Weed Detection Using Canopy Reflection

For site-specific application of herbicides, automatic detection and evaluation of weeds is desirable. Since reflectance of crop, weeds and soil differs in the visual and near infrared wavelengths, there is potential for using reflection measurements at different wavelengths to distinguish between them. Reflectance spectra of crop and weed canopies were used to evaluate the possibilities of weed detection with reflection measurements in laboratory circumstances. Sugarbeet and maize and 7 weed species were included in the measurements. Classification into crop and weeds was possible in laboratory tests, using a limited number of wavelength band ratios. Crop and weed spectra could be separated with more than 97% correct classification. Field measurements of crop and weed reflection were conducted for testing spectral weed detection. Canopy reflection was measured with a line spectrograph in the wavelength range from 480 to 820 nm (visual to near infrared) with ambient light. The discriminant model uses a limited number of narrow wavelength bands. Over 90% of crop and weed spectra can be identified correctly, when the discriminant model is specific to the prevailing light conditions.     

Ground-Sensor Soil Reflectance as Related to Soil Properties and Crop Response in a Cotton Field

Bare soil reflectance from airborne imagery or laboratory spectrometers has been used to infer soil properties such as soil texture, organic matter, water content, salinity and crop residue cover. However, the relation of soil properties to reflectance data often varies with soil type and conditions and surface reflectance may not be representative of the conditions in the root zone. The objectives of this study were to assess the soil reflectance data obtained by ground-based sensors and to model soil properties in the root zone as a function of surface soil reflectance and plant response. Ground-based sensors were used to simultaneously monitor soil and canopy reflectance in the visible and near-infrared (VNIR) along six rows and in two growth stages in a 7 ha cotton field. The reflectance data were compared to soil properties, leaf nutrients and biomass measured at 33 sampling positions along the rows. Brightness values of the blue and green bands of soil reflectance were better correlated to soil water content, particulate organic matter and extractable potassium and phosphorus, while those in the red and NIR bands were correlated to soil carbonate content, total nitrogen, electrical conductivity and foliar nutrients. The correlation of red soil reflectance with canopy reflectance was significant and indicated an indirect inverse relationship between soil fertility and plant stress. The integration of surface soil reflectance and plant response variables in a multiple regression model did not substantially improve the prediction of soil properties in the root zone. However, crop nutrient status explained a significant portion of the spatial variability of soil properties related to nitrification processes when soil reflectance did not. The implication of these findings to agricultural management is discussed.     

大豆叶面积的高光谱模型

以ASD FieldSpec-Vnir光谱仪实测不同生长季大豆的冠层反射率,同期采集对应大豆LAI,然后逐波段分析冠层光谱反射率、导数光谱与大豆LAI的相关关系;并采用单变量线性回归逐波段分析了冠层光谱反射率、导数光谱与大豆LAI确定性系数随波长的变化趋势,建立了以近红外与可见光波段冠层光谱反射率的比值植被指数RVI与大豆LAI的高光谱遥感估算模型。结果表明,冠层光谱反射率在350￣680nm、760￣1050nm波谱区与大豆LAI相关性较大,而在红边区680￣760nm的相关性变化较大;导数光谱在红边区与大豆LAI相关程度高。通RVI方式建立的遥感估算模型能较为准确估算大豆LAI,通过对红外与蓝波段建立的RVI指数与大豆LAI的回归模型,表明其预测大豆LAI的能力较好,有进一步研究的必要;通过对比发现,神经网络模型可以大大提升高光谱反演大豆LAI的水平,模型的确定系数R2为0.9661,而总均方根误差RMSE仅为0.446m2.m-2。     

基于“3S”技术的环境质量评价及其研究展望

论述了环境质量评价的基本内容和基本类型、"3S"技术在环境质量评价中的应用及其局限性,明确了今后基于"3S"技术的环境质量评价应用发展方向和研究领域,特别关注了近年来数据挖掘技术、数据融合技术、高光谱遥感技术给环境质量评价带来的应用前景.     

土壤剖面硝态氮含量的快速测试方法

为缩短分析时间提高时效，建立了田间条件下土壤剖面硝态氮含量测定的方法，主要包括1：1土水质量比快速浸提土壤硝态氮，Merck反射仪进行浸提液硝态氮快速定量及酒精灼烧法快速测定土壤水分，根据土壤容重换算土壤硝态氮含量。将该方法与实验室常规的0．01mol／LCaCl2溶液1：10土水质量比浸提、连续流动分析法比较．结果表明，田间快速法与实验室常规法测定的土壤硝态氮含量具有极显著的相关性，2种方法测定表土层(0～30cm)和底土层(30～90cm)土壤硝态氮质量分数(mg／kgN)的相关系数分别为0．96和0．92，按不同层次土壤容重换算表土层和底土层土壤硝态氮含量(kg／hm^2N)的相关系数为0．93和0．92。     

草坪近地面光谱特征研究进展

论述了草坪光谱反射特征的研究近况。国内外利用光谱分析技术在草坪领域开展了较为深入的研究,发现了高羊茅、多年生黑麦草、草地早熟禾的部分品种之间存在明显的＂同色异谱＂现象;筛选出与草坪外观质量指标显著相关的敏感波段和植被指数,建立了草坪色泽、密度、盖度及冠层叶绿素水平的光谱反演模型;氮肥水平能够明显改变草坪在可见光和近红外...     

近红外光谱技术在牧草育种研究中的应用前景

近红外光谱技术(NIRS)是一种快速、高效、无损的现代检测技术,已经在许多领域广泛应用.简要介绍了NIRS的基本原理、特点以及应用于牧草育种研究的意义,阐述了NIRS在牧草营养品质改良、次生代谢产物分析、病虫害抗性分析、抗逆能力评价等牧草育种方面的应用现状和前景.     

玉米苗期冠层多光谱反射率反演与叶绿素含量诊断

为了探索玉米苗期叶片叶绿素含量指标的快速、非破坏性估测方法,该文运用多光谱图像技术对大田玉米苗期叶绿素含量指标进行快速无损的诊断研究。大田试验中,采用2-CCD多光谱图像采集系统获取大田玉米苗期的冠层多光谱图像,并同步采集漫反射灰度板的多光谱图像。为消除光照对图像采集质量的影响,准确将不同光照条件下的玉米冠层图像数据转换为其叶面反射率数据,标定试验中采用一块4个不同灰度级的满足朗伯面条件漫反射灰度板,建立了叶片光谱反射率同图像灰度值之间的线性反演公式,并与大田试验中漫反射灰度板的多光谱图像建立了玉米冠层图像灰度值的校正公式。对玉米苗期冠层多光谱图像进行处理,提取出玉米冠层B、G、R、NIR(中心波长分别为470,550,620,800 nm)4个波段归一化平均灰度值。通过灰度值的校正公式得到校正后的归一化平均灰度值,由线性公式反演出R、G、B、NIR 4个波段的平均反射率值,并计算4种常见光谱植被指数(RNDVI、RNDGI、RRVI和RDVI),采用最小二乘-支持向量回归(LS-SVR)建立植被指数同叶绿素含量指标的拟合模型。结果表明:植被指数RNDVI、RRVI和RDVI和玉米冠层叶绿素含量指标拟合验证集决定系数R2为0.56,达到了较为理想的拟合结果。证明通过漫反射灰度板对玉米冠层多光谱图像建立反射率反演校正模型的方法是可行的,这一方法为快速无损检测玉米苗期叶绿素含量指标提供了支持。     

近红外技术快速测定肉鸡粪便主要肥料成分含量的研究

该文探讨了利用近红外光谱分析技术(NIRS)快速测定肉鸡粪便主要肥料成分含量的可行性。在饲养试验过程中采集了肉鸡粪便样品183个，利用常规实验室分析方法测定了其中的总氮(TN)、总磷(TP)、总钾(TK)、铵态氮(AN)、有效磷(EP)含量，利用近红外光谱仪取得了样品在1421.5～2572.2 nm波段的光谱，并使用偏最小二乘法(PLS)结合交互验证(CV)和留一检验法(LOO)建立了肉鸡粪便肥料成分的近红外光谱定标模型，同时利用该模型对20个样品的总氮、总磷、总钾、铵态氮、有效磷的含量进行预测，得出的5种主要肥料成分的预测值和真实值(实验室经典化学分析方法测定值)之间具有显著的相关性，其相关系数分别为0.9574，0.9590，0.9870，0.9572和0.9650。预测标准差分别为0.0014，0.0012，0.0012，2.3041×10^-4和0.3814。结果表明，利用近红外光谱法对肉鸡粪便风干样品的主要肥料成分进行快速测定是可行的。     

基于光谱理论的作物营养诊断研究进展

随着光谱技术的发展及其自身的优点,应用光谱技术在作物营养诊断和养分估测方面的研究也越来越多。本文概要地介绍了植物光谱诊断的原理和生理基础,总结了高光谱数据的提取和处理方法;着重评述了光谱技术在作物氮、磷、钾和其它营养元素的营养诊断和养分含量估测的国内外研究状况和进展。文章指出,氮素营养的光谱诊断研究较多,提取出一些敏感波段,建立了光谱指数,并初步得到验证;磷钾营养的光谱诊断研究相对较少,并且结论也不统一;其它营养元素的光谱诊断只是略有涉及,需要进一步研究。本文还就当前作物营养光谱诊断研究的重点以及光谱数据采集和建立养分预测模型中存在的问题进行了分析讨论,并提出了今后的研究方向和应用前景。