Reflectance-based Methods Research Articles

The balance between spectral and spatial information to estimate straw cereal plant density at early growth stages from optical sensors

Estimating straw cereal plant density at early stages is important for field crop management and phenotyping. Usual plant density estimation methods include manual counting and image-based counting, both of which have limited throughput, due to the need for high spatial resolution images. In this study, we explored the potential of high-throughput estimations with spectral information. A large and diverse dataset was collected on micro plot field experiments, encompassing six sites, three leaf stages, and four species of straw cereals. Canopy spectral reflectance was acquired with a spectrometer, both in 0° or 45° view zenith angle, perpendicularly to the row direction. Two reflectance-based approaches were then tested. In the direct approach, density was directly estimated from reflectance using Gaussian process regression (GPR) and spectral bands selected based on Akaike’s information criterion. In the indirect approach, the green fraction derived from high spatial resolution RGB images (GF_rgb) was first estimated from reflectance using GPR and selected bands, and then linearly related to density. These reflectance-based methods were compared to a classical image-based baseline method, which estimates density directly from GF_rgb.An ablation study firstly showed the superiority of 45° observations, and the necessity to calibrate one model for each site, growth stage, and species. The band selection process recommended using no more than four bands as inputs to the GPR models. The resulting direct and indirect estimations had an overall relative error of 30 %. The image-based baseline method had a lower error of 22 % for submillimeter spatial resolutions, but it performed worse than reflectance-based methods when degrading the spatial resolution to more than 1 to 2 mm to mimic an increase in sensor altitude. These results showed that spectral information can compensate for spatial information and that spectral methods can potentially provide high-throughput and reasonably accurate estimates of straw cereal plant density.

Building of mid-infrared spectral signature of pesticides using functionally-enhanced derivative spectroscopy (FEDS)

Pesticides are synthetic substances widely used in several processes related to agriculture-based food production. Their monitoring is important to evaluate their impact on environment, ensure the food quality, and for the developing of new methods and strategies that help to understand their dynamics from the moment they are released until their end disposal. In this way, the use of mid-infrared spectroscopy (mid-IR spectroscopy) and chemometric methods have been widely explored. However, the strong overlap of spectral signals in complex analytical matrices is recognized as the main limitation of reflectance-based methods for monitoring of this kind of pollutant. In this work, the use of MID-IR spectroscopy by attenuated total reflectance (ATR), in conjunction with functionally-enhanced derivative spectroscopy (FEDS), is proposed as non-destructive technique for fast and simple monitoring of pesticides. Thus, high-resolution dynamic spectral signature (HDSS) for six pesticides commonly used in tropical crops were used as model substances; as analytical matrix was used the peel of banana (e.g., Musa paradisiaca). In addition, concentration effect was evaluated to mimic real conditions. Data recording was obtained by mid-IR spectroscopy (from 4000 to 600 cm−1) and HDSS were evaluated by Pearson’s correlation coefficient and spectral similarity FEDS index between the spectra and the reference signals. Based on these correlation parameters, some selection criteria are proposed to estimate the spectral zones where the pesticide signature persists even at relative low concentrations. For the six pesticides, the spectral region considered coincide with those bands of high polarity bonds in the pesticide molecules. Additionally, a good linear prediction models that correlate the relative absorbance intensity and the peak area of the mid signal with the pesticide concentration level are obtained in the spectral ranges of interest. The model performance is defined by Pearson’s coefficients rf greater than 0.97, residual prediction deviation RPDs (root mean square errors - RMSEs) greater (lower) than 5 (3), and limits of detection (LODs) lower than 1 g/kg. Furthermore, this methodology and results are successfully employed to detect Chlorpyrifos residues in banana peel.

Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity

High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.

Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance

Leaf mass per area (LMA), leaf dry matter content (LDMC) and leaf water content/ equivalent water thickness (EWT) are commonly used functional plant traits in ecology. Whereas spectroscopy has recently proven to be a powerful tool to collect such functional trait information across large scales, it remains unclear whether these reflectance-based trait predictions are accurate enough to reliably model trait variation at the intraspecific level (i.e. across individuals of one species). We explored the potential of hyperspectral leaf reflectance-based methods to predict LMA, LDMC and EWT at the intraspecific level for two herbs (Hieracium umbellatum and Jacobaea vulgaris) and two shrubs (Rosa rugosa and Rubus caesius), based on 2400 leaf samples. More specifically we tested i) inversion of the PROSPECT-D radiative transfer model, ii) a generic PLSR approach using the multibiome LMA PLSR model and iii) a data-specific PLSR approach at the species level. For the latter approach we furthermore assessed both model transferability across species and the trade-off between sample size and model accuracy. Although the PROSPECT-D model inversion and the multibiome LMA PLSR model were relatively accurate for intraspecific LMA predictions of shrubs (R2 > 71 and 76%, respectively, however NRMSE = 33–47%), their performance was lower for herbs (R2 < 61%, NRMSE = 28–50%). PROSPECT-D was furthermore slightly less successful in retrieving EWT at the intraspecific level (R2 < 70%, NRMSE = 16–43%), and unsuccessful in retrieving LDMC through combining LMA and EWT inversion results (R2 < 10%, NRMSE = 9–192%). The highest correlation accuracy was obtained for all three traits with the species-specific PLSR models (R2 > 70%, NRMSE < 10%). If high predictive accuracy is needed, we thus suggest the use of species-specific PLSR models. The training of species-specific PLSR models comes at the cost of a needed sample size of 100–160 leaves however, depending on the trait. Although transferability of species-specific PLSR models seems limited overall, our results suggest potentially high transferability across herbaceous species.

110 Metmyoglobin reducing activity and oxygen consumption properties of atypical dark-cutting beef

Abstract Deviation from characteristic bright-red color during grading leads to discounted carcass price and value. Dark-cutting carcasses are examples of color deviation due to high-pH. However, atypical dark-cutters are not discounted at grading but have a dark color compared to normal-pH beef. Limited knowledge is currently available on the biochemical properties of atypical dark cutting beef compared with normal-pH beef. Therefore, the objective of the study was to determine metmyoglobin reducing activity and oxygen consumption of atypical dark-cutting beef. Strip loins from atypical dark-cutting (ADC; selected based on HunterLab L* values and pH less than 5.8 and not discounted by grader) and USDA Low Choice (C) carcasses were selected from a commercial beef processing plant and transported to the Food and Agricultural Products Center in Stillwater, Oklahoma. pH, color, oxygen consumption, and metmyoglobin reducing activity of atypical dark cutters and normal pH loins were determined on d3 postmortem. The surface color was determined using a HunterLab Miniscan spectrophotometer. Reflectance-based methods using K/S ratios were utilized to determine oxygen consumption and metmyoglobin reducing activity. The data were analyzed using the Mixed Procedure of SAS and the experiment was replicated 10 times (n = 10). There were no differences in initial pH (C = 5.46 and ADC = 5.6 atypical dark-cutter; P = 0.05) between normal-pH and atypical dark cutters. However, atypical dark cutters had lower lightness (L* values; C = 44.2 and ADC = 38.4), redness (a* values; C = 28.6 and ADC = 25.2), and red intensity (chroma; C = 40.2 and ADC = 36.4) compared with normal-pH. Further, atypical dark cutters had greater (P &amp;lt; 0.05) oxygen consumption and metmyoglobin reducing activity than normal-pH. The results suggest that atypical dark cutters and normal-pH beef have different biochemical properties.

A comparison of ear-canal-reflectance measurement methods in an ear simulator.

Ear-canal reflectance has been researched extensively for diagnosing conductive hearing disorders and compensating for the ear-canal acoustics in non-invasive measurements of the auditory system. Little emphasis, however, has been placed on assessing measurement accuracy and variability. In this paper, a number of ear-canal-reflectance measurement methods reported in the literature are utilized and compared. Measurement variation seems to arise chiefly from three factors: the residual ear-canal length, the ear-probe insertion angle, and the measurement frequency bandwidth. Calculation of the ear-canal reflectance from the measured ear-canal impedance requires estimating the ear-canal characteristic impedance in situ. The variability in ear-canal estimated characteristic impedance and reflectance due to these principal factors is assessed in an idealized controlled setup using a uniform occluded-ear simulator. In addition, the influence of this measurement variability on reflectance-based methods for calibrating stimulus levels is evaluated and, by operating the condenser microphone of the occluded-ear simulator as an electro-static speaker, the variability in estimating the emitted pressure from the ear is determined. The various measurement methods differ widely in their robustness to variations in the three principal factors influencing the accuracy and variability of ear-canal reflectance.

Estimation of Chlorophyll Fluorescence at Different Scales: A Review.

Measuring chlorophyll fluorescence is a direct and non-destructive way to monitor vegetation. In this paper, the fluorescence retrieval methods from multiple scales, ranging from near the ground to the use of space-borne sensors, are analyzed and summarized in detail. At the leaf-scale, the chlorophyll fluorescence is measured using active and passive technology. Active remote sensing technology uses a fluorimeter to measure the chlorophyll fluorescence, and passive remote sensing technology mainly depends on the sun-induced chlorophyll fluorescence filling in the Fraunhofer lines or oxygen absorptions bands. Based on these retrieval principles, many retrieval methods have been developed, including the radiance-based methods and the reflectance-based methods near the ground, as well as physically and statistically-based methods that make use of satellite data. The advantages and disadvantages of different approaches for sun-induced chlorophyll fluorescence retrieval are compared and the key issues of the current sun-induced chlorophyll fluorescence retrieval algorithms are discussed. Finally, conclusions and key problems are proposed for the future research.

A rapid real-time quantification in hybrid paper-polymer centrifugal optical devices

The research progress in the centrifugal microfluidic platform provides great opportunities for simple and effective analytical measurements in a variety of areas including biomedical engineering. In this study, we propose an optical reader that can measure the transmittance in a very sensitive and rapid manner on a hybrid paper-polymer centrifugal disc platform. This device enables real-time monitoring of multiple samples by measuring the absorbance of the light transmitted through the paper integrated on the disc between the light emitting diode (LED) and the photodiode (PD) regardless of the ambient light condition. To confirm its efficiency, we analyzed one of the blood's important indicators, glucose in a successful manner within 10 s without any additional complex image analysis. In addition, we discussed the results by comparing with the reflectance-based methods and with those of the previously reported studies by introducing a figure of merit to evaluate the performance of the assay.

Genome-wide association mapping of soybean chlorophyll traits based on canopy spectral reflectance and leaf extracts.

BackgroundChlorophyll is a major component of chloroplasts and a better understanding of the genetic basis of chlorophyll in soybean [Glycine max (L.) Merr.] might contribute to improving photosynthetic capacity and yield in regions with adverse environmental conditions. A collection of 332 diverse soybean genotypes were grown in 2 years (2009 and 2010) and chlorophyll a (eChl_A), chlorophyll b (eChl_B), and total chlorophyll (eChl_T) content as well as chlorophyll a/b ratio (eChl_R) in leaf tissues were determined by extraction and spectrometric determination. Total chlorophyll was also derived from canopy spectral reflectance measurements using a model of wavelet transformed spectra (tChl_T) as well as with a spectral reflectance index (iChl_T).ResultsA genome-wide associating mapping approach was employed using 31,253 single nucleotide polymorphisms (SNPs) to identify loci associated with the extract based eChl_A, eChl_B, eChl_R and eChl_T measurements and the two canopy spectral reflectance-based methods (tChl_T and iChl_T). A total of 23 (14 loci), 15 (7 loci) and 14 SNPs (10 loci) showed significant association with eChl_A, eChl_B and eChl_R respectively. A total of 52 unique SNPs were significantly associated with total chlorophyll content based on at least one of the three approaches (eChl_T, tChl_T and iChl_T) and likely tagged 27 putative loci for total chlorophyll content, four of which were indicated by all three approaches.ConclusionsResults presented here show that markers for chlorophyll traits can be identified in soybean using both extract-based and canopy spectral reflectance-based phenotypes, and confirm that high-throughput phenotyping-amenable canopy spectral reflectance measurements can be used for association mapping.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-016-0861-x) contains supplementary material, which is available to authorized users.

Spreading algorithm for efficient vegetation detection in cluttered outdoor environments

This paper investigates the problem of detecting vegetation in unstructured environments for guiding an autonomous robot safely, exploiting its mobility capability in a cluttered outdoor environment. The aim is to create an adaptive learning algorithm which performs a quantitatively accurate detection that is fast enough for a real-time application. Chlorophyll-rich vegetation pixels are selected by thresholding vegetation indices, and then are considered as the seeds of a “spread vegetation”. For each seed pixel, a convex combination of color and texture dissimilarities is used to infer the difference between the pixel and its neighbors. The convex combination, trained via semi-supervised learning, models either the difference of vegetation pixels or the difference between a vegetation pixel and a non-vegetation pixel, and thus allows a greedy decision-making process to expand the spread vegetation, so-called vision-based spreading. To avoid overspreading, especially in the case of noise, a spreading scale is set. On the other hand, another vegetation spreading based on spectral reflectance is carried out in parallel. Finally, the intersection part resulting from both the vision-based and spectral reflectance-based methods is added to the spread vegetation. The approach takes into account both vision and chlorophyll light absorption properties. This enables the algorithm to capture much more detailed vegetation features than does prior art, and also give a much richer experience in the interpretation of vegetation representation, even for scenes with significant overexposure or underexposure as well as with the presence of shadow and sunshine. In all real-world experiments we carried out, our approach yields a detection accuracy of over 90%.

Sensing crop nitrogen status with fluorescence indicators. A review

The optimization of nitrogen (N) fertilization is the object of intense research efforts around the world. Overfertilization is commonly used as a form of insurance against uncertain soil fertility level. However, this practice results in lower nitrogen use efficiency, high levels of residual N after harvest, and losses in the environment. Determining an N recommendation that would preserve actual crop requirements, profitability of the farm, and quality of the environment has been subjected to a number of research initiatives with a variable degree of success. On one hand, soil tests are capable of estimating the intensity of N release at any point in time, but rarely the capacity factor over a longer period. On the other hand, in the context of in-season N applications, crops are often considered good integrators of factors such as the presence of mineral N, climatic conditions, soil properties, and crop management. Strategies have been proposed with plant sensor-based diagnostic information for N recommendations, but the sensitivity of reflectance-based parameters alone do not provide complete satisfaction (delayed sensitivity, need of specific chlorophyll, biomass or cover fraction ranges, lack of specificity to the N stress). Fluorescence sensing methods have been used to monitor crop physiology for years, and they may offer solutions for N status diagnosis over reflectance-based methods. In this paper, we review three plant fluorescence components related to four sensing approaches—variable chlorophyll fluorescence, leaf chlorophyll content-related fluorescence emission ratio, blue-green fluorescence, and epidermal screening of chlorophyll fluorescence by phenolic compounds—from the perspective of their relevance to N fertilization management of agricultural crops. We examine the existence of N-induced changes in each case, together with applications and limitations of the approach. Among these approaches, the fluorescence emission ratio method is the most important and the most widely used to date. However, blue-green fluorescence and epidermal screening of chlorophyll fluorescence by phenolic compounds has also received a great deal of attention particularly with the recent commercial release of instruments which can measure in real time and in vivo both the leaf chlorophyll content and several phenolic compounds (anthocyanins, flavonoids, hydroxycinnamic acids). Overall, our conclusion is that fluorescence-based technologies allow for highly sensitive plant N status information, independently from soil interference, leaf area, or biomass status. They also allow for probing not only the chlorophyll status but also other physiological parameters known to react to N fertility conditions. These new parameters have the potential to provide new N status indicators that can be assessed remotely in a precision agriculture context.

Photomicrographs in color

This paper investigates the problem of detecting vegetation in unstructured environments for guiding an autonomous robot safely, exploiting its mobility capability in a cluttered outdoor environment. The aim is to create an adaptive learning algorithm which performs a quantitatively accurate detection that is fast enough for a real-time application. Chlorophyll-rich vegetation pixels are selected by thresholding vegetation indices, and then are considered as the seeds of a “spread vegetation”. For each seed pixel, a convex combination of color and texture dissimilarities is used to infer the difference between the pixel and its neighbors. The convex combination, trained via semi-supervised learning, models either the difference of vegetation pixels or the difference between a vegetation pixel and a non-vegetation pixel, and thus allows a greedy decision-making process to expand the spread vegetation, so-called vision-based spreading. To avoid overspreading, especially in the case of noise, a spreading scale is set. On the other hand, another vegetation spreading based on spectral reflectance is carried out in parallel. Finally, the intersection part resulting from both the vision-based and spectral reflectance-based methods is added to the spread vegetation. The approach takes into account both vision and chlorophyll light absorption properties. This enables the algorithm to capture much more detailed vegetation features than does prior art, and also give a much richer experience in the interpretation of vegetation representation, even for scenes with significant overexposure or underexposure as well as with the presence of shadow and sunshine. In all real-world experiments we carried out, our approach yields a detection accuracy of over 90%.