Modeling Dynamics of Leaf Color Based on RGB Value in Rice

This paper was to develop a model for simulating the leaf color changes in rice （Oryza sativa L.） based on RGB （red, green, and blue） values. Based on rice experiment data with different cultivars and nitrogen （N） rates, the time-course RGB values of each leaf on main stem were collected during the growth period in rice, and a model for simulating the dynamics of leaf color in rice was then developed using quantitative modeling technology. The results showed that the RGB values of leaf color gradually decreased from the initial values （light green） to the steady values （green） during the first stage, remained the steady values （green） during the second stage, then gradually increased to the final values （from green to yellow） during the third stage. The decreasing linear functions, constant functions and increasing linear functions were used to simulate the changes in RGB values of leaf color at the first, second and third stages with growing degree days （GDD）, respectively; two cultivar parameters, MatRGB （leaf color matrix） and AR （a vector composed of the ratio of the cumulative GDD of each stage during color change process of leaf n to that during leaf n drawn under adequate N status）, were introduced to quantify the genetic characters in RGB values of leaf color and in durations of different stages during leaf color change, respectively; FN （N impact factor） was used to quantify the effects of N levels on RGB values of leaf color and on durations of different stages during leaf color change; linear functions were applied to simulate the changes in leaf color along the leaf midvein direction during leaf development process. Validation of the models with the independent experiment dataset exhibited that the root mean square errors （RMSE） between the observed and simulated RGB values were among 8 to 13, the relative RMSE （RRMSE） were among 8 to 10%, the mean absolute differences （da） were among 3.85 to 6.90, and the ratio of da to the mean observation values （Clap） were among 3.04 to 4.90%. In addition, the leaf color model was used to render the leaf color change over growth progress using the technology of visualization, with a good performance on predicting dynamic changes in rice leaf color. These results would provide a technical support for further developing virtual plant during rice growth and development.     

小麦生长模型对拔节期和孕穗期低温胁迫响应能力的比较

[目的]作物生长模型是预测和评估气候变化对作物生产力影响的重要量化工具,明确典型作物生长模型对小麦拔节期和孕穗期低温胁迫响应能力的不足,可以为进一步改进低温胁迫对小麦生产力影响的模拟算法提供指导.[方法]本研究将来自4套国际知名小麦生长模型(美国密歇根州立大学的CERES-Wheat、美国华盛顿州立大学的CropSys...     

冬小麦及其叶片发育的模拟研究

为了消除在计算冬小麦全生育期总积温时不同条件造成的差异，用相对发育理论对积温进行了归一化处理。结果表明：与积温相比，相对发育阶段（RDS）表达的冬小麦生长发育进程更加清晰，更具有可比性。邯5316的拔节，抽穗和灌浆期的RDS分别约为0．47，0．59和0．69，连续5茬不施N肥使 3个时期的RDS分别增大到0．52，0．62和0．75，说明冬小麦的生长发育受到了严重的抑制；水分和盐分盐迫与缺N胁迫的效应相似；推迟20d播种时冬小麦前期的发育较快，上述3个时期的RDS分别为0．39，0．52和0．64。以RDS表示的叶片出现时间与叶片在主茎上的叶序之间存在显著的线性相关关系，方程的斜率和截距分别为0．0381和0．05，回归系数R^2达到0．8916；在非生物因子影响很小的理想条件下，叶片的衰亡时间与出叶时间的规律相似，回归方程的斜率，截距和回归系数分别为0．2099，0．00681和0．9938。在当地大田试验条件下，日长变化对几个品种叶片的出生速率影响不大。     

基于无人机图像的小麦主要生育时期LAI估算

LAI是表征作物生长状况的重要指标之一.为了快速无损监测小麦LAI,设置了3个密度和4个氮肥水平以形成不同的小麦生长群体,利用无人机搭载的RGB相机获取田间图像,并同步取样测定LAI.在小麦越冬期、返青期、拔节期、开花期和灌浆期选取R、G、B构建的9种颜色特征指数,与实测的LAI进行相关性分析.结果表明,在小麦生长前期...     

基于有效积温的冬小麦返青后植株三维形态模拟

【目的】基于有效积温,利用三维建模技术,实现小麦生长模型与形态模型的有机结合,真实表达环境因素对小麦生长发育和形态结构的影响,最终实现小麦生长过程的三维可视化,为小麦作物生长动态预测、栽培管理调控、作物株型设计等提供重要参考。【方法】以天津地区主要推广小麦品种衡观35、济麦22和衡4399为材料,于2015—2016年冬小麦生长季内开展不同小麦品种和施氮水平的田间试验,采集各品种冬小麦在不同施氮水平下的叶长和最大叶宽等形态数据,通过分析各品种冬小麦返青后形态数据和有效积温的定量关系,用Logistic方程构建了冬小麦返青后叶片叶长、最大叶宽模拟模型,并对该模型进行检验;基于该模拟模型,计算各品种冬小麦返青后每个生长日的形态数据,借助OpenGL和NURBS曲面造型技术,构建冬小麦几何形态模型,最终实现冬小麦生长模型与形态模型的结合,实现了冬小麦返青后生长过程可视化。【结果】在不同品种、不同施氮水平下,小麦叶长回归方程R2值在0.772—0.983之间,F值在10.153—340.191之间,且Sig小于显著水平0.05,最大叶宽回归方程R~2值在0.853—0.999之间,F值在17.371—4 359.236之间,且Sig小于显著水平0.05,表明上述模型拟合度和显著性均较好。经数据检验,叶长模型绝对误差在0—3.88 cm之间,根均方差(RMSE)值在0.24—1.95 cm之间,最大叶宽模型绝对误差在0—0.28 cm之间,RMSE值在0.02—0.15 cm之间,表明所建模拟模型精度较高,该模型对不同品种冬小麦返青后的叶片生长具有较好的预测性;基于所建模拟模型计算冬小麦返青后逐日形态数据,可构造不同品种、不同施氮水平下的冬小麦植株形态,可逼真模拟冬小麦返青后植株动态生长过程。【结论】基于有效积温构建的冬小麦返青后叶长和最大叶宽模拟模型,可较好预测冬小麦返青后叶片生长状态,可实现小麦生长模型和形态模型的有机结合,实现不同品种冬小麦在不同施氮水平下的叶片生长可视化。     

橡胶树叶片不同发育时期SPAD值及叶色动态变化

以橡胶树12个无性系为试验材料,测定叶片从萌动至脱落整个生长周期的SPAD值及记录叶色的动态变化情况,绘制叶片SPAD值随生长时间的变化趋势图,比较分析无性系间叶片SPAD值及叶色动态变化过程的差异与联系,为橡胶树叶片物候期划分提供理论参考。结果表明,橡胶树叶片SPAD值随时间的变化趋势可以分成3个阶段:上升阶段、稳定阶段和下降阶段。叶片颜色在各阶段的表现差异明显,上升阶段包括古铜色、古铜-浅黄色过渡色、浅黄色、淡绿色及绿色,平稳阶段只含深绿色,下降阶段包括深绿-绿带紫过渡色、绿带紫色、黄带红色及黄色。不同物候期无性系间历时时长存在较大差异,且对应的SPAD值差异也较大。186、272、314、277及620等5个无性系萌动至浅色期持续时间较短,而深绿色期持续时间较长,且整个生长期SPAD值相对较高;而551、552及553等3个无性系萌动至浅色期持续时间较长,深绿色期持续时间较短,且整个生长期SPAD值相对较低。建议根据橡胶树叶片SPAD值与叶色相结合将橡胶树叶片生长物候划分成3个阶段11个时期,包括萌动期、古铜期、变色期、浅色期、淡绿期、绿色期;深绿色期;深绿-绿带紫过渡期、绿带紫色期、黄带红色期及脱落期。     

河南省冬小麦种植频率时空变化及影响因素分析

【目的】通过对河南省2001—2015年间不同时期（2001—2005、2006—2010及2011—2015年）冬小麦种植频率（winter wheat planting frequency,WWPF）时空变化及其主要影响因素定量分析,进一步明晰区域作物种植频率变化时空变化分布特征和主要影响因素顺序。【方法】以河南省为研究区,冬小麦为研究作物,在利用中低分辨率MODIS EVI时序遥感数据和CART决策树算法进行连续15年（2001—2015年）作物种植空间分布信息提取基础上,获取了研究区不同时期冬小麦种植频率空间信息。在此基础上,开展不同时期冬小麦种植频率时空变化分析,并利用相关分析、主成分分析和线性回归分析等数理统计方法对不同时期研究区种植频率变化的影响因素进行分析,最终确定主要影响因素的重要性排序。【结果】基于MODIS EVI时序遥感数据和CART决策树算法可获得河南省较高精度连续多年冬小麦种植空间分布信息,经验证,研究区冬小麦遥感提取平均总体精度为90.39%,Kappa系数在0.82—0.92之间,可满足区域冬小麦种植频率变化研究所需作物空间分布精度要求;通过分析河南省不同时期冬小麦种植频率时空变化信息,省域内冬小麦主产区大部分具有较高的冬小麦种植频率（WWPF>80%）,而豫西南和豫南等山区由于地形复杂、自然条件较差导致冬小麦种植频率普遍较低（WWPF≤40%）。此外,3个时段期间,河南省冬小麦主产区高频种植冬小麦面积呈逐步增加趋势,WWPF>80%的面积比例分别为42.68%、59.94%和63.07%,低频种植面积呈减小趋势,WWPF≤40%的面积比例分别为28.53%、17.99%和16.63%,这对我国冬小麦主产区稳定粮食种植面积具有重要意义;从冬小麦种植频率影响因素分析结果看,河南省冬小麦种植频率与有效灌溉面积比例、土壤质量综合指数、播期气候适宜度、坡度和高程等指标间均存在显著的相关性,且除与坡度、高程呈负相关外,与其余因素均为正相关关系。以上指标对河南省冬小麦种植频率变化影响程度的排序结果为土壤综合质量指数>播期气候适宜度>有效灌溉面积比例>坡度（高程）,即土壤质量>播期气候条件>灌溉条件>地形条件。【结论】通过对河南省冬小麦种植频率时空变化及其影响因素进行定量分析,明确了河南省冬小麦种植频率时空分布特征和变化规律,明晰了河南省区域冬小麦种植频率变化影响因素及其重要性排序,为开展作物种植面积变化分析提供了一定技术方法和思路借鉴,为区域农业土地利用决策模型构建提供一定基础理论支撑。     

基于Lab颜色空间下的小麦赤霉病图像分割

针对智能识别小麦赤霉病方法中分割患病麦穗图像效果不佳的问题,运用中值滤波方法对患赤霉病麦穗图像进行降噪预处理,采用基于阈值的最大类间方差算法(OTSU)、基于聚类的k-means算法在RGB、HSV和Lab颜色空间中对小麦扬花期到黄熟期感染赤霉病的麦穗图像进行分割,提取出麦穗的病害部分。采用试验田环境下扬花期到黄熟期200张患赤霉病的麦穗图像进行分割试验,结果表明:将图像从RGB颜色空间转化为Lab颜色空间并对a分量采用最大类间方差算法(OTSU)进行分割的效果最佳,误分率仅有1.11%。     

土壤水分运动与作物生长过程耦合机理模型初探

根据冬小麦生长发育与土壤水分条件关系的机理研究,建立了田间条件下的土壤水分运动与作物生产耦合机理模型。该模型包括土壤水分运动子模型和作物生长发育子模型,分别模拟了棵间蒸发、根系吸水、生育期、光合、呼吸、同化物分配、根系生长、叶面积增长、产量形成等过程。用1995-10～1996-06的田间试验资料对模型进行验证,结果表明该模型能较好地模拟土壤水分运动与作物生长发育过程的耦合关系。     

A Preliminary Study on Systems Engineering-Based Method for the Evaluation of Allelopathic Potential in Crops and Its Application

The objective of this study was to develop a method to assess and analyze the total allelopathic potential of crop germplasm and to test this method on four winter wheat accessions commonly planted in the Loess Plateau. A systems engineering model was developed and used to evaluate the total allelopathic potential of crop cultivars. In addition, a method for quantifying the total allelopathic potential in crop accessions was presented. Total allelopathic potential of four winter wheat accessions from the Loess Plateau was estimated and compared using a systems theory approach. The model assessed allelopathic potential in different parts of the plants from the time wheat turned green in spring until maturity. Results from these models indicated that the four wheat accessions had very weak allelopathic potential. Allelopathic potential declined in the order Xiaoyan 22 〉 Ningdong 1 〉 Fengchan 3 〉 Bima 1. This system engineering evaluation method allows for the assessment of allelopathic potential among crop varieties. It will help plant breeders to select and develop allelopathic crop accessions that combine weed suppression properties with agronomic traits related to yield and quality.     

限量灌溉不同品种冬小麦茎可溶性糖与产量和水分利用率的关系

【目的】通过分析不同品种冬小麦灌浆期穗下节可溶性糖含量(SS)、籽粒产量、水分利用效率(WUE)的差异,探讨限量灌溉条件下SS与产量和WUE的关系。【方法】以中国北方12个冬小麦品种(系)为材料,在甘肃陇东黄土高原进行大田试验。【结果】不同冬小麦品种之间产量、WUE、SS存在明显差异,随着灌浆进程的推进,SS含量呈先升高后降低的单峰变化趋势,且灌浆中后期差异达到最大。冬小麦灌浆期SS与产量、WUE均呈显著正相关,并且相关性在灌浆中后期和后期达到极显著;【结论】限量灌溉条件下冬小麦籽粒灌浆期SS可作为筛选高产和高水分利用效率品种的参考指标之一。     

RGB与HSI色彩空间下预测叶绿素相对含量的研究

为探明RGB与HSI两种色彩空间下水稻叶色图像参数与叶绿素相对含量(SPAD)之间的关系,应用支持向量机的方法预测水稻叶片的SPAD值,为快速精准获取植物SPAD值提供理论基础,同时为科学施肥提供理论指导。水稻田间试验于2015—2017年在江西农业大学农学试验站和江西省成新农场进行,供试水稻品种为金优458(JY458)、中早35(ZZ35)和两优培九(LYP9),每个水稻品种均设计4组不同的氮素水平。通过对获取的水稻图像进行叶色参数提取以及叶绿素仪测量的SPAD值来分析水稻叶色图像参数与SPAD值之间的关系,并用支持向量机的方法建立相关模型预测SPAD值。结果显示,较RGB色彩空间下三种水稻品种在HSI色彩空间上预测值的均方根误差分别减少了0.067 5(JY458)、0.020 0(ZZ35)和0.154 2(LYP9),平均相对误差比RGB色彩空间下分别减少了0.084 2(JY458)、0.133 5(ZZ35)和0.238 2百分点(LYP9)。水稻叶片在两种不同色彩空间下的叶色图像参数和水稻叶片SPAD值之间存在显著性相关(P<0.05),利用改进的网格搜索算法优化支持向量机的方法建立水稻叶片SPAD值预测模型,其预测结果误差小,为快速准确无损获取植物SPAD值的预测提供了一种新方法。     

基于系统工程原理的作物化感潜力评价及其应用初探

 【目的】探索作物化感潜力评价方法，分析作物整体化感潜势，理论评价黄土高原旱作小麦的综合化感潜势。【方法】建立作物化感潜力的系统工程评价模型，提出作物植株整体评价的定量描述方法，对黄土高原半干旱区4种普通小麦品种化感潜力进行理论上的整体评价和总体化感潜力比较。【结果】对黄土高原半干旱区4种普通小麦品种的化感潜力综合评价理论上得出其化感潜力总体处于较低水平，其中化感潜力综合表现为小偃22号>宁冬1号>丰产3号>碧玛1号。【结论】系统工程评价模型的建立对于辅助快速有效地评价作物品种和单植株的化感潜力和培育具有抗草和高产优质及相关优良农艺特征作物新品种具有重要的科学指导意义。     

基于高光谱成像估测冬小麦不同生育时期水分状况

【目的】研究实时、快速估测冬小麦不同生育时期水分状况并构建模型,为冬小麦水分精准管理提供科学依据。【方法】以新疆典型滴灌冬小麦为研究对象,应用高光谱成像技术获取冬小麦冠层光谱信息,并对原始光谱反射率进行平滑和数据变换,利用一元线性回归(Simple linear regression,SLR)、主成分回归(Principal components regression,PCR)和偏最小二乘回归(Partial least squares regression,PLSR)3种建模方法,对冬小麦冠层原始光谱及变换光谱分别构建植株水分含量估测模型。【结果】冬小麦冠层原始光谱反射率与植株水分含量相关性不高,对原始光谱反射率进行数据变换可以显著增强与水分含量的相关性和相关波段数,其中倒数一阶微分变换与冬小麦植株水分含量的相关系数最大,为-0.893 0,但不同变换最优相关系数所对应的波段位置并不固定。PLSR方法的模型精度最高,对数变换的PLSR模型估测精度最高,模型$R_{p}^{2}$、RMSE_p、RPD值分别为0.880 8、3.251 2%、2.934 3;冬小麦不同生育时期估测模型精度存在差异,拔节期、抽穗期估测模型精度较低,灌浆中期最高,其估测模型$R_{p}^{2}$、RMSE_p、RPD值分别为0.904 8、1.381 1%、3.454 7。【结论】利用高光谱成像技术对估测冬小麦植株水分含量是可行的,在灌浆中期的估测效果最佳。     

A simulation of winter wheat crop responses to irrigation management using CERES-Wheat model in the North China Plain

To improve efficiency in the use of water resources in water-limited environments such as the North China Plain(NCP), where winter wheat is a major and groundwater-consuming crop, the application of water-saving irrigation strategies must be considered as a method for the sustainable development of water resources. The initial objective of this study was to evaluate and validate the ability of the CERES-Wheat model simulation to predict the winter wheat grain yield, biomass yield and water use efficiency(WUE) responses to different irrigation management methods in the NCP. The results from evaluation and validation analyses were compared to observed data from 8 field experiments, and the results indicated that the model can accurately predict these parameters. The modified CERES-Wheat model was then used to simulate the development and growth of winter wheat under different irrigation treatments ranging from rainfed to four irrigation applications(full irrigation) using historical weather data from crop seasons over 33 years(1981–2014). The data were classified into three types according to seasonal precipitation: 100 mm, 100–140 mm, and 140 mm. Our results showed that the grain and biomass yield, harvest index(HI) and WUE responses to irrigation management were influenced by precipitation among years, whereby yield increased with higher precipitation. Scenario simulation analysis also showed that two irrigation applications of 75 mm each at the jointing stage and anthesis stage(T3) resulted in the highest grain yield and WUE among the irrigation treatments. Meanwhile, productivity in this treatment remained stable through different precipitation levels among years. One irrigation at the jointing stage(T1) improved grain yield compared to the rainfed treatment and resulted in yield values near those of T3, especially when precipitation was higher. These results indicate that T3 is the most suitable irrigation strategy under variable precipitation regimes for stable yield of winter wheat with maximum water savings in the NCP. The application of one irrigation at the jointing stage may also serve as an alternative irrigation strategy for further reducing irrigation for sustainable water resources management in this area.     

Calibration and validation of SiBcrop Model for simulating LAI and surface heat fluxes of winter wheat in the North China Plain

The accurate representation of surface characteristic is an important process to simulate surface energy and water flux in land-atmosphere boundary layer. Coupling crop growth model in land surface model is an important method to accurately express the surface characteristics and biophysical processes in farmland. However, the previous work mainly focused on crops in single cropping system, less work was done in multiple cropping systems. This article described how to modify the sub-model in the SiBcrop to realize the accuracy simulation of leaf area index(LAI), latent heat flux(LHF) and sensible heat flux(SHF) of winter wheat growing in double cropping system in the North China Plain(NCP). The seeding date of winter wheat was firstly reset according to the actual growing environment in the NCP. The phenophases, LAI and heat fluxes in 2004–2006 at Yucheng Station, Shandong Province, China were used to calibrate the model. The validations of LHF and SHF were based on the measurements at Yucheng Station in 2007–2010 and at Guantao Station, Hebei Province, China in 2009–2010. The results showed the significant accuracy of the calibrated model in simulating these variables, with which the R~2, root mean square error(RMSE) and index of agreement(IOA) between simulated and observed variables were obviously improved than the original code. The sensitivities of the above variables to seeding date were also displayed to further explain the simulation error of the SiBcrop Model. Overall, the research results indicated the modified SiBcrop Model can be applied to simulate the growth and flux process of winter wheat growing in double cropping system in the NCP.     

Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean

Leaf photosynthesis and rhizobial nitrogen fixation are the two metabolic processes of utmost importance to legume growth and development. As these processes are closely related to each other, measuring of leaf chlorophyll content can provide information on the nodulation and nitrogen fixation status of crop plants. In the present investigation, a number of soybean breeding lines consisting of near-isogenic families which are genetically segregating for the nodulation trait were utilized in field experiments carried out across three growing seasons at Vienna, Austria. For phenotyping leaf chlorophyll content, the Minolta SPAD spectrometer was applied in parallel to a simple leaf digital image analysis procedure based on a commercial digital still camera. The main objectives of the research included the comparison of SPAD metering and image analysis for determination of chlorophyll content, phenotyping of the soybean nodulation vs. non-nodulation characteristic with respect to leaf, agronomic and seed traits, and relating both chlorophyll and image analysis data to seed quality characteristics. Nodulating and non-nodulating soybean lines significantly differed in chlorophyll content from the V5 (five leaves fully developed) soybean developmental stage onwards. Apart from chlorophyll content, leaf size, plant height, number of pods per plant, 1000-seed weight, and seed protein and oil content were also affected by nodulation type. The chlorophyll content of soybean leaves as determined by SPAD metering was significantly correlated (r = −0.937) to the green color value (RGB color model) of leaf image analysis at the R3 (beginning of pod growth) soybean developmental stage. Both chlorophyll content and leaf image analysis parameters were correlated to 1000-seed weight, seed protein and seed oil content. Thus, it appears that these leaf parameters related to photosynthesis and nitrogen fixation could be utilized to determine the nitrogen status of a soybean crop and subsequently in forecasting seed quality parameters of the harvest product.     

A variable thermal time of the double ridge to flag leaf emergence phase improves the predictive quality of a CERES-Wheat type phenology model

A modified version of the widely used CERES-Wheat phenology model (CWm) was developed introducing variable thermal time from double ridge stage to flag leaf emergence and compared with the original model (CW V3.x). Both model versions were newly implemented and calibrated using routinely collected field ratings of phenological stages of winter wheat crops. Calibration for all parameters of both model versions was done using a step by step procedure thereby using different methods for parameter identification. For calibration and validation of the model a data set containing more than 6000 single observations of wheat phenology was used. The improved model version had a better prediction of phenological (BBCH) stages compared to the original CW approach for an independent validation data set. The average RMSE for BBCH 37 and BBCH 39 was decreased by two and five days, respectively. We could show that it was possible to calibrate the wheat phenology models using solely routinely available field ratings. Because the predictive quality for wheat phenology of actual wheat crop growth simulators without calibration especially often is still limited the presented approach may be seen as an approach for improving this type of models without especially designed experiments.     

基于新型植被指数的冬小麦LAI高光谱反演

【目的】本研究旨在分析冠层叶片水分含量对作物冠层光谱的影响,构建新型光谱指数来提高作物叶面积指数高光谱反演的精度。【方法】在冬小麦水肥交叉试验的支持下,分析不同筋性品种、施氮量、灌溉量处理下的冬小麦叶面积指数冠层光谱响应特征,并分析标准化差分红边指数(NDRE)、水分敏感指数(WI)与叶面积指数的相关性,据此构建一个新型的植被指数——红边抗水植被指数(red-edge resistance water vegetable index,RRWVI)。选取常用的植被指数作为参照,分析RRWVI对于冬小麦多个关键生育期叶面积指数的诊断能力,随机选取约2/3的实测样本建立基于各种植被指数的叶面积指数高光谱响应模型,未参与建模的样本用于评价模型精度。【结果】研究结果表明,随着生育期的推进,冬小麦的叶面积指数呈先增加后降低的变化趋势,不同的水肥处理对冬小麦叶面积指数具有较大影响。开花期之后冬小麦LAI显著下降,强筋小麦(藁优2018)在整个生育期叶面积指数均高于中筋小麦(济麦22);不同氮水平下冬小麦冠层光谱反射率在近红外波段(720—1 350 nm)随着施氮量的增加而增大,与氮肥梯度完全一致,其中2倍氮肥处理的近红外反射率达到最高;不同生育期下冬小麦冠层光谱反射率变化波形大体一致;各个关键生育期的NDRE和WI均存在较高的相关性,而NDRE与LAI的相关性明显优于WI,新构建的植被指数RRWVI与LAI的相关性均优于NDRE、WI;虽然8个常用的植被指数均与LAI存在显著相关,但RRWVI与LAI相关性达到最大,其拟合曲线的决定系数R2为0.86。【结论】通过分析各种指数所构建的冬小麦叶面积指数高光谱反演模型,新构建的RRWVI取得了比NDRE、NDVI等常用植被指数更为可靠的反演效果,说明本研究新构建的红边抗水植被指数可有效提高冬小麦叶面积指数的精度。     

基于图像分割的苹果叶片几何参数计算

针对传统方法和叶面积仪法测定作物叶面积、叶长、叶宽等参数费时费力,成本高等问题,提出苹果叶面积、叶长、叶宽和周长的图像测定法。首先采用颜色空间转换法转换RGB(红、绿、蓝)图为HIS(色调、亮度、饱和度)图,并以大津法(Otsu)阈值色调(H)获取二值图像;再根据二值图中苹果叶边缘像素坐标分布特征,计算叶长、叶宽和叶周长,根据苹果叶片像素数计算叶面积;最后对叶长、叶宽和叶面积的计算值与实测值之间进行均方根误差(RMSE)和决定系数(R~2)计算,并以测定硬币周长的方式验证周长算法。结果表明:苹果叶面积、叶长、叶宽的RMSE值分别为0.58cm~2、0.46和0.10cm,R~2值分别为0.99、0.93、0.97,测定的1元、5角、1角硬币的周长分别为7.68、6.42和6.10cm。RMSE取值较小和R~2取值较高表明叶面积、叶长和叶宽算法结果可靠,周长算法验证结果表明周长计算值与实测值之间差异较小。