A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery          下载免费PDF全文

《农业科学学报》2023,22(7):2248-2270

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R²=0.67–0.89, RMSE=13.63–23.71 mg g^–1, MAE=10.75–17.59 mg g^–1) performed better than the direct inversion models (R²=0.61–0.80, RMSE=18.01–25.12 mg g^–1, MAE=12.96–18.88 mg g^–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R²=0.89, RMSE=13.63 mg g^–1, MAE=10.75 mg g^–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.  相似文献   

Leaf chlorophyll content retrieval of wheat by simulated RapidEye,Sentinel-2 and EnMAP data     

《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   

Mapping wheat nitrogen uptake from RapidEye vegetation indices     

Troy S. Magney  Jan U. H. Eitel  Lee A. Vierling 《Precision Agriculture》2017,18(4):429-451

Mapping wheat nitrogen (N) uptake at 5 m spatial resolution could provide growers with new insights regarding nitrogen-use efficiency at the field scale. This study explored the use of spectral information from high resolution (5 × 5 m) RapidEye satellite data at peak leaf area index (LAI) to estimate end-of-season cumulative N uptake of wheat (Triticum spp.) in a heterogeneous, rainfed system. The primary objectives were to evaluate the usefulness of simple, widely used vegetation indices (VIs) from RapidEye as a tool to map crop N uptake over three growing seasons, farms and growing conditions, and to examine the usefulness of remotely sensed N uptake maps for precision agriculture applications. Data on harvested wheat N was collected at twelve plots over three seasons at four farms in the Palouse region of Northern Idaho and Eastern Washington. Seventeen commonly used spectral VIs were computed for images collected during ‘peak greenness’ (maximum LAI) to determine which VIs would be most appropriate for estimating wheat N uptake at harvest. The normalized difference red-edge index was the top performing VI, explaining 81 % of the variance in wheat N uptake with a regression slope of 1.06 and RMSE of 15.94 kg/ha. Model performance was strong across all farms over all three seasons regardless of crop variety, allowing the creation of high accuracy wheat N uptake maps. In conclusion, for this particular agro-ecosystem, mid-season VIs that incorporate the use of the NIR and red-edge bands are generally better predictors of end-of-season crop N uptake than VIs that do not include these bands, thereby further enabling their use in precision agriculture applications.  相似文献   

Vineyard yield estimation by combining remote sensing,computer vision and artificial neural network techniques     

Ballesteros  Rocío  Intrigliolo  Diego S.  Ortega  José F.  Ramírez-Cuesta  Juan M.  Buesa  Ignacio  Moreno  Miguel A. 《Precision Agriculture》2020,21(6):1242-1262

In viticulture, it is critical to predict productivity levels of the different vineyard zones to undertake appropriate cropping practices. To overcome this challenge, the final yield was predicted by combining vegetation indices (VIs) to sense the health status of the crop and by computer vision to obtain the vegetated fraction cover (F_c) as a measure of plant vigour. Multispectral imagery obtained from an unmanned aerial vehicle (UAV) is used to obtain VIs and F_c, which are used together with artificial neural networks (ANN) to model the relationship between VIs, F_c and yield. The proposed methodology was applied in a vineyard, where different irrigation and fertilisation doses were applied. The results showed that using computer vision techniques to differentiate between canopy and soil is necessary in precision viticulture to obtain accurate results. In addition, the combination of VIs (reflectance approach) and F_c (geometric approach) to predict vineyard yield results in higher accuracy (root mean square error (RMSE)?=?0.9 kg vine^?1 and relative error (RE)?=?21.8% for the image when close to harvest) compared to the simple use of VIs (RMSE?=?1.2 kg vine^?1 and RE?=?28.7%). The implementation of machine learning techniques resulted in much more accurate results than linear models (RMSE?=?0.5 kg vine^?1 and RE?=?12.1%). More precise yield predictions were obtained when images were taken close to the harvest date, although promising results were obtained at earlier stages. Given the perennial nature of grapevines and the multiple environmental and endogenous factors determining yield, seasonal calibration for yield prediction is required.

  相似文献   

Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat     

《农业科学学报》2017,(2)

Using simultaneously collected remote sensing data and field measurements,this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1(GF-1) wide field of view(WFV) camera,environment and disaster monitoring and forecasting satellite(HJ-1) charge coupled device(CCD),and Landsat-8 operational land imager(OLI) data for estimating the leaf area index(LAI) of winter wheat via reflectance and vegetation indices(VIs). The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration,spectral response functions,and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations:(1) The correlation between reflectance from different sensors is relative good,with the adjusted coefficients of determination(R2) between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors' images can all be used for cross calibration of the reflectance and VIs.(2) The four VIs from the three sensors are all demonstrated to be highly correlated with LAI(R2 between 0.703 and 0.849). The linear models associated with the 2-band enhanced vegetation index(EVI2),which feature the highest R2(higher than 0.746) and the lowest root mean square errors(RMSE)(less than 0.21),were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest,with the relative errors(RE) of 2.18% and an RMSE of 0.13,while the HJ-1 was the lowest,with the RE of 2.43% and the RMSE of 0.15.(3) The inversion errors in the different sensors' LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%,and the RMSE of 0.22,whereas that of the HJ-1 is the lowest with the RE of 4.95%,and the RMSE of 0.26.(4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored,but the effects of spatial resolution are significant and must be taken into consideration in practical applications.  相似文献   

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images          下载免费PDF全文

《农业科学学报》2023,22(8):2536-2552

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner. The nitrogen nutrition index (NNI) can quantitatively describe the nitrogen status of crops. Nevertheless, the NNI diagnosis for cotton with unmanned aerial vehicle (UAV) multispectral images has not been evaluated yet. This study aimed to evaluate the performance of three machine learning models, i.e., support vector machine (SVM), back propagation neural network (BPNN), and extreme gradient boosting (XGB) for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images. The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI. The XGB model performed the best among the three models in predicting nitrogen weight. The prediction accuracy of nitrogen weight at the upper half-leaf level (R²=0.89, RMSE=0.68 g m^–2, RE=14.62% for calibration and R²=0.83, RMSE=1.08 g m^–2, RE=19.71% for validation) was much better than that at the all-leaf level (R²=0.73, RMSE=2.20 g m^–2, RE=26.70% for calibration and R²=0.70, RMSE=2.48 g m^–2, RE=31.49% for validation) and at the plant level (R²=0.66, RMSE=4.46 g m^–2, RE=30.96% for calibration and R²=0.63, RMSE=3.69 g m^–2, RE=24.81% for validation). Similarly, the XGB model (R²=0.65, RMSE=0.09, RE=8.59% for calibration and R²=0.63, RMSE=0.09, RE=8.87% for validation) also outperformed the SVM model (R²=0.62, RMSE=0.10, RE=7.92% for calibration and R²=0.60, RMSE=0.09, RE=8.03% for validation) and BPNN model (R²=0.64, RMSE=0.09, RE=9.24% for calibration and R²=0.62, RMSE=0.09, RE=8.38% for validation) in predicting NNI. The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields, which can help farmers implement precise cotton nitrogen management in a timely and accurate manner.  相似文献   

Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: performance and influencing factors     

Zhang  Jian  Wang  Chufeng  Yang  Chenghai  Jiang  Zhao  Zhou  Guangsheng  Wang  Bo  Shi  Yeyin  Zhang  Dongyan  You  Liangzhi  Xie  Jing 《Precision Agriculture》2020,21(5):1092-1120

The objective of this study was to evaluate the crop monitoring performance of a consumer-grade camera with non-modified and modified spectral ranges which are commonly used in low-altitude unmanned aerial vehicle (UAV) platforms. The camera was fixed sequentially with seven types of filters for collecting visible images and near-infrared (NIR) images with different center band locations and bandwidths. Meanwhile, field-based hyperspectral data and normalized difference vegetation index (NDVI) measured by a GreenSeeker handheld crop sensor (GS-NDVI) were collected to examine the accuracy of rapeseed growth monitoring in terms of vegetation indices (VIs) derived from UAV images. Results showed that the UAV-based RGB-VIs and optimal NIR-VIs had similar accuracy for predicting GS-NDVI. Moreover, similar results were achieved based on the hyperspectral data, indicating the importance of spectral characteristics for GS-NDVI estimation. However, the UAV-based results also indicated that the performance of VIs derived from the band combinations containing longer NIR center wavelengths and narrower bandwidths was obviously poorer than that of the RGB-VIs. The image quality of the NIR band was also found to be inferior to the visible band based on quantitative analysis, which also revealed that image quality had great impact on UAV-based results. Image quality was then related to the effects of camera exposure, spectral sensitivity, soil background and dark areas. The results from this study provide useful information for camera modifications by selecting appropriate filters that not only are sensitive to crop growth, but also ensure image quality.

  相似文献   

Leaf area index based nitrogen diagnosis in irrigated lowland rice     

Xiao-jun LIU  Qiang CAO  Zhao-feng YUAN  Xia LIU  Xiao-ling WANG  Yong-chao TIAN  Wei-xing CAO  Yan ZHU 《农业科学学报》2018,17(1):111-121

Leaf area index(LAI)is used for crop growth monitoring in agronomic research,and is promising to diagnose the nitrogen(N)status of crops.This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice.Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014.Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice.LAI was determined by LI-3000,and estimated indirectly by LAI-2000 during vegetative growth period.Group and individual plant characters(e.g.,tiller number(TN)and plant height(H))were investigated simultaneously.Two N indicators of plant N accumulation(NA)and N nutrition index(NNI)were measured as well.A calibration equation(LAI=1.7787LAI_(2000)–0.8816,R~2=0.870~(**))was developed for LAI-2000.The linear regression analysis showed a significant relationship between NA and actual LAI(R~2=0.863~(**)).For the NNI,the relative LAI(R~2=0.808~(**))was a relatively unbiased variable in the regression than the LAI(R~2=0.33~(**)).The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice(NA=29.778LAI–5.9397;NNI=0.7705RLAI+0.2764).Finally,a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H(LAI=–0.3375(TH×H×0.01)~2+3.665(TH×H×0.01)–1.8249,R~2=0.875~(**)).With these models,the N status of rice can be diagnosed conveniently in the field.  相似文献   

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model          下载免费PDF全文

BAI Tie-cheng  WANG Tao  ZHANG Nan-nan  CHEN You-qi  Benoit MERCATORIS 《农业科学学报》2020,19(3):721-734

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops, thereby performing yield prediction, but not for fruit tree species such as jujube tree(Zizyphus jujuba). The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter. The model was established using data collected from dedicated field experiments performed in 2016–2018. Simulated growth dynamics of dry weights of leaves, stems, fruits, total biomass and leaf area index(LAI) agreed well with measured values, showing root mean square error(RMSE) values of 0.143, 0.333, 0.366, 0.624 t ha~(–1) and 0.19, and R~2 values of 0.947, 0.976, 0.985, 0.986 and 0.95, respectively. Simulated phenological development stages for emergence, anthesis and maturity were 2, 3 and 3 days earlier than the observed values, respectively. In addition, in order to predict the yields of trees with different ages, the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI), which was calculated as averaged, fitted and optimized values of trees with the same age. The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI. The modelling performance was significantly improved when it considered TDWI integrated with tree age, showing good global(R~2≥0.856, RMSE≤0.68 t ha~(–1)) and local accuracies(mean R~2≥0.43, RMSE≤0.70 t ha~(–1)). Furthermore, the optimized TDWI exhibited the highest precision, with globally validated R~2 of 0.891 and RMSE of 0.591 t ha~(–1), and local mean R~2 of 0.57 and RMSE of 0.66 t ha~(–1), respectively. The proposed model was not only verified with the confidence to accurately predict yields of jujube, but it can also provide a fundamental strategy for simulating the growth of other fruit trees.  相似文献   

基于随机森林算法的冬小麦叶面积指数遥感反演研究   总被引：10，自引：1，他引：9  

张春兰  杨贵军  李贺丽  汤伏全  刘畅  张丽妍 《中国农业科学》2018,51(5):855-867

【目的】通过利用随机森林算法（random forest,RF）反演冬小麦叶面积指数（leaf area index, LAI）,及时、准确地监测冬小麦长势状况,为作物田间管理和产量估测等提供科学依据。【方法】本研究依据冬小麦拔节期、挑旗期、开花期及灌浆期地面观测数据,将相关系数分析（correlation coefficient,r）和袋外数据（out-of-bag data,OOB）重要性分析与随机森林算法（random forest,RF）相结合,在优选光谱指数和确定最佳自变量个数的基础上,构建了两种冬小麦LAI反演模型|r|-RF和OOB-RF,并利用独立数据集对两种模型进行验证;然后,将所建LAI反演模型用于无人机高光谱影像,进一步检验所建模型对无人机低空遥感平台的适用性和可靠性。【结果】|r|-RF和OOB-RF反演模型分别采用相关性前5强、重要性前2强的光谱指数作为输入因子时精度最优,验证决定系数（R²）分别为0.805、0.899,均方根误差（RMSE）分别为0.431、0.307,表明这两个模型均能对作物LAI进行精确反演,其中OOB-RF模型的反演效果更好。利用无人机高光谱影像数据结合OOB-RF估算模型反演得到冬小麦LAI与地面实测值的拟合方程的决定系数R²为0.761,RMSE为0.320,数值范围（1.02-6.41）与地面实测（1.29-6.81）亦比较吻合。【结论】本文基于地面数据构建的OOB-RF模型不仅具有较高的反演精度,而且适用性强,可用于无人机高光谱遥感平台提取高精度的冬小麦LAI信息。  相似文献   

Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in planophile crop canopies     

M.?VinciniEmail author  E.?Frazzi 《Precision Agriculture》2011,12(3):334-344

A comparison of the sensitivity of several broad- and narrow-band vegetation indices (VIs) to leaf chlorophyll content in planophile crop canopies is addressed by the analysis of a large synthetic dataset. Broad-band indices included classical slope-based VIs (i.e. NDVI—normalized difference VI and SR—simple ratio) and some indices incorporating green reflectance (i.e. Green NDVI, NIR/green ratio and the newly proposed CVI—chlorophyll vegetation index), whereas narrow-band indices included those specifically proposed to estimate leaf chlorophyll at the canopy scale (i.e. MCARI—modified chlorophyll absorption reflectance index, TCARI—transformed CARI, TCARI/OSAVI ratio—TCARI/optimized soil adjusted VI and REIP—red edge inflection position). Synthetic data were obtained from the coupled PROSPECT + SAILH leaf and canopy reflectance models in the direct mode. In addition to traditional regression-based statistics (coefficient of determination and root mean square error, RMSE), changes in sensitivity of a VI over the range of chlorophyll content were analyzed using a sensitivity function. The broad-band chlorophyll vegetation index outperformed the other VIs considered as a leaf chlorophyll estimator at the canopy scale, with the exception of the TCARI/OSAVI ratio for some soil conditions.  相似文献   

Unmanned aerial vehicle to evaluate frost damage in coffee plants     

Marin  Diego Bedin  Ferraz  Gabriel Araújo e Silva  Schwerz  Felipe  Barata  Rafael Alexandre Pena  de Oliveira Faria  Rafael  Dias  Jessica Ellen Lima 《Precision Agriculture》2021,22(6):1845-1860

Damage caused by frost on coffee plants can impact significantly in the reduction of crop quality and productivity. Remote sensing can be used to evaluate the damage caused by frost, providing precise and timely agricultural information to producers, assisting in decision making, and consequently minimizing production losses. In this context, this study aimed to evaluate the potential use of multispectral images obtained by unmanned aerial vehicle (UAV) to analyze and identify damage caused by frost in coffee plants in different climatic favorability zones. Visual evaluations of frost damage and chlorophyll content quantification were carried out in a commercial coffee plantation in Southern Minas Gerais, Brazil. The images were obtained from a multispectral camera coupled to a UAV with rotating wings. The results obtained demonstrated that the vegetation indices had a strong relationship and high accuracy with the frost damage. Among the indices studied the normalized difference vegetation index (NDVI) was the one that had better performances (r?=?? 0.89, R²?=?0.79, MAE?=?10.87 e RMSE?=?14.35). In a simple way, this study demonstrated that multispectral images, obtained from UAV, can provide a fast, continuous, and accessible method to identify and evaluate frost damage in coffee plants. This information is essential for the coffee producer for decision-making and adequate crop management.

  相似文献   

Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages     

Fei Li  Yuxin Miao  Simon D. Hennig  Martin L. Gnyp  Xinping Chen  Liangliang Jia  Georg Bareth 《Precision Agriculture》2010,11(4):335-357

Many hyperspectral vegetation indices (VIs) have been developed to estimate crop nitrogen (N) status at leaf and canopy levels. However, most of these indices have not been evaluated for estimating plant N concentration (PNC) of winter wheat (Triticum aestivum L.) at different growth stages using a common on-farm dataset. The objective of this study was to evaluate published VIs for estimating PNC of winter wheat in the North China Plain for different growth stages and years using data from both N experiments and farmers’ fields, and to identify alternative promising hyperspectral VIs through a thorough evaluation of all possible two band combinations in the range of 350–1075 nm. Three field experiments involving different winter wheat cultivars and 4–6 N rates were conducted with cooperative farmers from 2005 to 2007 in Shandong Province, China. Data from 69 farmers’ fields were also collected to evaluate further the published and newly identified hyperspectral VIs. The results indicated that best performing published and newly identified VIs could explain 51% (R₇₀₀/R₆₇₀) and 57% (R₄₁₈/R₄₀₅), respectively, of the variation in PNC at later growth stages (Feekes 8–10), but only 22% (modified chlorophyll absorption ratio index, MCARI) and 43% (R₇₆₃/R₇₆₁), respectively, at the early stages (Feekes 4–7). Red edge and near infrared (NIR) bands were more effective for PNC estimation at Feekes 4–7, but visible bands, especially ultraviolet, violet and blue bands, were more sensitive at Feekes 8–10. Across site-years, cultivars and growth stages, the combination of R₃₇₀ and R₄₀₀ as either simple ratio or a normalized difference index performed most consistently in both experimental (R ² = 0.58) and farmers’ fields (R ² = 0.51). We conclude that growth stage has a significant influence on the performance of different vegetation indices and the selection of sensitive wavelengths for PNC estimation, and new approaches need to be developed for monitoring N status at early growth stages.  相似文献   

An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat          下载免费PDF全文

ZHAO Yu  WANG Jian-wen  CHEN Li-ping  FU Yuan-yuan  ZHU Hong-chun  FENG Hai-kuan  XU Xin-gang  LI Zhen-hai 《农业科学学报》2021,20(9):2535-2551

The nitrogen nutrition index(NNI) is a reliable indicator for diagnosing crop nitrogen(N) status. However, there is currently no specific vegetation index for the NNI inversion across multiple growth periods. To overcome the limitations of the traditional direct NNI inversion method(NNI_(T1)) of the vegetation index and traditional indirect NNI inversion method(NNI_(T2)) by inverting intermediate variables including the aboveground dry biomass(AGB) and plant N concentration(PNC), this study proposed a new NNI remote sensing index(NNI_(RS)). A remote-sensing-based critical N dilution curve(Nc_(_RS)) was set up directly from two vegetation indices and then used to calculate NNI_(RS). Field data including AGB, PNC, and canopy hyperspectral data were collected over four growing seasons(2012–2013(Exp.1), 2013–2014(Exp. 2), 2014–2015(Exp. 3), 2015–2016(Exp. 4)) in Beijing, China. All experimental datasets were cross-validated to each of the NNI models(NNI_(T1), NNI_(T2) and NNI_(RS)). The results showed that:(1) the NNI_(RS) models were represented by the standardized leaf area index determining index(sLAIDI) and the red-edge chlorophyll index(CI_(red edge)) in the form of NNI_(RS)=CI_(red edge)/(a×sLAIDI~b), where "a" equals 2.06, 2.10, 2.08 and 2.02 and "b" equals 0.66, 0.73, 0.67 and 0.62 when the modeling set data came from Exp.1/2/4, Exp.1/2/3, Exp.1/3/4, and Exp.2/3/4, respectively;(2) the NNI_(RS) models achieved better performance than the other two NNI revised methods, and the ranges of R2 and RMSE were 0.50–0.82 and 0.12–0.14, respectively;(3) when the remaining data were used for verification, the NNI_(RS) models also showed good stability, with RMSE values of 0.09, 0.18, 0.13 and 0.10, respectively. Therefore, it is concluded that the NNI_(RS) method is promising for the remote assessment of crop N status.  相似文献   

Developing a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) and its validation over the Northeast China Plain          下载免费PDF全文

Sha ZHANG  Yun BAI  Jia-hua ZHANG  Shahzad ALI 《农业科学学报》2021,20(2):408-423

Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing(RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies(DMCSs) over the Northeast China Plain(NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics(2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage(DVS)-based grain-filling algorithm and RS phenology information and leaf area index(LAI), had higher correlation(R, 0.61) and smaller root mean standard error(RMSE, 1.33 t ha~(–1)) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index(HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.  相似文献   

Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques     

Abdulridha  Jaafar  Ampatzidis  Yiannis  Kakarla  Sri Charan  Roberts  Pamela 《Precision Agriculture》2020,21(5):955-978

Early and accurate diagnosis is a critical first step in mitigating losses caused by plant diseases. An incorrect diagnosis can lead to improper management decisions, such as selection of the wrong chemical application that could potentially result in further reduced crop health and yield. In tomato, initial disease symptoms may be similar even if caused by different pathogens, for example early lesions of target spot (TS) caused by the fungus Corynespora cassicola and bacterial spot (BS) caused by Xanthomonas perforans. In this study, hyperspectral imaging (380–1020 nm) was utilized in laboratory and field (collected by an unmanned aerial vehicle; UAV) settings to detect both diseases. Tomato leaves were classified into four categories: healthy, asymptomatic, early and late disease development stages. Thirty-five spectral vegetation indices (VIs) were calculated to select an optimum set of indices for disease detection and identification. Two classification methods were utilized: (i) multilayer perceptron neural network (MLP), and (ii) stepwise discriminant analysis (STDA). Best wavebands selection was considered in blue (408–420 nm), red (630–650 nm) and red edge (730–750 nm). The most significant VIs that could distinguish between healthy leaves and diseased leaves were the photochemical reflectance index (PRI) for both diseases, the normalized difference vegetation index (NDVI850) for BS in all stages, and the triangular vegetation index (TVI), NDVI850 and chlorophyll index green (Chl green) for TS asymptomatic, TS early and TS late disease stage respectively. The MLP classification method had an accuracy of 99%, for both BS and TS, under field (UAV-based) and laboratory conditions.

  相似文献   

Using hyperspectral remote sensing techniques to monitor nitrogen,phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)     

G. R. Mahajan  R. N. Sahoo  R. N. Pandey  V. K. Gupta  Dinesh Kumar 《Precision Agriculture》2014,15(5):499-522

In situ, non-destructive and real time mineral nutrient stress monitoring is an important aspect of precision farming for rational use of fertilizers. Studies have demonstrated the ability of remote sensing to monitor nitrogen (N) in many crops, phosphorus (P) and potassium (K) in very few crops and none so far to monitor sulphur (S). Specially designed (1) fertility gradient experiment and (2) test crop experiments were used to check the possibility of mineral N–P–S–K stress detection using airborne hyperspectral remote sensing. Leaf and canopy hyperspectral reflectance data and nutrient status at booting stage of the wheat crop were recorded. N–P–S–K sensitive wavelengths were identified using linear correlation analysis. Eight traditional vegetation indices (VIs) and three proposed (one for P and two for S) were evaluated for plant N–P–S–K predictability. A proposed VI (P_1080_1460) predicted P content with high and significant accuracy (correlation coefficient (r) 0.42 and root means square error (RMSE) 0.180 g m^?2). Performance of the proposed S VI (S_660_1080) for S concentration and content retrieval was similar whereas prediction accuracies were higher than traditional VIs. Prediction accuracy of linear regressive models improved when biomass-based nutrient contents were considered rather than concentrations. Reflectance in the SWIR region was found to monitor N–P–S–K status in plants in combination with reflectance at either visible (VIS) or near infrared (NIR) region. Newly developed and validated spectral algorithms specific to N, P, S and K can further be used for monitoring in a wheat crop in order to undertake site-specific management.  相似文献   

Application of day and night digital photographs for estimating maize biophysical characteristics     

Toshihiro Sakamoto  Anatoly A. Gitelson  Brian D. Wardlow  Timothy J. Arkebauer  Shashi B. Verma  Andrew E. Suyker  Michio Shibayama 《Precision Agriculture》2012,13(3):285-301

In this study, an inexpensive camera-observation system called the Crop Phenology Recording System (CPRS), which consists of a standard digital color camera (RGB cam) and a modified near-infrared (NIR) digital camera (NIR cam), was applied to estimate green leaf area index (LAI), total LAI, green leaf biomass and total dry biomass of stalks and leaves of maize. The CPRS was installed for the 2009 growing season over a rainfed maize field at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, NE, USA. The vegetation indices called Visible Atmospherically Resistant Index (VARI) and two green–red–blue (2g–r–b) were calculated from day-time RGB images taken by the standard commercially-available camera. The other vegetation index called Night-time Relative Brightness Index in NIR (NRBI_NIR) was calculated from night-time flash NIR images taken by the modified digital camera on which a NIR band-pass filter was attached. Sampling inspections were conducted to measure bio-physical parameters of maize in the same experimental field. The vegetation indices were compared with the biophysical parameters for a whole growing season. The VARI was found to accurately estimate green LAI (R² = 0.99) and green leaf biomass (R² = 0.98), as well as track seasonal changes in maize green vegetation fraction. The 2g–r–b was able to accurately estimate total LAI (R² = 0.97). The NRBI_NIR showed the highest accuracy in estimation of the total dry biomass weight of the stalks and leaves (R² = 0.99). The results show that the camera-observation system has potential for the remote assessment of maize biophysical parameters at low cost.  相似文献   

不同光谱植被指数反演冬小麦叶氮含量的敏感性研究   总被引：6，自引：0，他引：6  

张潇元  张立福  张霞  王树东  田静国  翟涌光 《中国农业科学》2017,50(3):474-485

【目的】氮素是作物生长发育过程中最重要的营养元素之一,研究叶氮含量反演的有效光谱指标设置,为应用高光谱植被指数反演作物叶氮含量,以及作物的实时监测与精确诊断提供重要依据。【方法】以冬小麦为例,选取涵盖冬小麦全生育期不同覆盖程度225组冠层光谱与叶氮含量数据,通过遥感方法建立模型,模拟了不同光谱指标,即中心波长、信噪比和波段宽度对定量模型的影响,通过模型精度评价指标决定系数(coefficient of determination,R~2)、根均方差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)、平均相对误差(mean relative error,MRE)和显著性检验水平(P0.01)确定最优模型及最佳指标,分析光谱指标对叶氮含量定量模型反演的敏感性和有效性。【结果】反演冬小麦叶氮含量的最佳植被指数为MTCI_B,与实测叶氮含量的相关性最好(R~2=0.7674,RMSE=0.5511%,MAE=0.4625%,MRE=11.11个百分点,且P0.01),对应的最佳指标为中心波长420 nm、508 nm和405 nm,波段宽度1 nm,信噪比大于70 DB;高覆盖状况反演的最优指数为RVIinf_r(R~2=0.6739,RMSE=0.2964%,MAE=0.2851%,MRE=6.44个百分点,且P0.01),最优中心波长为826 nm和760 nm;低覆盖状况反演的最优指数为MTCI(R~2=0.8252,RMSE=0.4032%,MAE=0.4408%,MRE=12.22个百分点,且P0.01),最优中心波长为750 nm、693 nm和680 nm;应用最适于高低覆盖的植被指数RVIinf_r和MTCI构建的联合反演模型(R~2=0.9286,RMSE=0.3416%,MAE=0.2988%,MRE=7.16个百分点,且P0.01),明显优于最佳单一指数MTCI_B;模拟Hyperion和HJ1A-HSI传感器数据,联合反演模型精度(R~2为0.92—0.93,RMSE在0.37%—0.39%,MAE为0.285%左右,MRE约为7.00个百分点)明显优于单一植被指数反演精度(R~2为0.79—0.81,RMSE为0.63%—0.66%,MAE为0.455%左右,MRE约为10.90个百分点)。【结论】利用高光谱植被指数可有效实现作物叶氮含量反演,作物叶氮含量定量反演对不同光谱指标—中心波长、信噪比和波段宽度,具有较强敏感性。应用多指数联合反演模型,可显著提高反演精度,并且联合反演模型在不同高光谱传感器下有一定普适性。  相似文献   

Determination of differences in crop injury from aerial application of glyphosate using vegetation indices     

B.V. Ortiz  S.J. ThomsonY. Huang  K.N. ReddyW. Ding 《Computers and Electronics in Agriculture》2011,77(2):204-213

Crop injury caused by off-target drift of herbicide can seriously reduce growth and yield and is of great concern to farmers and aerial applicators. Farmers can benefit from identifying an indirect method for assessing the level of crop injury. This study evaluates the combined use of statistical methods and vegetation indices (VIs) derived from multispectral images to assess the level of crop injury. An experiment was conducted in 2009 to determine glyphosate injury differences among the cotton, corn, and soybean crops. The crops were planted in eight rows spaced 102 cm apart and 80 m long with four replications. Seven VIs were calculated from multispectral images collected at 7 and 21 days after the glyphosate application (DAA). At each image collection date, visual injury estimates were assessed and data were collected for plant height, chlorophyll content, and shoot dry weight. From the seven VIs evaluated as surrogate for glyphosate injury identification using a canonical correlation analysis (CCA), the Chlorophyll Vegetation Index (CVI) showed the highest correlation with field-measured plant injury data. CVI image values were subtracted from the CVI average values of the non-injured area to generate CVI residual images (CVI_res). Frequency distribution histograms of CVI_res image values were calculated to assess the level of injury between crops. These data suggested that injury increased from 7DAA to 21DAA with corn exhibiting higher severity of injury than cotton or soybean, while only moderate injury was observed for cotton. The techniques evaluated in this study are promising for estimating the level of glyphosate herbicide drift, which can be used to make appropriate management decisions considering crop proximity.  相似文献