Coffee ripeness monitoring using a UAV-mounted low-cost multispectral camera

Coffee beverage quality is highly correlated with the degree of fruit ripeness. In this sense, monitoring fruit ripeness is of utmost importance for harvest planning and, especially for obtaining high-quality beverages. Currently, this process is carried out through manual counts of unripe fruits, which is laborious and limited to a few plants within the field. This study aimed at evaluating the potential of a low-cost multispectral camera for coffee ripeness monitoring in the Zona da Mata region of Minas Gerais State, Brazil. For that, five fields of Arabica coffee with distinct characteristics were evaluated. During the coffee ripeness period, four flights were carried using a Phantom 4 Pro quadcopter equipped with a Mapir Survey 3W camera for imagery acquisition. After that, nine vegetation indices (VIs) were obtained. For the same dates, the percentage of unripe fruits was obtained using an irregular grid in all fields. The data was split into two ripeness classes: suitable for harvest (R) with?<?30% of unripe fruits; and not suitable for harvest (U), with?>?30% of unripe fruits. Then, a principal component analysis was used to infer the importance of the VIs to discriminate plants with unripe fruits from those with ripe fruits. The first two principal components explained?>?75% of the variance in the datasets from all coffee fields. The VIs were able to discriminate the ripeness classes (U and R) in most fields; however, their performance was directly influenced by the crop yield and canopy volume.

     

Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves

Ten, widely-used vegetation indices (VIs), based on mathematical combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate leaves of 1 month old wheat plants infected with yellow (stripe), leaf and stem rust. Narrow band indices representing changes in non-chlorophyll pigment concentration and the ratio of non-chlorophyll to chlorophyll pigments proved more reliable in discriminating rust infected leaves from healthy plant tissue. Yellow rust produced the strongest response in all the calculated indices when compared to healthy leaves. No single index was capable of discriminating all three rust species from each other. However the sequential application of the Anthocyanin Reflectance Index to separate healthy, yellow and mixed stem rust/leaf rust classes followed by the Transformed Chlorophyll Absorption and Reflectance Index to separate leaf and stem rust classes would provide for the required species discrimination under laboratory conditions and thus could form the basis of rust species discrimination in wheat under field conditions.     

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

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.     

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index,SPAD-based chlorophyll and a random forest approach

Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R²?=?0.977–0.980, RMSE?=?0.119–0.167) and SPAD-based chlorophyll (R²?=?0.983–0.986, RMSE?=?2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments.

     

植物叶片面积测量系统的设计及应用

介绍了用CCD（Charge Coupled Device）测量植物叶片面积的方法，设计了测量系统的多种组成方案。该系统通过标准的图像接口，实现了硬件部分与软件部分相互无关，硬件可以采用扫描仪、数码相机、视频图像采集卡加视频摄像头等应用CCD的数字化图像采集设备。软件采用交到方式实现图像分割、系统标定和最终测量，试验表明，该系统具有测量精度高、测量范围大、使用方便等特点，使用数码相机时可以实现非破坏性测量，在农业、林业等方面具有广泛的应用前景。     

Unmanned aerial vehicle to evaluate frost damage in coffee plants

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.

     

Hyperspectral image analysis for water stress detection of apple trees

Plant stress significantly reduces plant productivity. Automated on-the-go mapping of plant stress would allow for a timely intervention and mitigation of the problem before critical thresholds were exceeded, thereby maximizing productivity. The spectral signature of plant leaves was analyzed by a hyperspectral camera to identify the onset and intensity of plant water stress. Five different levels of water treatment were created in young apple trees (cv. ‘Buckeye Gala’) in a greenhouse. The trees were periodically monitored with a hyperspectral camera along with an active-illuminated spectral vegetation sensor and a digital color camera. Individual spectral images over a 385-1000 nm wavelength range were extracted at a specific wavelength to estimate reflectance and generate spectral profiles for the five different water treatment levels. Various spectral indices were calculated and correlated to stress levels. The highest correlation was found with Red Edge NDVI at 705 and 750 nm in narrowband indices and NDVI at 680 and 800 nm in broadband indices. The experimental results indicated that intelligent optical sensors could deliver decision support for plant stress detection and management.     

Using spectral distance, spectral angle and plant abundance derived from hyperspectral imagery to characterize crop yield variation

Vegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance (crop fractional cover estimated with spectral unmixing) derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band and three-band VIs based on selected wavelengths for quantifying crop yield variability. Airborne 102-band hyperspectral images acquired at the peak development stage and yield monitor data collected from two grain sorghum fields were used. A total of 64 VI images were generated based on the eight VIs and selected wavelengths for each field in this study. Two spectral distance images, two spectral angle images and two abundance images were also created based on a pair of pure plant and soil reference spectra for each field. Correlation analysis with yield showed that the eight VIs with the selected wavelengths had r values of 0.73–0.79 for field 1 and 0.82–0.86 for field 2. Although all VIs provided similar correlations with yield, the modified soil-adjusted vegetation index (MSAVI) produced more consistent r values (0.77–0.79 for field 1 and 0.85–0.86 for field 2) among the selected bands. Spectral distance, spectral angle and plant abundance produced similar r values (0.76–0.78 for field 1 and 0.83–0.85 for field 2) to the best VIs. The results from this study suggest that either a VI (MSAVI) image based on one near-infrared band (800 or 825 nm) and one visible band (550 or 670 nm) or a plant abundance image based on a pair of pure plant and soil spectra can be used to estimate relative yield variation from a hyperspectral image.     

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

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.     

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

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.

     

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

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.

     

50种植物叶片绿色度和叶绿素含量相关性研究

[目的]比较不同类群植物叶片绿色度(SPAD值)和叶绿素含量,分析其相关性,以期了解各类植物对环境的特殊适应性。[方法]利用SPAD-502叶绿素计和分光光度计分别测定广州大学校园50种常见植物的SPAD值以及叶绿素含量。[结果]13种豆目植物和4种夹竹桃科植物叶片SPAD值均在0.05水平显著高于总体平均值;豆目植物中,云实科植物叶片SPAD值和叶绿素含量普遍高于蝶形花科和含羞草科植物的相关值。6种外来植物叶片SPAD值在0.05水平显著低于总体平均值,而叶绿素a/b以及叶绿素含量则低于总体平均值,但差异不显著。[结论]50种植物叶片SPAD值与叶绿素含量的最佳相关函数为多项式函数。     

6种岭南乡土灌木耐阴性比较研究

采用隶属函数法对6种岭南乡土灌木树种耐阴性进行综合评价,选取的耐阴性因子分别为丙二醛、超氧化物歧化酶、可溶性糖、可溶性蛋白、叶绿素a/b、叶绿素含量.结果表明,6种植物耐阴性的大小顺序为波叶黄杨、谷木、三叶赤楠、子楝树、鲫鱼胆、岗柃.     

21世纪植物标本馆的发展方向——数字植物标本馆

笔者认为21世纪植物标本馆的发展方向是数字植物标本馆。数字植物标本馆是将各种原植物形态特征转化成数字化信息并存储起来以计算机技术进入标本馆并提供有效服务,而且能在网络化的环境中被本地和远程用户存取。数字植物标本馆的特点是:收藏数字化、操作电脑化、传递网络化、资源共享化。数字植物标本馆的模式是一个开放式的硬件和软件的集成平台,通过数码摄像机和数码像机把植物的各种特征数字化并通过计算机和植物标本的集成在网上服务。利用计算机图像处理软件和多媒体以及存储技术,对原始的植物标本图像等进行处理。并就数字植物标本馆存在着保存和网络安全问题进行了讨论。     

Crop water status assessment in controlled environment using crop reflectance and temperature measurements

Crop water status is an important parameter for plant growth and yield performance in greenhouses. Thus, early detection of water stress is essential for efficient crop management. The dynamic response of plants to changes of their environment is called ‘speaking plant’ and multisensory platforms for remote sensing measurements offer the possibility to monitor in real-time the crop health status without affecting the crop and environmental conditions. Therefore, aim of this work was to use crop reflectance and temperature measurements acquired remotely for crop water status assessment. Two different irrigation treatments were imposed in tomato plants grown in slabs filed with perlite, namely tomato plants under no irrigation for a certain period; and well-watered plants. The plants were grown in a controlled growth chamber and measurements were carried out during August and September of 2014. Crop reflectance measurements were carried out by two types of sensors: (i) a multispectral camera measuring the radiation reflected in three spectral bands centred between 590–680, 690–830 and 830–1000 nm regions, and (ii) a spectroradiometer measuring the leaf reflected radiation from 350 to 2500 nm. Based on the above measurements several crop indices were calculated. The results showed that crop reflectance increased due to water deficit with the detected reflectance increase being significant about 8 h following irrigation withholding. The results of a first derivative analysis on the reflectance data showed that the spectral regions centred at 490–510, 530–560, 660–670 and 730–760 nm could be used for crop status monitoring. In addition, the results of the present study point out that sphotochemical reflectance index, modified red simple ratio index and modified ratio normalized difference vegetation index could be used as an indicator of plant water stress, since their values were correlated well with the substrate water content and the crop water stress index; the last being extensively used for crop water status assessment in greenhouses and open field. Thus, it could be concluded that reflectance and crop temperature measurements might be combined to provide alarm signals when crop water status reaches critical levels for optimal plant growth.     

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

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.     

UV-B辐照下拟南芥突变体npq4、vtc2、ndr1叶绿素荧光特性研究

通过对野生型拟南芥及npq4（非光化学猝灭减弱）、vtc2（抗坏血酸生物合成途径受阻）、ndr1（NDR1基因突变导致系统获得性抗性上游信号途径中断）3种拟南芥突变体进行UV-B周期性处理,探究其在UV-B辐照下的叶绿素荧光特性并检测其叶绿素含量。结果表明：随着UV-B辐照辐射时间的延长,野生型和突变体的NPQ、Fv/Fm、ΦPSⅡ、qN、qP、叶绿素含量及叶绿素a/b均有所降低。npq4、vtc2两个突变体的NPQ、Fv/Fm、ΦPSⅡ、叶绿素a含量及叶绿素a/b值较野生型和ndr1下降幅度大而且差异显著,表现出更严重的伤害症状。据推测,这是因为非光化学猝灭途径及抗氧化物质在植物抵抗短期高强度UV-B辐照中起重要作用,而系统获得性抗性途径不能被短期的UV-B辐照所启动,因而在植物抵抗UV-B辐照中作用较弱。     

Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple

Handheld chlorophyll sensors and remote sensing are two nondestructive approaches for estimating plant nitrogen (N) status, which are now commercially available. In this paper we address three questions on the application of these technologies in perennial fruit trees: (1) can individual leaf meter measurements be used to predict N status for surrounding trees?, (2) are narrow band indices more sensitive than the normalized difference vegetation index (NDVI) to differences in plant N?, and (3) is NDVI from satellite remote sensing correlated to leaf level vegetation indices? We evaluated data from a N rate trial conducted in a commercial Fuji apple orchard (Malus domestica Borkh. cv. ‘Fuji’). SPAD and CM1000 handheld chlorophyll meters and reflectance measurements using a portable spectrometer were made on individual leaves three or four times during each growing season. The reflectance measurements were used to determine NDVI and three narrow band vegetation indices. Satellite imagery from the Quickbird sensor was acquired two or three times during each growing season and used to generate NDVI for individual trees. The leaf meter measurements and vegetation indices were compared with the N application rate and plant N status measured as total leaf tissue N.We evaluated how well single leaf meter measurements predict N status for surrounding trees by calculating the differences between actual and estimated N applications from individual measurements. On average, a sample of 12 leaves (from the same treatment and same measurement date) resulted in an estimation error of 30 kg ha⁻¹ for either the SPAD or the CM1000 sensor, representing almost half of the range in N treatment rates. To evaluate any improvement in prediction of applied N using narrow band indices, we used analysis of variance (ANOVA) to compare three narrow band indices with the leaf meters and NDVI measured at leaf and canopy levels. Two narrow band indices, red edge vegetation stress index (RVSI) and modified chlorophyll absorption in reflectance index (MCARI) had higher F-values (31 and 41, respectively) than did NDVI from leaf level measurements (26), from satellite NDVI (6), or the CM1000 chlorophyll meter (12). The ANOVA results support improvements in leaf sensors using index values other than NDVI. We found that NDVI from satellite imagery acquired close to the leaf level measurement dates were positively correlated to the chlorophyll sensors and vegetation indices. When the data was averaged to the experiment plot level (twelve leaves total), the correlation coefficients between the satellite NDVI and the other sensors ranged from 0.68 for NDVI from leaf level reflectance to 0.84 with the CM1000 chlorophyll meter. Given the level of correlations, remote sensing might be a useful tool to extrapolate handheld measurements spatially throughout an orchard.     

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

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.     

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

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.