Hand-held Chlorophyll Meter Research Articles

Prediction Method for Pomegranate Chlorophyll Content Based on Multi-feature Fusion of Unmanned Aerial Vehicle

The aim of this study was to obtain RGB and multispectral images of fruit tree canopy during the flowering period of pomegranate by multispectral unmanned aerial vehicle (UAV) to quickly and accurately predict the chlorophyll content in order to improve the monitoring efficiency of the orchard. A handheld chlorophyll meter was used to obtain the actual chlorophyll values, and image processing techniques were combined to extract parameters such as color features and texture features of the RGB images as well as vegetation index of the multispectral images. A chlorophyll content prediction method based on the support vector regression model and the convolutional neural network model (CNN-Attention) combined with the attention mechanism was established. The results showed that (1) the accuracy of the prediction model was improved by the fusion of RGB image features and multispectral image vegetation index. (2) After model comparison, the CNN-Attention model built under the fused features was the best in chlorophyll content prediction with R2, RE, and RMSE of 0.9699, 0.0052, and 0.6013, respectively.This study provides a more accurate method for fruit tree chlorophyll content prediction using UAVs, which provides a practical reference for orchard management.

Assessment of Nitrogen Nutrition Index of Winter Wheat Canopy from Visible Images for a Dynamic Monitoring of N Requirements

Hand-held chlorophyll meters or leaf-clip-type sensors indirectly and instantaneously measure leaf N content. They can provide an N nutrition index (NNI) value that is crucial information for adjusting the amount of N fertilizer to the actual N status of the plant. Although these measurements are non-invasive and non-destructive, they require numerous repetitions at the canopy scale. The objective of this work was to explore the potential of visible images to predict nitrogen status in winter wheat crops from estimating NNI and to compare these results with those deduced from classical methods. Based on a dark green colour index (DGCI), which combines hue, saturation and brightness, a normalized DGCI (nDGCI) was proposed as the ratio between the measurements of the study microplot and those of the over-fertilized microplot. The methodology was performed on winter wheat microplots with a nitrogen gradient. Half of the microplots were grown with a single cultivar (LG Absalon) and the other half with a mixture of four wheat cultivars. The impact of optical device (digital camera or smartphone), the white balance (Manual or Automatic), the crop growth stage (two-nodes or heading) and cultivars (single or mixed) on the relationship between (DGCI, nDGCI) and NNI was evaluated. The results showed a close correlation between the nDGCI values and the NNI_NTester values, especially on a single cultivar (LG Absalon; R2 = 0.73 up to 0.91 with smartphone). It suggested that the relationship is highly sensitive to the wheat cultivar. This approach with no specific calibration of images is promising for the estimation of N requirements in wheat field.

Estimation of Relative Chlorophyll Content in Spring Wheat Based on Multi-Temporal UAV Remote Sensing

Relative chlorophyll content (SPAD) is an important index for characterizing the nitrogen nutrient status of plants. Continuous, rapid, nondestructive, and accurate estimation of SPAD values in wheat after heading stage can positively impact subsequent nitrogen fertilization management strategies, which regulate grain filling and yield quality formation. In this study, the estimation of SPAD of leaf relative chlorophyll content in spring wheat was conducted at the experimental base in Wuyuan County, Inner Mongolia in 2021. Multispectral images of different nitrogen application levels at 7, 14, 21, and 28 days after the wheat heading stage were acquired by DJI P4M UAV. A total of 26 multispectral vegetation indices were constructed, and the measured SPAD values of wheat on the ground were obtained simultaneously using a handheld chlorophyll meter. Four machine learning algorithms, including deep neural networks (DNN), partial least squares (PLS), random forest (RF), and Adaptive Boosting (Ada) were used to construct SPAD value estimation models at different time from heading growth stages. The model’s progress was evaluated by the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAPE). The results showed that the optimal SPAD value estimation models for different periods of independent reproductive growth stages of wheat were different, with PLS as the optimal estimation model at 7 and 14 days after heading, RF as the optimal estimation model at 21 days after heading, and Ada as the optimal estimation model at 28 d after heading. The highest accuracy was achieved using the PLS model for estimating SPAD values at 14 d after heading (training set R2 = 0.767, RMSE = 3.205, MAPE = 0.060, and R2 = 0.878, RMSE = 2.405, MAPE = 0.045 for the test set). The combined analysis concluded that selecting multiple vegetation indices as input variables of the model at 14 d after heading stage and using the PLS model can significantly improve the accuracy of SPAD value estimation, provides a new technical support for rapid and accurate monitoring of SPAD values in spring wheat.

Relationship between chlorophyll and nitrogen content, and fresh matter production in basil ‘Nufar’ (Ocimum basilicum) with three handheld chlorophyll meter readings: SPAD, atLEAF and MC-100

Determinations of nitrogen (N) and chlorophyll (Chl) by destructive methods are precise but time-consuming and frequently expensive. The objective of this study was to determine the relationship between SPAD, atLEAF, and MC-100 readings with the concentrations of Chl, N, and fresh matter (FM) in basil 'Nufar'. In addition, we determined the relationship of the N concentration with fresh matter (FM). The plants were irrigated with five levels of N (0, 4, 8, 12, and 16 mEq·L-1 of NO3) based on a modified Steiner solution for 42 days after transplant. Plants were evaluated weekly with SPAD, atLEAF and MC-100 portable chlorophyll meters in the greenhouse, while fresh matter and the concentration of chlorophyll and N were determined in the laboratory using conventional techniques. The relationship between SPAD readings, Chl, N, and FM was strong, positive, linear, and significant, with correlation coefficients (R2) between 0.94 and 0.98. For atLEAF and MC-100 readings, R2 values were 0.96 to 0.99 and 0.96 to 0.99, respectively. The relationships between SPAD, atLEAF and MC-100 readings were strong, positive, and significant, with R2 values of 0.98. Similar behavior was shown by the relationship of N concentration and FM with R2 of 0.88. With these results, we conclude that SPAD, atLEAF, and MC-100 can be used to estimate the nutrient status of N in a non-destructive, rapid and reliable manner during basil production.

Non-destructive estimation of chlorophyll content of peanuts grown at different soil texture and salinity levels

Chlorophyll is a significant biochemical component and can be determined in the laboratory (destructive) and using various chlorophyll content measuring devices (non-destructive). In this study, destructive and non-destructive methods were used to determine chlorophyll content and compared in peanut (Arachis hypogaea cv. NC-7) grown under different soil texture and saline water applications. The experiment was carried out in a complete randomized block design in pots using two soil textures (clay-loam and sandy) and three irrigation water salinity (0.7, 2.1 and 3.3 dS m-1). While the chlorophyll contents (Chl-a , Chl-b, Chl-a+b, Chl-a/b) were determined with the acetone extraction procedure, which is classified as destructive methods under laboratory conditions, the Chlorophyll Content Index (CCI) values were measured with the hand-held chlorophyll meter device (Apogee CCM-200), which is a non-destructive method. While irrigation water salinity decreased all types of chlorophyll contents (Chl-a, Chl-b, Chl-a+b) (mg cm-2), it did not cause a statistical difference in Chl-a/b. Linear and polynomial models were fitted between the different chlorophyll contents and the CCI values under different soil textures and saline water levels. Model performances were slightly better with the polynomial model compared to the linear model in all experimental treatments. Since the difference between model performances is small, it is recommended to use the linear model due to its ease of use. In addition, the total chlorophyll content can be safely estimated under saline conditions by using portable chlorophyll meters.

Estimation of shrub willow leaf chlorophyll concentration across different growth stages using a hand-held chlorophyll meter to monitor plant health and production

Perennial shrub willow crops can simultaneously address environmental issues and produce biomass for biofuels, bioproducts and bioenergy. Chlorophyll is an essential biochemical property for characterizing plant health and growth and remote sensing techniques have been developed to quantify plant chlorophyll concentration. However, these techniques are highly dependent on the ability to obtain a large number of ground-based observations to train and validate the models. Given the species-specific and growth stage-dependent nature of leaf chlorophyll concentration (LCC), and the limited research about LCC estimation for shrub willow, we proposed a new model for quantifying shrub willow LCC from chlorophyll meter (CM) readings. Results indicated that there were statistically significant interaction effects in CM readings between cultivar and root age across willow growth stages. However, neither the cultivar, root age, nor their interaction were statistically significant predictor variables in a model for LCC, so separate response curves for different cultivars or root ages were not necessary. To consider changes in LCC across the growing season, we included growing degree days (GDD) as an additional predictor for estimating LCC from CM readings. The model including GDD performed better (R2 = 0.92, RMSE = 4.81 μg/cm2) than the model (R2 = 0.90, RMSE = 5.38 μg/cm2) using only CM readings. Compared to previous studies, this model reduced complexity and achieved more accurate predictions of seasonal LCC, thus providing a readily applicable approach for mapping willow LCC using remote sensing technologies.

Spectral response of chlorophyll content during leaf senescence in European beech trees

Leaf chlorophyll content declines in response to environmental stressors and during natural senescence periods. Assessments of forest health and vulnerability to stressors require repeated measurements of forest health to quantify declines in chlorophyll content and identify shifts in the timing of key phenological events. Although remote sensing is ideal for making reliable and repeated forest health measurements to assess forest response to environmental change over large areas, reliable warning systems for monitoring plant health are lacking. To facilitate the estimation of chlorophyll content from remotely sensed data, we define the relationships between leaf chlorophyll content and spectral indices in European beech trees (Fagus sylvatica). We show that hand-held chlorophyll meters can be calibrated to leaf chlorophyll content providing a non-destructive method for rapid assessment of leaf chlorophyll content in the field. Spectral indices based on the red edge spectral region have the strongest relationship with leaf chlorophyll content (mND705 R2 = 0.95, RMSE = 4.70; mSR705 R2 = 0.95, RMSE = 4.71) but are unable to discriminate between photosynthetically active leaves and inactive leaves. Alternative spectral indices can discriminate effectively between photosynthetically active and inactive leaves but are less well suited to defining declines in chlorophyll content. These findings reveal key research needs for improving the use of remote sensing data for forest health assessments.

SPAD-502 and atLEAF CHL PLUS values provide good estimation of the chlorophyll content for Hevea brasiliensis Müll. Arg. Leaves

Measurement of chlorophyll content using destructive methods is not efficient due to a large number of samples, cost, and time needed. Estimationof chlorophyll content by nondestructive methods using handheld chlorophyll meter may be considered to improve efficiency. This research aimed to determine the formula to convert SPAD-502 and atLEAF CHL PLUS values (relative indicator of chlorophyll content) to estimated (absolute) rubber leaves chlorophyll content. Twenty leaves of rubber plant were measured using SPAD-502 and atLEAF CHL PLUS at the same time to determine SPAD-502 and atLEAF CHL PLUS values. The measured leaves were then collected to determine the chlorophyll content using a standard laboratory procedure. Regression and correlation analyses (among 3 methods) were conducted using SAS v.9 software. The results showed that between SPAD-502 and atLEAF CHL PLUS values were closely correlated, hence both of the devices can substitute each other to estimate rubber leaf chlorophyll content. In addition, the relationship between atLEAF CHL PLUS and SPAD-502 values with actual chlorophyll content of rubber clone SP 217, PB 260, GT1, and all clones (general) were significant with high coefficient of determination (R2) as well as low Root Mean Square Error (RMSE) and Coefficient of Variation (CV). Therefore, by using formula determined in this study, both atLEAF CHL PLUS and SPAD-502 can be suggested for accurate, fast, and non-destructive estimation of chlorophyll content of rubber plant leaf.

Leaf chlorophyll estimates of temperate deciduous shrubs during autumn senescence using a SPAD-502 meter and calibration with extracted chlorophyll

The soil plant analysis development (SPAD) meter proved effective at estimating leaf Chl content of native and non-native temperate deciduous shrubs. However, due to the change in the relationship between SPAD values and Chl content during the course of autumn senescence, it may be necessary to establish separate calibration equations to cover a range of leaf Chl concentrations. Non-destructive estimates of leaf chlorophyll (Chl) content by hand-held chlorophyll meters such as the Minolta SPAD-502 provide an effective time-efficient method of collecting field data on senescence. To establish useable species-specific calibration equations of relationships between chlorophyll meter readings and leaf Chl content. During autumn, we collected SPAD values and quantum yield of photosystem-II (ΦPSII) over a 10-week period from 1044 leaf samples representing native and non-native temperate deciduous shrub species growing in the wild. Subsequently, Chl was extracted from leaf discs (1 cm diameter), incubated at 65 °C for 2 h in dimethyl sulfoxide, and quantified by spectroscopy. Relationships between extracted Chl and SPAD values were established using linear, quadratic, and exponential equations. Quadratic functions proved the most reliable fit for the data. Interestingly, non-native species tended to have higher leaf Chl content and SPAD values but also exhibited higher variability than native species. The strength (r2) of the SPAD-Chl relationships was weaker than those reported for agricultural or greenhouse-grown plants, but strengthened over time as Chl, ΦPSII, and SPAD values declined. The SPAD meter proved effective at estimating leaf Chl content of native and non-native temperate deciduous shrubs. These estimates may be used in future to validate remote sensing (satellite and near-surface) vegetation indices in late autumn when trees are leafless.

Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries.

To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available.

Nitrogen and chlorophyll status determination in durum wheat as influenced by fertilization and soil management: Preliminary results.

Handheld chlorophyll meters as Soil Plant Analysis Development (SPAD) have proven to be useful tools for rapid, no-destructive assessment of chlorophyll and nitrogen status in various crops. This method is used to diagnose the need of nitrogen fertilization to improve the efficiency of the agricultural system and to minimize nitrogen losses and deficiency. The objective of this study is to evaluate the effect of repeated conservative agriculture practices on the SPAD readings, leaves chlorophyll concentration and Nitrogen Nutrition Index (NNI) relationships in durum wheat under Mediterranean conditions. The experimental site is a part of a long-term-experiment established in 1994 and is still on-going where three tillage managements and three nitrogen fertilizer treatments were repeated in the same plots every year. We observed a linear relationship between the SPAD readings performed in the central and distal portion of the leaf (R2 = 0.96). In fertilized durum wheat, we found all positive exponential relationships between SPAD readings, chlorophyll leaves concentration (R2 = 0.85) and NNI (R2 = 0.89). In the unfertilized treatment, the SPAD has a good attitude to estimate leaves chlorophyll concentration (R2 = 0.74) and NNI (R2 = 0.77) only in crop grow a soil with relative high content of soil organic matter and nitrogen availability, as observed in the no tilled plots. The results show that the SPAD can be used for a correct assessment of chlorophyll and nitrogen status in durum wheat but also to evaluate indirectly the content of soil organic matter and nitrogen availability during different growth stages of the crop cycle.

Multispectral Imaging with Raspberry Pi for Assessment of Plant Health Status

Non-destructive measurement of plant chlorophyll concentration using the normalized difference vegetation index (NDVI) has long been standard practice to determine the plant health status. This is, because the NDVI value is correlated with the chlorophyll concentration which in turn is highly correlated with other vital plant parameters such as nitrogen and magnesium concentration. Initially the NDVI values were obtained from satellite imagery and thus could only be used to assess the health status of bigger ecosystems like forests and crop fields. With the introduction of handheld chlorophyll meters like the Chlorophyll Meter SPAD-502 Plus made by Konica Minolta, the same principle could be used to determine the chlorophyll concentration of single leaves. However, these devices still have one major shortcoming: They can only measure the chlorophyll concentration on one single spot on the leaf at a time. But depending on the species the chlorophyll concentration tends to vary significantly over the leaf. To overcome this shortcoming, we developed our PlantAnalyzer which offers better spatial resolution of the NDVI values and hence the chlorophyll concentration. Its technical realization and precision shall be elaborated in the following article.

Relationships between reflectance and absorbance chlorophyll indices with RGB (Red, Green, Blue) image components in seedlings of tropical tree species at nursery stage

Methods based on RGB (Red, Green, Blue) image segmentation may emerge as a new and low-cost method for estimation the quality of tree seedlings. However, the vast number of indexes based on the use of the RGB image segmentation and the lack of references in the literature still hinder the widespread use of this technology. Thus, we conducted a study aiming to test the relationships between methods based on absorbance and reflectance, widely used for the estimation of chlorophyll contents and physiological status of trees, and ten indexes based on RGB component analysis. We used leaves of five tropical tree species, belonging to different botanical families. Leaf absorbance was measured using the handheld chlorophyll meter SPAD-502, reflectance was measured using a spectrometer and the RGB indices were obtained from digitalized images of the leaves using a flatbed scanner. Modified linear regression models including all five species were used to relate RGB indices to absorbance and reflectance indices. Data collected from leaves of seedlings of five tropical tree species indicated that digital image processing technology can be a useful and rapid nondestructive method for assessment of physiological status of tree seedlings at nursery stage. Among the RGB indexes tested in this study the R, 2R*(G − B)/(G + B) and 2G*(G − B)/(G + B) are the most promising for analysis the tropical seedlings physiological status and quality.

Relationship between SPAD value and grain yield can be affected by cultivar, environment and soil nitrogen content in wheat

SPAD-502 (Minolta Ltd, Osaka Japan), a hand-held chlorophyll meter is widely used in the synchronization of N supply with actual crop demand, however it is also known, that genotype and environment may effect SPAD value. Consequently, the aim of this study was to evaluate the genetic and environmental variation in SPAD value and to determine the relationship between SPAD value at heading (GS 59) and grain yield. Field experiments were conducted in three consecutive cropping seasons between 2012 and 2015 in Hungary and forty winter wheat varieties were tested at two nitrogen levels. Strong significant positive correlation was found between grain yield and SPAD values, but it was highly influenced by cultivars. The proportion of the phenotypical variance explained by the cultivars was different in each growing season and was ranged from 12.50 to 59.04 %. Additionally, it was revealed that the cultivars can be categorised by different SPAD—yield relationship and modern cultivar can be separated into five groups. While same SPAD value can predict different yield level in different cultivars it can be concluded, that SPAD value should be calibrated for cultivar. Based on regression analysis, such an option is also presented here for forty important wheat cultivars. Hence, cultivar specific SPAD value at heading can provide a more accurate estimate of the final yield in wheat.

Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China

Rice farming in Northeast China is crucially important for China’s food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N application at the stem elongation stage in Northeast China. Five farmers’ fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R2 = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments.

In situ estimates of waterhyacinth leaf tissue nitrogen using a SPAD-502 chlorophyll meter

Previous investigations with Eichhornia crassipes and its biological control agents Neochetina bruchi and N. eichhorniae identified leaf tissue nitrogen (N) as a driver in their interactions. However, traditional methods for determining plant tissue N content are cumbersome, time-consuming, and destructive–and thus unsuited for rapid in situ evaluations. We therefore tested the utility of a hand-held chlorophyll meter as a means of producing in situ estimates of N in the leaves of this floating aquatic weed. The Minolta SPAD-502 chlorophyll meter provided excellent estimates (F=385.96, P<0.0001) of leaf tissue N levels. SPAD readings varied within leaves (F=78.66, P<0.0001), so average readings per leaf were used. The relationship between SPAD readings and tissue N levels was affected by the phenological stage of the leaf (F=102.79, P<0.0001), but not leaf size (F=0.75, P=0.3867). The estimates were also unaffected by fertilizer level (F=0.95, P=0.4354), but were marginally affected by herbivory (F=3.86, P=0.0505). Thus, with suitable calibration (e.g., different field sites, presence and type of herbivory) output from the SPAD-502 could be used to provide consistent estimates for the nitrogen content of E. crassipes leaves.

Leaf nitrogen concentration and chlorophyll meter readings as predictors of tall fescue nitrogen nutrition status

Determining the nitrogen nutrition index (NNI) of a crop requires measuring the amount and nitrogen concentration of standing biomass. This limits its use at farm conditions where simplicity and agility are required. In this study, two proxies of NNI were assessed: the nitrogen concentration of leaves located in the sward upper 5–7cm layer (NUSL, gNkg−1 dry matter), and its greenness, as measured by a SPAD 502 handheld chlorophyll meter (SPADUSL, SPAD units). Seven field experiments carried out at Balcarce (Argentina), on two soil types, were conducted from 2008 to 2010, during autumn, late winter/early spring, spring and summer regrowths of tall fescue swards (Lolium arundinaceum (Schreb.) Darbysh.). Different nitrogen application rates were imposed to generate contrasting conditions of nitrogen availability. NNI, NUSL and SPADUSL were simultaneously measured four to seven times during sward regrowths. NUSL was closely associated with NNI. However, the regression parameters changed with elapsed time since sward initial cut. Once developmental effects were accounted for by fitting lineal regressions separately to thermal time intervals related to leaf appearance rate, equation parameters did not differ among years, seasons and sites: NNI=0.024 (NUSL−3.27), R2=0.89, N=78; NNI=0.026 (NUSL−2.37), R2=0.81, N=108; and NNI=0.030 (NUSL−3.26), R2=0.84, N=102 for swards accumulating, respectively, <260, 260–440, and >440 growing degree days (GDD, base temperature 4°C) since the initial cut. Obtained equation parameters compared well to those reported for other C3 grasses, suggesting that the NNI–NUSL relationship is approximately constant among members of this functional subgroup. Likewise, SPADUSL was highly associated with NUSL and, therefore, with NNI. Developmental effects were also evident, but only two equations arised: NNI=−0.81LN (−0.82LN (SPADUSL/68.42)), R2=0.69, N=72; and NNI=0.035 (SPADUSL–18.50), R2=0.75, N=169, for swards accumulating, respectively, <260 or >260 GDD since initial cut. The effect of the higher intrinsic variability of SPADUSL on its NNI predictive precision can be compensated increasing its sampling intensity, except at early stages of sward development when higher leaf N concentrations derive in the saturation of the chlorophyll meter. Still, SPADUSL emerges as an adequate method for performing an instantaneous, approximated, ‘in the field’ estimation of swards N status, thus allowing a prompt correction of N deficiencies through fertilization.

Early season remote sensing of wheat nitrogen status using a green scanning laser

In-season, spatially variable nitrogen (N) fertilizer applications in agricultural systems can help to maximize crop N use efficiency and minimize N losses via hydrological leaching, runoff, and atmospheric volatilization. N fertilizer management often relies upon measurements of crop spectral reflectance using ground-based optical on-the-go sensors or hand-held chlorophyll meters. However, soil background reflectance can confound on-the-go sensing, especially during early crop growth stages, and hand-held chlorophyll meters are impractical for spatially explicit mapping at the field scale. Scanning laser technology is available that measures the intensity of the reflected laser light plus height information within a mm-scale ground instantaneous field of view at a very high sampling rate (up to 50,000 points s −1 in this study). We examined the ability to quantify foliar N status of spring wheat ( Triticum aestivum L.) using a green (532 nm) terrestrial laser scanner during an early stem extension growth stage (Zadoks growth stage 3.2). Laser data were processed by (1) removing soil background returns based on laser-determined height information, (2) standardizing green laser intensity based on white-reference panel readings, and (3) filtering noisy laser returns from leaf edges based on a laser return intensity threshold value. The return intensity of the reflected green laser light more accurately ( r 2 = 0.68, RMSE = 0.30 μg g −1) predicted foliar N concentration than conventional chlorophyll meter readings ( r 2 = 0.36, RMSE = 0.41 μg g −1) and spectral indices measured by a ground optical on-the-go sensor ( r 2 < 0.41, RMSE > 0.39 μg g −1). The results indicate that laser scanners are useful for measuring the N status of wheat during early growth stages, and provide justification for incorporating laser scanner based measurements into developing spatially-explicit estimates of foliar N during this critical growth period. Further research is needed to evaluate the operational practicality of a green scanning laser from a moving platform.

Modeling the relationship between extractable chlorophyll and SPAD-502 readings for endangered plant species research

Summary Handheld chlorophyll meters have proven to be useful tools for rapid, non-destructive assessment of chlorophyll and nutrient status in various agricultural and arborescent plant species. We proposed that a SPAD-502 chlorophyll meter would provide valuable information when monitoring life cycle changes and intraspecific variation in endangered plant populations, whereby, destruction of plants to obtain this information is impractical. Further, use of this instrument would augment leaf morphometric measurements collected during controlled studies, circumventing the need for leaf harvest. We developed a regression model relating foliar chlorophyll concentration and content to SPAD chlorophyll content indices for a genetically diverse population of the federally listed Lindera melissifolia. Application of the regression to four additional L. melissifolia populations, and to the ecologically widespread congener L. benzoin, proved the SPAD-502 to be an effective tool for non-destructive estimation of total foliar chlorophyll concentration (r2=0.8230) and content (r2=0.9029) across a range of plant ages, growing conditions, and genotypes.

Potato Crop Nitrogen Status Assessment to Improve N Fertilization Management and Efficiency: Past–Present–Future

Improving nitrogen use efficiency is important for the potato crop, because of its relatively low ability to take up available soil mineral nitrogen (N). Splitting of N fertilizer application is a suitable approach to better match N need and supply. In-season crop N monitoring methods are therefore required to support such strategies. This paper deals with the state of the art and potential development of characteristics, use and implementation of well known and more recent methods aimed to assess in-season potato Crop Nitrogen Status (CNS). A short overview of this concept is given for the potato crop. The most important and available methods for CNS assessment are evaluated for their accuracy, precision, sensitivity, sensibility and feasibility. These are: the petiole sap nitrate concentration test; the leaf chlorophyll concentration measurement using a hand-held chlorophyll meter; the measurement of crop light reflectance through a hand-held radiometer using passive sensors. More recent methods still under investigation based on near, ground-based, air-borne or space-borne remote sensing are discussed for their scientific and practical interest in the near future. The current and potential use and implementation of these methods into decision support systems for potato N fertilization management aimed at improving the potato crop nitrogen use efficiency are analysed by: comparing relative and raw data; establishing threshold values of CNS; and combining or integrating the CNS values into models dedicated to N recommendation or to crop growth simulation.