Extended-altitude, aerial mapping of crop NDVI using an active optical sensor: A case study using a Raptor™ sensor over wheat

Recently reported testing of active, optical crop sensors in low-level aircraft have demonstrated a new class of airborne sensing system that can be deployed under any ambient illumination conditions, including at night. A second-generation, high-powered, light-emitting diode system has been assembled and tested over a 80 ha field of wheat (Triticum aestevum) by mapping the normalised difference vegetation index (NDVI) at altitudes ranging from 15 to 45 m above the canopy; significantly higher altitudes than existing systems. Comparisons with a detailed on-ground NDVI survey indicated the aerial sensor values were highly correlated to the on-ground sensor (0.79 < R² < 0.85), with close to unity slope and zero offset. The maximum average deviation between aerial and on-ground NDVI values was 0.04. Sample calculations involving two exemplar algorithms, one for estimating monoculture pasture biomass and the other for estimating wheat yield, indicate such deviations to have no significant effect on prediction accuracy. The subsequent NDVI maps proved to be invariant to sensor height over the 15-45 m altitude range meaning this new sensor configuration can be deployed over undulating crops and pastures and in areas with nearby obstacles such as trees and buildings.     

Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds

Site-specific weed management is defined as the application of customised control treatments only where weeds are located within the crop-field by using adequate herbicide according to weed emergence. The aim of the study was to generate georeferenced weed seedling infestation maps in two sunflower fields by analysing overlapping aerial images of the visible and near-infrared spectrum (using visible or multi-spectral cameras) collected by an unmanned aerial vehicle (UAV) flying at 30 and 60 m altitudes. The main tasks focused on the configuration and evaluation of the UAV and its sensors for image acquisition and ortho-mosaicking, as well as the development of an automatic and robust image analysis procedure for weed seedling mapping used to design a site-specific weed management program. The control strategy was based on seven weed thresholds with 2.5 steps of increasing ratio from 0 % (herbicide must be applied just when there is presence or absence of weed) to 15 % (herbicide applied when weed coverage >15 %). As a first step of the imagery analysis, sunflower rows were correctly matched to the ortho-mosaicked imagery, which allowed accurate image analysis using object-based image analysis [object-based-image-analysis (OBIA) methods]. The OBIA algorithm developed for weed seedling mapping with ortho-mosaicked imagery successfully classified the sunflower-rows with 100 % accuracy in both fields for all flight altitudes and camera types, indicating the computational and analytical robustness of OBIA. Regarding weed discrimination, high accuracies were observed using the multi-spectral camera at any flight altitude, with the highest (approximately 100 %) being those recorded for the 15 % weed threshold, although satisfactory results from 2.5 to 5 % thresholds were also observed, with accuracies higher than 85 % for both field 1 and field 2. The lowest accuracies (ranging from 50 to 60 %) were achieved with the visible camera at all flight altitudes and 0 % weed threshold. Herbicide savings were relevant in both fields, although they were higher in field 2 due to less weed infestation. These herbicide savings varied according to the different scenarios studied. For example, in field 2 and at 30 m flight altitude and using the multi-spectral camera, a range of 23–3 % of the field (i.e., 77 and 97 % of area) could be treated for 0–15 % weed thresholds. The OBIA procedure computed multiple data which permitted calculation of herbicide requirements for timely and site-specific post-emergence weed seedling management.     

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS) fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI) and photochemical reflectance index(PRI). NDVI is useful for indicating crop growth/phenology, whereas PRI was developed for observing physiological conditions. Thus, the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system. However, capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions, such as solar irradiance and precipitation. Further, unlike growth/phenology, the physiological condition has diurnal changes as well as seasonal characteristics. This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice, barley, and garlic. First, the measurement accuracy of SRSs was compared with handheld spectrometers, and the R~2 values between the two devices were 0.96 and 0.81 for NDVI and PRI, respectively. Second, the experimental study of threshold criteria with respect to meteorological variables(i.e., insolation, cloudiness, sunshine duration, and precipitation) was conducted, and sunshine duration was the most useful one for excluding distorted values of the vegetation indices. After data processing based on sunshine duration, the R2 values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI) and 0.065–0.298(PRI) on the three crops, and the seasonal signals of vegetation indices became noticeably improved. This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.     

Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn

The efficiency of side-dressing, a more efficient of nitrogen application method than uniform application in either late Fall or early Spring, relies heavily on the capability of nitrogen deficiency detection on a sprayer. To determine the site-specific yield potential for corn, multi-spectral image analysis including aerial- and ground-based images has been used. Some acceptable calibration relationships between the multi-spectral reflectance and SPAD readings have been found from previous study. In sunny weather conditions there was a shadow in the image made by corn leaf itself. This research investigated the shadow effect on the image for detecting corn nitrogen deficiency based on corn canopy reflectance information. The results indicated that the reflectance of red channel in shadow area showed strong inverse correlation, so the vegetation index NDVI using red and NIR channels also showed strong correlation (R² = 77) compared to the whole leaf and bright area. And the reflectance (green and red) and vegetation index(G_NDVI, NDVI, and ratio) in shadow area showed more consistent correlations than others using these image analysis methods.     

不同水氮耦合条件下棉花冠层NDVI分析与产量估算

为研究冠层归一化差值植被指数(Normalized difference vegetation index,NDVI)在棉花重要生育时期估算棉花产量的可行性,使用GreenSeeker分别对不同灌水施肥条件下棉花光谱反射率NDVI值进行测定优化,建立NDVI值与产量关系数学模型,并对模型精度进行验证。结果显示:不同水氮组合随着生育期的推移棉花冠层NDVI值变化趋势基本一致,都呈"低-高-低"的变化规律;选取在棉花出苗后80、105和140d冠层NDVI值分别与产量进行相关性分析,得出冠层NDVI值与产量具有明显的正相关关系,相关系数分别为R2=0.376 0,0.093 4,0.363 9。利用独立的试验数据对相关性最高的水氮组合棉花出苗后80d的产量模型进行模型验证,其相关系数R2=0.712 6,均方根误差(Root mean square error,RMSE)561.04kg/hm2。因此,棉花出苗后80d的冠层NDVI值可以估测棉花产量。     

Methodology for measuring fAPAR in crops using a combination of active optical and linear irradiance sensors: a case study in Triticale (X Triticosecale Wittmack)

The amount of photosynthetically active radiation (PAR, 0.4–0.7 μm) absorbed by plants for photosynthesis relative to incident radiation is defined as the fraction of absorbed photosynthetically active radiation (fAPAR). This is an important variable in both plant biomass production and plant growth modeling. This study investigates the application of a newly developed, linear irradiance sensor (LightScout Quantum Bar Sensor, LightScout, Spectrum Technologies, Inc. USA), to quantify fAPAR for a demonstrator crop, Triticale (X Triticosecale Wittmack). A protocol was devised for sensor placement to determine reflected PAR components of fAPAR and to determine the optimal time of day and sensor orientation for data collection. Coincident, top of canopy, normalized difference vegetation index (NDVI) measurements were also acquired with a CropCircle? ACS-210 sensor and measurements correlated with derived fAPAR values. The optimum height of the linear irradiance sensor above soil or plant canopy was found to be 0.4 m while measuring reflected PAR. Measurement of fAPAR was found to be stable when conducted within 1 h of local solar noon in order to avoid significant bidirectional effects resulting from diurnal changes of leaf orientation relative to the vertically-placed sensor. In the row crop studied, averaging fAPAR readings derived from the linear irradiance sensor orientated across and along the plant row provided an R² = 0.81 correlation with above-canopy NDVI. Across row sensor orientation also gave a similar correlation of R² = 0.76 allowing the user to reduce sampling time.     

Evaluating the performance of xanthophyll,chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species

This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (g_s), stem water potential (Ψ_s) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R₇₀₀/R₆₇₀), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI_(570–515)] and the normalized PRI (PRI_norm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψ_s yielded the best correlations with PRI and PRI_(570–515) (r² = 0.53) in almond trees, with TCARI/OSAVI (r² = 0.88) in apricot trees and with PRI_norm, R₇₀₀/R₆₇₀ and NDVI (r² from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (T_c) and PRI_(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψ_s was the most sensitive indicator at midday. PRI_norm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI_(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, g_s and, particularly, Ψ_s (r² = 0.61 and 0.65, respectively). This work demonstrated the feasibility of using narrow-band multispectral-derived indices to retrieve water status for a variety of crop species with contrasting phenology and canopy architecture.     

A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring

Image-based remote sensing is one promising technique for precision crop management. In this study, the use of an ultra light aircraft (ULA) equipped with broadband imaging sensors based on commercial digital cameras was investigated to characterize crop nitrogen status in cases of combined nitrogen and water stress. The acquisition system was composed of two Canon^? EOS 400D digital cameras: an original RGB camera measuring luminance in the Red, Green and Blue spectral bands, and a modified camera equipped with an external band-pass filter measuring luminance in the near-infrared. A 5?month experiment was conducted on a sugarcane (Saccharum officinarum) trial consisting of three replicates. In each replicate, two sugarcane cultivars were grown with two levels of water input (rainfed/irrigated) and three levels of nitrogen (0, 65 and 130?kg/ha). Six ULA flights, coupled with ground crop measurements, took place during the experiment. For nitrogen status characterisation, three indices were tested from the closed canopy: the normalised difference vegetation index (NDVI), the green normalised difference vegetation index (GNDVI), and a broadband version of the simple ratio pigment index (hereafter referred to as the SRPI_b), calculated from the ratio between blue and red bands of the digital camera. The indices were compared with two nitrogen crop variables: leaf nitrogen content (N_L) and canopy nitrogen content (N_C). SRPI_b showed the best correlation (R ²?=?0.7) with N_L, independently of the water and the N treatment. NDVI and GNDVI were best correlated with N_C values with correlation coefficients of 0.7 and 0.64 respectively, but the regression coefficients were dependent on the water and N treatment. These results showed that SRPI_b could characterise the nitrogen status of sugarcane crop, even in the case of combined stress, and that such acquisition systems are promising for crop nitrogen monitoring.     

Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops

LiDAR (Light Detection And Ranging) is a remote-sensing technique for the measurement of the distance between the sensor and a target. A LiDAR-based detection procedure was tested for characterisation of the weed vegetation present in the inter-row area of a maize field. This procedure was based on the hypothesis that weed species with different heights can be precisely detected and discriminated using non-contact ranging sensors such as LiDAR. The sensor was placed in the front of an all-terrain vehicle, scanning downwards in a vertical plane, perpendicular to the ground, in order to detect the profile of the vegetation (crop and weeds) above the ground. Measurements were taken on a maize field on 3 m wide (0.45 m²) plots at the time of post-emergence herbicide treatments. Four replications were assessed for each of the four major weed species: Sorghum halepense, Cyperus rotundus, Datura ferox and Xanthium strumarium. The sensor readings were correlated with actual, manually determined, height values (r² = 0.88). With canonical discriminant analysis the high capabilities of the system to discriminate tall weeds (S. halepense) from shorter ones were quantified. The classification table showed 77.7% of the original grouped cases (i.e., 4800 sampling units) correctly classified for S. halepense. These results indicate that LiDAR sensors are a promising tool for weed detection and discrimination, presenting significant advantages over other types of non-contact ranging sensors such as a higher sampling resolution and its ability to scan at high sampling rates.     

RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields

In this paper, a new method to fuse low resolution multispectral and high resolution RGB images is introduced, in order to detect Gramineae weed in rice fields with plants at 50 days after emergence (DAE).The images are taken from a fixed-wing unmanned aerial vehicle (UAV) at 60 and 70 m altitude. The proposed method combines the texture information given by a high resolution red–green–blue (RGB) image and the reflectance information given by a low resolution multispectral (MS) image, to obtain a fused RGB-MS image with better weed discrimination features. After analyzing the normalized difference vegetation index (NDVI) and normalized green red difference index (NGRDI) for weed detection, it was found that NGRDI presents better features. The fusion method consists of decomposing the RGB image using the intensity, hue and saturation (IHS) transformation, then, a second order Haar wavelet transformation is applied to the intensity layer (I) and the NGRDI image. From this transformation, the low–low (LL) coefficients of the NGRDI image are replaced by the LL coefficients of the I layer. Finally, the fused image is obtained by transforming the new wavelet coefficients to RGB space. To test the method, a one hectare experimental plot with rice plants at 50 DAE with Gramineae weeds was selected. Additionally, to compare the performance of the method, two indices were used, specifically, the M/M_GT index which is the percentage of detected weed area, and the MP index which indicates the precision of weed detection. These indices were evaluated in four validation zones using three Neural Networks (NN) detection systems based on three types of images; namely, RGB, RGB + NGRDI, and fused RGB-NGRDI. The best weed detection performance was obtained by the NN with the fused image, with M/M_GT index between 80 and 108% and MP between 70 and 85%.     

Assessment of forage mass from grassland swards by height measurement using an ultrasonic sensor

The non-destructive assessment of forage mass in legume-grass mixtures as a tool for yield mapping in precision farming applications has been investigated in two field experiments. An ultrasonic sensor was used to determine sward heights. Forage mass-height relationships were evaluated by carrying out static measurements on binary legume-grass mixtures of white clover (Trifolium repens L.), red clover (Trifolium pratense L.), and lucerne (Medicago sativa L.) with perennial rye grass (Lolium perenne L.) across a wide range of sward heights (5.0-104.2 cm) and forage mass (0.15-11.25 t ha⁻¹). Mobile measurements, hereafter referred to as “on-the-go” were conducted by mounting the ultrasonic sensor in combination with a high-precision Differential Global Positioning System (DGPS) on a vehicle. Data were recorded along experimental plots consisting of perennial rye grass and grass-clover mixtures similar to the mixtures that were used for the static experiment. The static experiment revealed a relationship between ultrasonic sward height and forage mass explaining 74.8% of the variance with a standard error (SE) of 1.05 t ha⁻¹ in a common dataset. The type of legume species, weed proportion, and growth period had a significant impact on the above mentioned relationship. Legume-specific regression functions had higher R²-values of up to 0.855 (white clover mixture). Datasets including legume-specific mixtures and pure swards of both components reached comparable R² values between 0.799 and 0.818 but exhibited higher SE values. The abundance of weeds resulted in increased ultrasonic sward heights for the same levels of forage mass. On-the-go measurements across experimental field plots yielded a sward height range of 1.4-70.4 cm. Abrupt forage mass changes at the transition from treatment plots to cut interspaces resulted in a significant deviation from stubble height within a distance of 50 cm to plot borders. When legume-specific equations derived from static measurements were applied to sward heights, forage mass was overestimated by 21.4% on average. Mean residuals from predicted forage mass ranged between 0.893 (pure grass) and 1.672 (red clover mixture) and increased significantly if the point sampling distance along the track was increased to more than 0.82 m on average across all plots. The prediction accuracy of forage mass from ultrasonic height measurements is promising; however, further modifications to the technique are necessary. One such improvement can be the use of spectral reflectance signatures in combination with the ultrasonic sensor.     

Performance evaluation of a crop/weed discriminating microsprayer

An intelligent real-time microspraying weed control system was developed. The system distinguishes between weed and crop plants and a herbicide (glyphosate) is selectively applied to the detected weed plants. The vision system captures 40 RGB images per second, each covering 140 mm by 105 mm with an image resolution of 800 × 600 pixels. From the captured images the forward velocity is estimated and the spraycommands for the microsprayer are calculated. Crop and weed plants are identified in the image, and weed plants are sprayed. Performance of the microsprayer system was evaluated under laboratory conditions simulating field conditions. A combination of maize (Zea mays L.), oilseed rape (Brassica napus L.) and scentless mayweed (Matricaria inodora L.) plants, in growth stage BBCH10, was placed in pots, which were then treated by the microspray system. Maize simulated crop plants, while the other species simulated weeds. The experiment were conducted at a velocity of 0.5 m/s. Two weeks after spraying, the fraction of injured plants was determined visually. None of the crop plants were harmed while 94% of the oilseed rape and 37% of the scentless mayweed plants were significantly limited in their growth. Given the size and shape of the scentless mayweed plants and the microsprayer geometry it was calculated that the microsprayer could only hit 64% of the scentless mayweed plants. The system was able to effectively control weeds larger than 11 mm × 11 mm.     

A rapid multiplication system for ‘Candidatus Liberibacter asiaticus’ through regeneration of axillary buds in vitro

‘Candidatus Liberibacter asiaticus (CLas)’, which causes citrus Huanglongbing (HLB) disease, has not been successfully cultured in vitro to date. Here, a rapid multiplication system for CLas was established through in vitro regeneration of axillary buds from CLas-infected ‘Changyecheng’ sweet orange (Citrus sinensis Osbeck). Stem segments with a single axillary bud were cultured in vitro to allow CLas to multiply in the regenerating axillary buds. A high CLas titer was detected in the regenerated shoots on an optimized medium at 30 days after germination (DAG). This titer was 28.2-fold higher than in the midribs from CLas-infected trees growing in the greenhouse. To minimize contamination during in vitro regeneration, CLas-infected axillary buds were micrografted onto seedlings of ‘Changyecheng’ sweet orange and cultured in a liquid medium. In this culture, the titers of CLas in regenerated shoots rapidly increased from 7.5×10⁴ to 1.4×10⁸ cells μg^–1 of citrus DNA during the first 40 DAG. The percentages of shoots with >1×10⁸ CLas cells μg^–1 DNA were 30 and 40% at 30 and 40 DAG, respectively. Direct tissue blot immunoassay (DTBIA) indicated that the distribution of CLas was much more uniform in regenerated plantlets than in CLas-infected trees growing in the greenhouse. The disease symptoms in the plantlets were die-back, stunted growth, leaf necrosis/yellowing, and defoliation. The death rate of the plantlets was 82.0% at 60 DAG. Our results show that CLas can effectively multiply in citrus plantlests in vitro. This method will be useful for studying plant-HLB interactions and for rapid screening of therapeutic compounds against CLas in citrus.     

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

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.     

Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae)

Detection of crop stress is one of the major applications of hyperspectral remote sensing in agriculture. Many studies have demonstrated the capability of remote sensing techniques for detection of nutrient stress on cotton with only few on pest damage but none so far on leafhopper (LH) severity. Subsequent to introduction of Bt cotton, leafhopper is emerging as a key pest in several countries. In view of its wide host range, geographical distribution and damage potential, a study was initiated to characterise leafhopper stress on cotton, identify sensitive bands, and derive hyperspectral vegetation indices specific to this pest. Cotton plants with varying levels of LH severity were selected from three locations across major cotton growing regions of India. About 57-58 cotton plants from each location exhibiting different levels of LH damage symptoms were selected. Reflectance measurements in the spectral range of 350-2500 nm were made using hyperspectral radiometer. Simultaneously chlorophyll (Chl) and relative water content (RWC) were also estimated from the selected plants. Reflectance from healthy and leafhopper infested plants showed a significant difference in VIS and NIR regions. Decrease in Chl a pigment was more significant than Chl b in the infested plants and the ratio of Chl a/b showed a decreasing trend with increase in LH severity. Regression analysis revealed a significant linear relation between LH severity and Chl (R² = 0.505^∗∗), and a similar fit was also observed for RWC (R² = 0.402^∗∗). Plotting linear intensity curves between reflectance at each waveband with infestation grade resulted in six sensitive bands that exhibited maximum correlation at different regions of the electromagnetic spectrum (376, 496, 691, 761, 1124 and 1457 nm). Regression analysis of several ratio indices formulated with two or more of these sensitive bands led to the identification of new leaf hopper indices (LHI) with a potential to detect leafhopper severity. These new indices along with 20 other stress related hyperspectral indices compiled from literature were further tested for their ability to detect LH severity. Two novel indices LHI 2 and LHI 4 proposed in this study showed significantly high coefficients of determination across locations (R² range 0.521 to 0.825^∗∗) and hence have the potential use for detection of leafhopper severity in cotton.     

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

Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events. Leaf water content(LWC) is an important waterlogging indicator, and hyperspectral remote sensing provides a non-destructive, real-time and reliable method to determine LWC. Thus, based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage. Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature. Combined with methods such as vegetation index construction, correlation analysis, regression analysis, BP neural network(BPNN), etc., we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC. LWC decreased faster under severe stress than under slight stress, but the effect of long-term slight stress was greater than that of short-term severe stress. The sensitive spectral bands of LWC were located in the visible(VIS, 400–780 nm) and short-wave infrared(SWIR, 1 400–2 500 nm) regions. The BPNN Model with the original spectrum at 648 nm, the first derivative spectrum at 500 nm, the red edge position(λr), the new vegetation index RVI(437, 466), NDVI(437, 466) and NDVI′(747, 1 956) as independent variables was the best model for inverting the LWC of waterlogging in winter wheat(modeling set: R~2=0.889, RMSE=0.138; validation set: R~2=0.891, RMSE=0.518). These results have important theoretical significance and practical application value for the precise control of waterlogging stress.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

Effects of Bupleurum extract on blood metabolism,antioxidant status and immune function in heat-stressed dairy cows

This experiment was conducted to evaluate the effects of Bupleurum extract(BE) on blood metabolites, antioxidant status, and immune function in dairy cows under heat stress. Forty lactating Holstein cows were randomly assigned to 1 of 4 treatments. The treatments consisted of 0, 0.25, 0.5, and 1.0 g of BE kg–1 dry matter. Supplementation with BE decreased(P0.05) blood urea nitrogen(BUN) contents and increased blood total protein(TP) and albumin(ALB) levels compared with control cows, but it had no effects(P0.05) on blood glucose(GLU), nonesterified fatty acid(NEFA), total triglyceride(TG), low-density lipoprotein cholesterol(LDL-C), and high density lipoprotein cholesterol(HDL-C). Compared with control cows, cows fed BE had higher(P0.05) superoxide dismutase(SOD) and glutathione peroxidase(GSH-Px) activity. However, supplementation with BE had no effect(P0.05) on total antioxidant capacity(T-AOC) or malondialdehyde(MDA) levels. The immunoglobulin(Ig) A and G contents increased(P0.05) in cows fed 0.25 or 0.5 g of BE kg–1. Interleukin(IL)-2 and IL-4 levels were higher(P0.05) in cows fed 0.5 and 1.0 g of BE kg–1, and IL-6 was significantly elevated(P0.05) in cows fed 0.5 g of BE kg–1. There were no treatment effects(P0.05) on the CD4+ and CD8+ T lymphocyte ratios, CD4+/CD8+ ratio, or tumor necrosis factor-α(TNF-α) level among the groups. These findings suggest that BE supplementation may improve protein metabolism, in addition to enhancing antioxidant activity and immune function in heat-stressed dairy cows.