据,从而能够进行农作物的施肥管理分区的划分,依据划分好的施肥管理分区可以实现有针对性的变量施肥。模糊c-均值(FCM)算法是划分农作物施肥管理分区常用的算法,但是模糊c-均值算法具有一定的局限性,就是在计算过程中随着NDVI数据量的增加会不断进行数据的迭代计算,从而会影响施肥管理分区划分的速度。在模糊c-均值算法的基础上提出一种基于模型的模糊c-均值(MFCM)算法,基于模型的模糊c-均值算法在划分农作物施肥管理分区过程中不必在每获取一组数据时就对全部数据进行迭代计算,可有效提高划分施肥管理分区的速度。通过搭建的农作物冠层光谱信息采集平台获取大豆和玉米的NDVI数据,利用基于模型的模糊c-均值算法划分大豆和玉米的施肥管理分区,使用分区评价指标轮廓系数(SC)和调整兰德指数(ARI)评价划分施肥管理分区的效果。结果表明,随着获取的NDVI数据量的不断增加,基于模型的模糊c-均值算法相比于模糊c-均值算法能够更快的划分施肥管理分区,在划分大豆施肥管理分区上,基于模型的模糊c-均值算法快0.02~0.15 s;在划分玉米施肥管理分区上,基于模型的模糊c-均值算法快0.07~0.51 s。通过计算评价划分施肥管理分区效果的指标轮廓系数和调整兰德指数发现,在不同NDVI数据量的情况下进行划分施肥管理分区,轮廓系数的值最大相差为0.022,说明两种算法划分施肥管理分区的效果相差不大;调整兰德指数的值对数据的波动变化比较敏感,在NDVI数据量达到6000后能够维持在0.7以上,但当NDVI数据波动变化较大时会出现一定的下降。

Spectral indices measured with proximal sensing using canopy reflectance sensor,chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen(N)topdressing decisions.Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field.The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor,soil and plant analyzer development(SPAD)chlorophyll meter,and two different types of leaf color charts(LCCs)for five basmati rice genotypes across different growth stages.Regression analysis was performed using normalized difference vegetation index(NDVI)recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University,Ludhiana(India)(PAU-LCC)and the International Rice Research Institute,Philippines(IRRI-LCC).The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination(R^(2))and minimum normalized root mean square error(NRMSE)at panicle initiation stage and explained 38.3%–76.4%variation in yield using genotype-specific models.Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained53.4%–57.2%variation in grain yield.Normalizing different spectral indices with cumulative growing degree days(CGDD)decreased the accuracy of yield prediction.Normalization with days after transplanting(DAT),however,did not reduce or improve the predictability of yield.The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses.The NDVI-based genotype-specific models exhibited a robust linear correlation(R^(2)=0.77,NRMSE=7.37%,n=180)between observed and predicted grain yields only at 35 DAT(i.e.,panicle initiation stage),while the SPAD,PAU-LCC,and IRRI-LCC consistently provided reliable predictions(with respective R^(2)of 0.63,0.60,and 0.53 and NRMSE of 10%,10%,and 13.6%)even with genotype invariant models with 900 data points obtained at different growth stages.The study revealed that unnormalized values of spectral indices,namely NDVI,SPAD,PAU-LCC,and IRRI-LCC,can be satisfactorily used for in-season estimation of grain yield for basmati rice.As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors,they can be preferably used by small farmers in developing countries.

Nondestructive estimation of bok choy nitrogen status with an active canopy sensor in comparison to a chlorophyll meter

Precise estimation of vegetable nitrogen(N)status is critical in optimizing N fertilization management.However,nondestructive and accurate N diagnostic methods for vegetables are relatively scarce.In our two-year field experiment,we evaluated whether an active canopy sensor(GreenSeeker)could be used to nondestructively predict N status of bok choy(Brassica rapa subsp.chinensis)compared with a chlorophyll meter.Results showed that the normalized difference vegetation index(NDVI)and ratio vegetation index(RVI)generated by the active canopy sensor were well correlated with the aboveground biomass(AGB)(r=0.698–0.967),plant N uptake(PNU)(r=0.642–0.951),and root to shoot ratio(RTS)(r=-0.426 to-0.845).Compared with the chlorophyll meter,the active canopy sensor displayed much higher accuracy(5.0%–177.4%higher)in predicting AGB and PNU and equal or slightly worse(0.54–1.82 times that of the chlorophyll meter)for RTS.The sensor-based NDVI model performed equally well in estimating AGB(R2=0.63)and PNU(R2=0.61),but the meter-based model predicted RTS better(R2=0.50).Inclusion of the days after transplanting(DAT)significantly improved the accuracy of sensor-based AGB(19.0%–56.7%higher)and PNU(24.6%–84.6%higher)estimation models.These findings suggest that the active canopy sensor has a great potential for nondestructively estimating N status of bok choy accurately and thus for better N recommendations,especially with inclusion of DAT,and could be applied to more vegetables with some verification.