Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters

Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events.Leaf water content(LWC)is an important waterlogging indicator,and hyperspectral remote sensing provides a non-destructive,real-time and reliable method to determine LWC.Thus,based on a pot experiment,winter wheat was subjected to different gradients of waterlogging stress at the jointing stage.Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature.Combined with methods such as vegetation index construction,correlation analysis,regression analysis,BP neural network(BPNN),etc.,we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC.LWC decreased faster under severe stress than under slight stress,but the effect of long-term slight stress was greater than that of short-term severe stress.The sensitive spectral bands of LWC were located in the visible(VIS,400–780 nm)and short-wave infrared(SWIR,1400–2500 nm)regions.The BPNN Model with the original spectrum at 648 nm,the first derivative spectrum at 500 nm,the red edge position(λr),the new vegetation index RVI(437,466),NDVI(437,466)and NDVI´(747,1956)as independent variables was the best model for inverting the LWC of waterlogging in winter wheat(modeling set:R^(2)=0.889,RMSE=0.138;validation set:R^(2)=0.891,RMSE=0.518).These results have important theoretical significance and practical application value for the precise control of waterlogging stress.

KZ31模块化垂直起升井架模态与谐响应分析

为研究新型KZ31模块化垂直起升井架的动态性能,使用ANSYSWorkbench软件完成井架有限元模型的建立,并对井架进行模态与谐响应分析。模态分析确定井架振动特性的同时也是谐响应分析的基础。井架作为低频振动结构,选取其前10阶模态以及相应的模态振型进行分析,发现井架顶部、二层台和前立柱低段处的模态变化较为明显,前立柱的抗弯扭能力更需要加强。通过谐响应得到井架在激励频率下的位移-频率曲线。结果表明,沿Z方向(井架前后)的振动最为明显,钻机顶驱设备开启后的工作频率可避开1.3211Hz、5.1811Hz和7.1323Hz3个危险频率,井架能够有效克服共振发生。

Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data

Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.

西南地区新发玉米害虫一点缀螟危害特点及空间分布型

【目的】明确中国西南地区新发玉米害虫一点缀螟Paralipsa gularis(Zeller)幼虫对玉米的为害特征和空间分布,为田间预测预报与防治工作提供理论支撑。【方法】采用5点随机取样法对云南省江城县玉米田一点缀螟幼虫的发生与为害情况进行抽样调查,通过聚集度指标法和回归分析法分析幼虫在玉米田的空间分布。【结果】田间调查发现,幼虫自玉米乳熟期开始为害,通过钻蛀玉米果穗、穗芯及茎秆,造成籽粒残缺,并引发穗腐。为害部位有明显的蛀孔和钻蛀隧道,并产生的白色排泄物,老熟幼虫在玉米苞叶和果穗上吐丝结茧化蛹。幼虫平均密度范围为0.02-3.62头/株,玉米受害株率为2%-58%,玉米果穗受害率与虫口密度呈正相关。幼虫在玉米果穗上呈聚集分布,个体间相互吸引,分布的基本成分为个体群。聚集均数λ分析表明,幼虫聚集的原因是由环境因素所导致的。基于空间分布型的研究结果,建立了一点缀螟幼虫的理论抽样公式N=(3.8416/D^(2))(4.9015/x+3.5031)和基于幼虫密度防治指标的最佳序贯抽样公式T_(Iwao)(n)=nm_(0)±1.96√n(4.9015m_(0)+3.5031m_(0)^(2))。【结论】本文明确了玉米田一点缀螟的为害特征及其空间分布,提出了田间种群的抽样方法,为田间幼虫种群密度的调查和防治工作提供了技术支撑。