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1.
《农业科学学报》2019,18(6):1230-1245
Leaf chlorophyll content(LCC) is an important physiological indicator of the actual health status of individual plants. An accurate estimation of LCC can therefore provide valuable information for precision field management. Red-edge information from hyperspectral data has been widely used to estimate crop LCC. However, after the advent of red-edge bands in satellite imagery, no systematic evaluation of the performance of satellite data has been conducted. Toward this end, we analyze herein the performance of winter wheat LCC retrieval of currant and forthcoming satellites(RapidEye, Sentinel-2 and EnMAP) and their new red-edge bands by using partial least squares regression(PLSR) and a vegetation-indexbased approach. These satellite spectral data were obtained by resampling ground-measured hyperspectral data under various field conditions and according to specific spectral response functions and spectral resolution. The results showed: 1) This study confirmed that RapidEye, Sentinel-2 and EnMAP data are suitable for winter wheat LCC retrieval. For the PLSR approach, Sentinel-2 data provided more accurate estimates of LCC(R2=0.755, 0.844, 0.805 for 2002, 2010, and 2002+2010) than do RapidEye data(R2=0.689, 0.710, 0.707 for 2002, 2010, and 2002+2010) and EnMAP data(R2=0.735, 0.867, 0.771 for 2002, 2010, and 2002+2010). For index-based approaches, the MERIS terrestrial chlorophyll index, which is a vegetation index with two red-edge bands, was the most sensitive and robust index for LCC for both the Sentinel-2 and EnMAP data(R2≥0.628), and the indices(NDRE1, SRRE1 and CIRE1) with a single red-edge band were the most sensitive and robust indices for the RapidEye data(R2≥0.420); 2) According to the analysis of the effect of the wavelength and number of used red-edge spectral bands on LCC retrieval, the short-wavelength red-edge bands(from 699 to 734 nm) provided more accurate predictions when using the PLSR approach, whereas the long-wavelength red-edge bands(740 to 783 nm) gave more accurate predictions when using the vegetation indice(VI) approach. In addition, the prediction accuracy of RapidEye, Sentinel-2 and EnMAP data was improved gradually because of more number of red-edge bands and higher spectral resolution; VI regression models that contain a single or multiple red-edge bands provided more accurate predictions of LCC than those without red-edge bands, but for normalized difference vegetation index(NDVI)-, simple ratio(SR)-and chlorophyll index(CI)-like index, two red-edge bands index didn't significantly improve the predictive accuracy of LCC than those indices with a single red-edge band. Although satellite data with higher spectral resolution and a greater number of red-edge bands marginally improve the accuracy of estimates of crop LCC, the level of this improvement remains insufficient because of higher spectral resolution, which results in a worse signal-to-noise ratio. The results of this study are helpful to accurately monitor LCC of winter wheat in large-area and provide some valuable advice for design of red-edge spectral bands of satellite sensor in future. 相似文献
2.
Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geostatistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied field. The loading coefficients for TC, pH and TN of the first factor were highest and positive. This means that the first factor can be assumed as a synthetic indicator of soil fertility. The overall spatial association between the yield classes and MZs was about 40 %, which reveals that more than 50 % of the yield variation can be attributed to more dynamic factors than soil parameters, such as agro-meteorological conditions, plant diseases and nutrition stresses. Nevertheless, multivariate geostatistics proved to be an effective approach for site-specific management of agricultural fields. 相似文献
3.
Leon Chris T. Shaw David R. Cox Michael S. Abshire Melinda J. Ward Brian Wardlaw MiLton C. Watson Clarence 《Precision Agriculture》2003,4(4):359-384
Remote sensing during the production season can provide visual indications of crop growth along with the geographic locations of those areas. A grid coordinate system was used to sample cotton and soybean fields to determine the relationship between spectral radiance, soil parameters, and cotton and soybean yield. During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield generally ranged from 0.52 to 0.87. These correlation coefficients were obtained using the green–red ratio and a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands. After 102 days after planting (DAP), the ratio vegetation index (RVI), difference vegetation index (DVI), NDVI, and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87. Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from 0.51 to 0.76 for all bands, ratios, and indices examined, with the exception of Band 4 (720nm). The most consistent correlation coefficients for soybean yield were obtained 89–93 DAP, corresponding to peak vegetative production and early pod set, using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the largest amount of variability for cotton growth occurred during the period from first bloom to first open boll, with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on the soybean fields, R
2 values were obtained ranging from 0.43 to 0.82, 15 DAP, and ranged from 0.27 to 0.78, 55–130 DAP. The use of individual bands located in the green, red, and NIR, ratios such as RVI and DVI, indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields performed well during the cotton and soybean production season, though none of these single bands, ratios, or indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within a production season. More research needs to be conducted to determine whether a certain image analysis method will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for each imagery date in order to provide reliable estimates of crop and soil characteristics. 相似文献
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5.
High resolution satellite imagery has the potential to map within-field variation in crop growth and yield. This study examined
SPOT 5 satellite multispectral imagery for estimating grain sorghum yield. A 60 km × 60 km SPOT 5 scene and yield monitor
data from three grain sorghum fields were recorded in south Texas. The satellite scene contained four spectral bands (green,
red, near-infrared and mid-infrared) with a 10-m spatial resolution. Subsets were extracted from the scene that covered the
three fields. Images with pixel sizes of 20 and 30 m were also generated from the individual field images to simulate coarser
resolution satellite imagery. Vegetation indices and principal components were derived from the images at the three spatial
resolutions. Grain yield was related to the vegetation indices, the four bands and the principal components for each field,
and for all the fields combined. The effect of the mid-infrared band on estimates of yield was examined by comparing the regression
results from all four bands with those from the other three bands. Statistical analysis showed that the 10-m, four-band image
and the aggregated 20-m and 30-m images explained 68, 76 and 83%, respectively, of the variation in yield for all the fields
combined. The coefficient of determination between yield and the imagery increased with pixel size because of the smoothing
effect. The inclusion of the mid-infrared band slightly improved the R
2 values. These results indicate that high resolution SPOT 5 multispectral imagery can be a useful data source for determining
within-field yield variation for crop management. 相似文献
6.
Santos Isabel Carolina de Lima dos Santos Alexandre Costa Jerffersoney Garcia Rosa Anderson Melo Zanuncio Antonio José Vinha Zanetti Ronald Oumar Zakariyyaa Zanuncio José Cola 《Precision Agriculture》2021,22(3):647-659
The phytosanitary status of Tectona grandis plantations are monitored conventionally with periodic data collection in the field, which is often costly and has low efficiency. The objective of this research was to develop a methodology to predict the canopy cover of T. grandis plantations using multispectral images of the Sentinel-2 (S2) satellite and photographic imagery. The study was carried out in a T. grandis plantation of seminal origin, in Cáceres, Mato Grosso state, Brazil. Hemispherical photographic (HP) images of the plant canopy were obtained with a digital camera coupled to a “fisheye” lens fixed at 1.3 m high at two dates in the rainy and the dry season. Cloudless and no shadow images of the S2 satellite bands were concurrently obtained with the field images. Multivariate permutative analysis of variance (PERMANOVA) and partial least squares regression (PLSR) were used to predict canopy cover percentage. The accuracy of the predicted T. grandis canopy cover (%) by the PLSR model approach was 77.8?±?0.09%. The results indicate that a PLS model calibrated with 28 HP sample images can accurately estimate the percentage canopy cover for a continuous area of T. grandis plantations and facilitate mapping of canopy heterogeneity to monitor threats of diseases, mortality, fires, pests and other disturbances.
相似文献7.
黑龙江省西部大豆胞囊线虫病发生动态及防治对策 总被引：2，自引：1，他引：1
大豆胞囊线虫病是世界大豆产区危害最重的病害之一,随着大豆重迎茬面积的增加,其发生面积也在逐年扩大,危害程度逐渐加重,使大豆产量显著降低,一般减产10%～20%,重者可达30%～50%,甚至绝产。重迎茬是导致黑龙江省西部地区孢囊线虫发病程度逐年加重的主要原因,通过合理轮作、应用抗线大豆品种、药剂防治、配方施肥等综合措施可有效地控制大豆胞囊线虫的危害。 相似文献
8.
Xun-lan Li Shi-lai Yi Shao-lan He Qiang Lv Rang-jin Xie Yong-qiang Zheng Lie Deng 《Precision Agriculture》2016,17(3):365-374
Vis/NIR spectroscopy was used in combination with pattern recognition methods to identify cultivars of pummelo (Citrus grandis (L.) Osbeck). A total of 240 leaf samples, 60 for each of the four cultivars were analyzed by Vis/NIR spectroscopy. Soft independent modeling of class analogy (SIMCA), partial least square discriminant analysis (PLS-DA), back propagation neural network (BPNN) and least squares support vector machine (LS-SVM) were applied to the spectral data. The first 8 principal components extracted by principal component analysis were used as inputs in building the BPNN and the LS-SVM models. The results showed that a 97.92 % of discrimination accuracy was achieved for both the BPNN and the LS-SVM models when used to identify samples of the validation set, indicating that the performance of the two models was acceptable. Comparatively, the results of the PLS-DA and the SIMCA models were unacceptable because they had lower discrimination accuracy. The overall results demonstrated that use of Vis/NIR spectroscopy coupled with the use of BPNN and LS-SVM could achieve an accurate identification of pummelo cultivars. 相似文献
9.
A comparison of the sensitivity of canopy scale estimators of leaf chlorophyll, obtainable with Sentinel-2 spectral resolution, to soil, canopy and leaf mesophyll factors, was addressed. The analysis of a synthetic dataset, generated simulating the reflectance in the 1–4 LAI range of canopies for the main general classes of leaf inclination (i.e. erectophile, plagiophile, spherical, planophile and extremophile) and for different soil types was used for such a purpose. The synthetic dataset was obtained using the PROSPECT5-4SAIL model in the direct mode with a large variety of soil backgrounds. Additionally an experimental dataset including airborne hyperspectral data gathered during ESA (European Space Agency) campaigns SPARC and AGRISAR, was employed to simulate Sentinel-2 spectral and spatial resolution, to confirm model results. Analysis of the synthetic and experimental datasets indicated that: (i) the CVI (Chlorophyll Vegetation Index), relying only on visible and NIR (Near Infra-Red) bands and obtainable at 10 m spatial resolution, can be used as leaf chlorophyll estimator, at growth stages suitable for nitrogen fertilizer topdressings, for all canopy structures except for erectophile canopies; (ii) better results can be obtained by using different indices for different leaf architectures, with TCI/OSAVI (Triangular Chlorophyll Index/Optimized Soil Adjusted Vegetation Index) performing better for erectophile canopies, whereas MTCI (MERIS Terrestrial Chlorophyll Index) provides better results for planophile canopies, despite the fact that these indices require bands obtainable at 20 m spatial resolution from Sentinel-2 data. 相似文献
10.
Remote sensing imagery taken during a growing season not only provides spatial and temporal information about crop growth conditions, but also is indicative of crop yield. The objective of this study was to evaluate the relationships between yield monitor data and airborne multidate multispectral digital imagery and to identify optimal time periods for image acquisition. Color-infrared (CIR) digital images were acquired from three grain sorghum fields on five different dates during the 1998 growing season. Yield data were also collected from these fields using a yield monitor. The images and the yield data were georeferenced to a common coordinate system. Four vegetation indices (two band ratios and two normalized differences) were derived from the green, red, and near-infrared (NIR) band images. The image data for the three bands and the four vegetation indices were aggregated to generate reduced-resolution images with a cell size equivalent to the combine's effective cutting width. Correlation analyses showed that grain yield was significantly related to the digital image data for each of the three bands and the four vegetation indices. Multiple regression analyses were also performed to relate grain yield to the three bands and to the three bands plus the four indices for each of the five dates. Images taken around peak vegetative development produced the best relationships with yield and explained approximately 63, 82, and 85% of yield variability for fields 1, 2, and 3, respectively. Yield maps generated from the image data using the regression equations agreed well with those from the yield monitor data. These results demonstrated that airborne digital imagery can be a very useful tool for determining yield patterns before harvest for precision agriculture. 相似文献
11.
This paper presents the methodology to design and integrate a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. The system consisted of an LCTF-based SWIR spectral imager, an illumination unit, a frame grabber, and a computer with the data acquisition software. The spectral imager included an InGaAs camera (320 × 256 pixels), an SWIR lens (50 mm, F/1.4), and an LCTF (20 mm aperture). Four multifaceted reflector halogen lamps (35 W, 12 VDC) were used to build the illumination unit. The system was integrated by a LabVIEW program for data acquisition. It can capture hyperspectral or multispectral images of the test object in the spectral range of 900–1700 nm. The system was validated by differentiating sugar from wheat flour, and water from 95% ethanol. The results showed that the system can distinguish these materials in both spectral and spatial domains. This SWIR spectral imaging system could be a potential useful tool for nondestructive inspection of food quality and safety. 相似文献
12.
小麦—玉米(大豆)复种连作区硅钾肥施用效果研究 总被引：3，自引：0，他引：3
对小麦—玉米(大豆)复种连作区地块,在常规施肥的基础上进行增施硅钾肥研究。结果表明:小麦增产21.2%,后茬玉米增产6.1%,产投比3.37∶1;麦后直播大豆,大豆增产17.4%,产投比3.27∶1;参与高产创建,小麦产量9870kg/hm2,玉米产量14252kg/hm2。 相似文献
13.
Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine 
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下载免费PDF全文 Chong LUO Huan-jun LIU Lü-ping LU Zheng-rong LIU Fan-chang KONG Xin-le ZHANG 《农业科学学报》2021,20(7):1944-1957
Rapid and accurate access to large-scale, high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development. Due to the limitations of remote sensing image quality and data processing capabilities, large-scale crop classification is still challenging. This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine(GEE) and Sentinel-1 and Sentinel-2 images. We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018(May to September), combined monthly composite images of reflectance bands, vegetation indices and polarization bands as input features, and then performed crop classification using a Random Forest(RF) classifier. The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area, and the overall accuracy(OA) reached 89.75%. Through experiments, we also found that the classification performance using time-series images is significantly better than that using single-period images. Compared with the use of traditional bands only(i.e., the visible and near-infrared bands), the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly, followed by the addition of red-edge bands. Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%, respectively, compared to using only the Sentinel-2 reflectance bands. The analysis of timeliness revealed that when the July image is available, the increase in the accuracy of crop classification is the highest. When the Sentinel-1 and Sentinel-2 images for May, June, and July are available, an OA greater than 80% can be achieved. The results of this study are applicable to large-scale, high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas. 相似文献
14.
Coffee leaf rust (CLR) caused by the fungus Hemileia vastarix is a devastating disease in almost all coffee producing countries and remote sensing approaches have the potential to monitor the disease. This study evaluated the potential of Sentinel-2 band settings for discriminating CLR infection levels at leaf levels. Field spectra were resampled to the band settings of the Sentinel-2, and evaluated using the random forest (RF) and partial least squares discriminant analysis (PLS-DA) algorithms with and without variable optimization. Using all variables, Sentinel-2 Multispectral Imager (MSI)-derived vegetation indices achieved higher overall accuracy of 76.2% when compared to 69.8% obtained using raw spectral bands. Using the RF out-of-bag (OOB) scores, 4 spectral bands and 7 vegetation indices were identified as important variables in CLR discrimination. Using the PLS-DA Variable Importance in Projection (VIP) score, 3 Sentinel-2 spectral bands (B4, B6 and B5) and 5 vegetation indices were found to be important variables. Use of the identified variables improved the CLR discrimination accuracies to 79.4 and 82.5% for spectral bands and indices respectively when discriminated with the RF. Discrimination accuracy slightly increased through variable optimization for PLS-DA using spectral bands (63.5%) and vegetation indices (71.4%). Overall, this study showed the potential of the Sentinel 2 MSI band settings for CLR discrimination as part of crop condition assessment. Nevertheless further studies are required under field conditions. 相似文献
15.
[目的]研究不同连作方式对大豆生物固氮的影响。[方法]在长期定位试验中设置正茬(大豆-小麦-玉米-大豆)、重茬(小麦-小麦-大豆-大豆)和迎茬(小麦-大豆-小麦-大豆)3种连作方式,研究不同连作方式对大豆根瘤形成和固氮量的影响。[结果]结荚期和鼓粒期正茬大豆体内含氮量较重茬、迎茬多。3种连作方式的大豆根系形成根瘤的数量表现为正茬>迎茬>重茬,迎茬、重茬的大豆根瘤数分别较正茬减少1.3~1.4、13.4~20.5个/株。大豆通过共生固氮作用固定的氮素表现为正茬>迎茬>重茬,重茬、迎茬大豆固氮量分别比正茬降低18.4%、6.5%。大豆产量表现为正茬>迎茬>重茬。[结论]大豆体内含氮量、根瘤数、固氮量和产量都表现为正茬>迎茬>重茬。 相似文献
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Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite 总被引：2，自引：0，他引：2
Y. Cohen V. Alchanatis Y. Zusman Z. Dar D. J. Bonfil A. Karnieli A. Zilberman A. Moulin V. Ostrovsky A. Levi R. Brikman M. Shenker 《Precision Agriculture》2010,11(5):520-537
Relationships between leaf spectral reflectance at 400–900 nm and nitrogen levels in potato petioles and leaves were studied. Five nitrogen (N) fertilizer treatments were applied to build up levels of nitrogen variation in potato fields in Israel in spring 2006 and 2007. Reflectance of leaves was measured in the field over a spectral range of 400–900 nm. The leaves were sampled and analyzed for petiole NO3–N and leaf percentage N (leaf-%N). Prediction models of leaf nitrogen content were developed based on an optical index named transformed chlorophyll absorption reflectance index (TCARI) and on partial least squares regression (PLSR). Prediction models were also developed based on simulated bands of the future VENμS satellite (Vegetation and Environment monitoring on a New Micro-Satellite). Leaf spectral reflectance correlated better with leaf-%N than with petiole NO3–N. The TCARI provided strong correlations with leaf-%N, but only at the tuber-bulking stage. The PLSR analysis resulted in a stronger correlation than TCARI with leaf-%N. An R 2 of 0.95 (p < 0.01) and overall accuracy of 80.5% (Kappa = 74%) were determined for both vegetative and tuber-bulking periods. The simulated VENμS bands gave a similar correlation with leaf-%N to that of the spectrometer spectra. The satellite has significant potential for spatial analysis of nitrogen levels with inexpensive images that cover large areas every 2 days. 相似文献
18.
大豆产量取决于结荚的花数,而花荚脱落是大豆对环境胁迫的适应性反应。在总结了品种选用不合理、播种期不适宜、连年重茬、种植密度过大、高温干旱、营养缺乏等大豆开花不结荚原因的基础上,提出了一系列预防措施,旨在为大豆的高产丰收以及提高经济效益提供一定的理论参考。 相似文献
19.
【目的】微波遥感因具有全天时、全天候数据获取的特点,在多云雨的中国南方水稻识别研究中表现出巨大潜力。本研究通过对比Sentinel-1SAR遥感数据和Sentinel-2光学遥感数据用于水稻遥感制图的效果,分析光学和SAR遥感数据对于单双季稻识别结果的一致性,并探索水稻识别的最优SAR影像特征。【方法】本研究使用Sentinel-1/2卫星数据,基于面向对象的随机森林分类算法和Google Earth Engine平台,提取洞庭湖平原4个典型水稻种植区的单双季稻空间分布。通过比较9种不同传感器和特征组合场景的分类精度和分类结果统计指标,并计算NDVI和SAR特征时序（VH、VV、VH/VV）的R2和DTW距离,分析识别单双季稻的最优SAR特征,评估光学和SAR遥感数据对于单双季稻识别结果的一致性。【结果】VH、VV和VH/VV时序识别单双季的总体精度分别为90.42%、82.08%和88.33%,而联合VH和VH/VV时序识别单双季稻的总体精度可达91.67%。VH（VH/VV、VV）时序与单双季稻NDVI时序的R2和DTW距离分别为0.870（0.915、0.986）、4.715（1.896、5.506）（单季稻）和0.597（0.783、0.673）、2.396（1.839、3.441）（双季稻）。较高的R2和较低的DTW距离说明单双季稻的VH/VV时序与NDVI时序相关度更高,可以较好地反映单双季稻的生长周期规律。同时,VH可以较好地反映单双季稻移栽期的淹水特征。基于光学数据和SAR数据在6个时间窗口的特征（S-2：NDVI、EVI、LSWI;S-1：VH、VH/VV）识别单双季稻的总体精度分别为91.25%和90.00%,识别结果面积相关性可达95.70%。【结论】SAR遥感数据与光学遥感数据水稻识别结果一致性较高。应用Sentinel-1在多云雨区识别单双季稻具有巨大潜力,VH和VH/VV后向散射系数时序是识别水稻的优质特征。研究结果为多云多雨区使用SAR数据进行特征优选以高精度识别单双季稻提供了重要技术支撑。 相似文献
20.
为探索应用近红外光谱技术检测玉米单籽粒蛋白质含量,本研究采用JDSU近红外光谱检测仪采集了205份不同基因型玉米材料的单籽粒光谱值,用常规化学法测定玉米单籽粒蛋白质含量化学值,以117个样本为建模集,拟合了玉米单籽粒近红外光谱仪扫描得到的光谱图与玉米单籽粒蛋白质含量化学值之间的相互关系,用88个样本作预测集,比较了偏最小二乘回归法(PLSR)和支持向量机回归法(SVR)2种预测模型的效果。结果表明,玉米单籽粒种子的蛋白质含量在样本中变异范围为3.48%~18.15%,平均值为10.17%。偏最小二乘回归法(PLSR)和支持向量机回归法(SVR)所建的模型预测效果基本相同,其决定系数(R2)分别为0.99和0.99,校正标准差(SEC)分别为0.32和0.32,预测标准差(SEP)分别为0.46和0.46,相对预测标准差(RSEP)分别为4.61和4.60,RPD分别为6.106和6.111。上述参数表明PLSR和SVR所建立的模型预测效果都比较好,预测值基本接近参比值,便携式JDSU近红外光谱检测仪可以应用于定量分析玉米单籽粒蛋白质含量。 相似文献