Canopy Color Research Articles

Tracking photosynthetic phenology using spectral indices at the leaf and canopy scales in temperate evergreen and deciduous trees

Vegetation photosynthetic phenology is an important indicator to characterize the biological responses of terrestrial carbon cycle to climate change. Remote sensing-derived spectral indices have been used to estimate photosynthetic phenology of terrestrial ecosystems, yet there are large uncertainties due to differences in leaf structure and canopy function between evergreen and deciduous trees. In this study, we used in-situ measured meteorology, the maximum rate of leaf-level CO2 assimilation (Amax), chlorophyll fluorescence and canopy color changes in evergreen and deciduous species to evaluate and compare the abilities of spectral indices for photosynthetic phenology modeling. Seasonal variation in Amax is used as a proxy for photosynthetic phenology. Several indices were used, including the normalized difference vegetation index (NDVI), near-infrared reflectance vegetation index (NIRv), photochemical reflectance index (PRI), chlorophyll/carotenoid index (CCI), green-red vegetation index (GRVI), and green chromaticity coordinates (Gcc). We found that NIRv and PRI estimated phenological periods for Cedrus deodara and three deciduous trees all differed from Amax by less than 5 days, and all were linearly related to Amax (R2 > 0.6). CCI had less potential for Amax modeling, while NDVI had the largest error in predicting Amax. Gcc was a more accurate proxy for photosynthetic phenology than GRVI at the canopy scale. In addition, we found that the variability of different spectral indices in tracking photosynthetic phenology of evergreen and deciduous species was related to non-photochemical quenching processes. Especially for the fraction of absorbed light quenched by dynamic NPQ (ɸNPQ) and the fraction of absorbed light quenched by sustained NPQ (ɸf,D), they contribute more than 40 % to the variations of the NIRv and PRI spectral signals. Therefore, NIRv and PRI provide powerful tools for monitoring photosynthetic phenology, and future exploration of NIRv and PRI at larger spatial scales will favor the global plant phenology and carbon uptake modeling.

Creeping Bentgrass Nutritional, Morphological, and Putting Green Performance Response to Ca/Mg-Silicate Slag Liming Agent

While not classified as an essential plant nutrient, silicon (Si) assimilation following exogenous Si application has enhanced the wear resistance of cool-season turfgrass. Given this beneficial supplementation of lignin by Si reported in epidermal tissue of monocotyledonous plants, our research objective was to quantify root morphology, vegetative nutrition and vigor, soil chemistry, and putting green performance in response to split applications of pelletized liming agents rich in Si and/or Ca and Mg. Field evaluation of granular liming agent treatment, 2441 kg (ha year)−1, was conducted on creeping bentgrass (Agrostis stolonifera L. cv. Penn G-2) putting green maintained in the Mid-Atlantic US. Pelletized Ca/Mg-SiO3 slag or dolomitic limestone treatments were conducted in frequent split applications and incorporated into the upper 5 cm of the rootzone. Measurements of canopy color and density, shoot growth as clipping yield, soil pH, Si and nutrient content of clippings, and soil extractable Si were performed each season. Cumulative Ca/Mg-SiO3 application (kg ha−1) increased mean acetic acid (HOAc) extractable Si by 35 to 60 mg kg−1 and leaf Si content by 1.0 to 1.5 mg g−1. However, neither putting green canopy quality, shoot nutrient concentration, 5 to 15 cm depth root length density nor ball roll distance was improved by liming agent treatment. Liming agent-treated or untreated plots showed statistical, yet inconsistent, differences in clipping yield 4, 14, 15, 16, and 17 months from initiation (MFI). This thorough shuffling of treatment rank, resulting in identical experiment-wide means precludes the expectation of dependably superior vigor by any.

Nondestructive detection of key phenotypes for the canopy of the watermelon plug seedlings based on deep learning

Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding, management, and quality testing. The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient, subjective and destroys samples. Therefore, the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning. The Azure Kinect was used to shoot canopy color images, depth images, and RGB-D images of the watermelon plug seedlings. The Mask-RCNN network was used to classify, segment, and count the canopy leaves of the watermelon plug seedlings. To reduce the error of leaf area measurement caused by mutual occlusion of leaves, the leaves were repaired by CycleGAN, and the depth images were restored by image processing. Then, the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud. The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray. Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height. The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages. The average relative error of measurement is 2.33% for the number of true leaves, 4.59% for the number of cotyledons, 8.37% for the leaf area, and 3.27% for the plant height. The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.

Comparing phenocam color indices with phenological observations of black spruce in the boreal forest

Bud phenology identifies the growing period of trees and determines the pattern of mass and energy exchanges between forest and atmosphere over time and space. Canopy color metrics derived from phenocams have been widely used to investigate tree phenology. However, it remains unclear which color-based index better tracks the seasonal variations of tree phenology in evergreen forest ecosystems. Herein, we compared four color metrics (red chromatic coordinate (RCC), green chromatic coordinate (GCC), vegetation contrast index (VCI) and excess green index (ExG)) derived from phenocam images with bud phenological phases recorded in black spruce [Picea mariana (Mill.) B.S·P] during 2017–2020 at a boreal forest site in Quebec, Canada. Canopy redness (RCC) and greenness (GCC, ExG, and VCI) showed a bimodal and bell-shaped seasonal pattern, respectively. The phases of bud burst and bud set lasted from end-May to end-June and from mid-July to end-September, respectively. The neural network model indicated that GCC had the best predictive ability in capturing the sequential phases of bud phenology. Bud phenological phases predicted by GCC showed the highest correlation with actual bud phenological phases among four indices, with R2 above 0.9 and RMSE lower than 0.5. Overall, color indices performed better when representing bud burst than bud set. Our findings improve the efficiency and confidence of the phenocam greenness index to characterize the growing season of evergreen forests.

Integrated Visual Assessment Method for Infestation by Lebbek Borer (Xystrocera globosa) in Rain Trees of Singapore

"Background: In Singapore, determining the level of infestation by lebbek borer (Xystrocera globosa)(Olivier, 1795)[Coleoptera: Cerambycidae] is the crucial first step in control of this pestra in rain tree (Albizia saman [Jacq.] Merr.)[Fabales: Fabaceae]. Current assessment methods rely on symptoms such as canopy colour, defoliation, dieback, and actual estimation of borer population via counting of larvae or exit holes created by adults. Currently, there is a lack of systematic approach to integrate different tree health indicators and symptoms to quantify infestation level. This gap poses challenges in assessment of treatment efficacy as managers could not quantitatively determine whether infestation level has changed following treatment. Thus, this study aimed to develop a visual assessment method that can integrate all mentioned symptoms to quantify infestation level. Methods: We surveyed a total of 388 rain trees and used principal component analysis (PCA) to investigate the correlation between X. globosa infestation and different borer infestation symptoms. Borer Infestation Score (BIS) formula was developed based on the linear combinations of the statistically significant principal component. Results: Infestation level was strongly associated with bark peeling, exit holes, and proximity of bark peeling and/or exit holes to trunk base and weakly associated with defoliation, dieback, and canopy colour. Developed BIS formula generated numerical values that distinguished between noninfested and infested trees, reflected infestation level in surveyed areas and temporal progression of infestation. Conclusions: Described integrated visual assessment method can be executed quickly on field. BIS formula generates quantitative scores easy to be interpreted, tracked, and compared.

Response of Population Canopy Color Gradation Skewed Distribution Parameters of the RGB Model to Micrometeorology Environment in Begonia Fimbristipula Hance

The high quality and efficient production of greenhouse vegetation depend on micrometeorology environmental adjusting such as system warming and illumination supplement. In order to improve the quantity, quality, and efficiency of greenhouse vegetation, it is necessary to figure out the relationship between the crop growth conditions and environmental meteorological factors, which could give constructive suggestions for precise control of the greenhouse environment and reduce the running costs. The parameters from the color information of the plant canopy reflect the internal physiological conditions, thus, the RGB model has been widely used in the color analysis of digital pictures of leaves. We take photographs of Begonia Fimbristipula Hance (BFH) growing in the greenhouse at a fixed time every day and measure the meteorological factors. The results showed that the color scale for the single leaf, single plant, and the populated canopy of the BFH photographs all have skewed cumulative distribution histograms. The color gradation skewness-distribution (CGSD) parameters of the RGB model were increased from 4 to 20 after the skewness analysis, which greatly expanded the canopy leaf color information and could simultaneously describe the depth and distribution characteristics of the canopy color. The 20 CGSD parameters were sensitive to the micrometeorology factors, especially to the radiation and temperature accumulation. The multiple regression models of mean, median, mode, and kurtosis parameters to microclimate factors were established, and the spatial models of skewness parameters were optimized. The models can well explain the response of canopy color to microclimate factors and can be used to monitor the variation of plant canopy color under different micrometeorology.

Temporal Recovery of Polymer-Coated Urea-N by Kentucky Bluegrass in the Field

Relative to soluble N sources, controlled release fertilizer (CRF) fosters consistent turfgrass growth response and improved canopy quality while reducing N loss as nitrate, ammonia, and/or N2O from target systems. Commercial CRFs afford turfgrass managers greater operational efficiency and flexibility in nutrient management planning and compel the investigation of application rate thresholds to guide regional agencies tasked with their regulation. The experimental objective was to systematically evaluate, under an array of field conditions, Kentucky bluegrass (Poa pratensis L.) vigor/yield, fertilizer N offtake, canopy density, and canopy color temporal response to a single application of granular N fertilizer made at practical rates. In May of 2014 and 2015, plots within a mature Kentucky bluegrass system were fertilized by conventional urea or Duration 45 polymer coated urea (PCU) at a N rate of 43.9 kg·ha−1 (0.9 lbs N·1000 ft−2); or PCU (Duration 90, Duration 120, or 43% N Polyon) at a N rate of 87.8 kg·ha−1 (1.8 lbs N·1000 ft−2). Resulting measures of the described dependent variables proved similar over both growing seasons and were highly dependent on the N rate and PCU attribute. Following 18-week evaluations, the average total percent fertilizer N recoveries from conventional urea, Duration 45, Duration 90, Duration 120, and Polyon (43% N) were 63%, 87%, 82%, 78%, and 77%, respectively. Temporal release among commercial PCU fertilizers indicates varying suitability by commodity and seasonal nutrient requirements. Hypothesis tests on experiment-end unaccounted fertilizer N totals show one 87.8 kg N·ha−1 application of the described 100% PCU fertilizer treatments poses no greater environmental risk than a 43.9 kg N·ha−1 application of conventional urea fertilizer.

Canopy Density Estimation of Apple Trees

Tree canopy density is an important parameter in developing a decision support system for precision orchard management including application of the right amount of nutrients at the right time and right location. Previous studies mostly focus on canopy characterization using the light detection and ranging (LiDAR) sensor which lacks critical color and texture information. This study utilized a ground-based stereo-vision sensor mounted on a utility vehicle to capture the canopy data during the growing stage (July, 2021) in a commercial orchard. The acquired color images, along with the point-cloud data, was used to segment out individual trees and estimate canopy density which is the measure of canopy cover per unit total area for each tree. A K-means segmentation method followed by depth thresholding was used to segment the desired tree canopies. The segmentation was compared to the manual segmentation and a F1 score of 0.78 was obtained. The density was obtained using the ratio of pixel count of vegetation and total area of interest around the trunk of specific trees. The obtained result was compared against the expert's assessment of tree vigor (categorical variable with values from 1 to 5), which showed a good correlation (R2 = 0.81). The obtained canopy density, along with other parameters including trunk size, and canopy color change during fall, will be used in the future to develop a decision support system for the assessment of nutrient requirement for individual trees to achieve the plant level precision nutrient management.

Performance of diploid and interploid hybrids of St. Augustinegrass under moderate shade

AbstractShadetolerance is an important factor for cultivar selection in urban areas. Diploid (2n = 2x = 18) cultivars of St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] with moderate to high shade tolerance have been popular choices for residential and commercial landscapes in the southern United States. This study evaluated 26 elite drought resistant diploid and interploid (polyploid × diploid) hybrids and four commercial cultivars under 75% continuous shade from 20 June 2017 through 30 Oct. 2018. Biweekly data were collected during the growing season for percent green cover, visual turfgrass quality, canopy color, shoot density, and daily leaf elongation rates (DER; mm d–1). Spring greenup was rated annually, and to gray leaf spot incidence was rated when naturally occurring. All genotypes lost green cover through 2017, and greenup was delayed transitioning into the summer of 2018 when most diploids struggled to recover from disease, shade, and winter injury whereas, on an average, interploid hybrids reached up to 84.0% green cover. Daily leaf elongation rates were lower for diploids than polyploids in both years. Turfgrass quality, color, and density generally declined under shade as expected. Overall, three interploid hybrids (DALSA 1329, 1404, and 1406) were the top performers demonstrating good retention of turfgrass performance traits and reduced DER under shade.

Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level

BackgroundAnoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis. The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.MethodsMultispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Classification And Regression Tree (CART), were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis.ResultsThe confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis’ damage stages.ConclusionsA novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China.

Yield estimation of soybean breeding lines under drought stress using unmanned aerial vehicle-based imagery and convolutional neural network

Crop yield is a primary trait to select superior genotypes and evaluate breeding efficiency in breeding programs. Crops with high yield potential are usually selected from numerous breeding lines in multiple years and locations. However, the efficiency of conventional breeding programs is limited by the capacity of field phenotyping, which can be improved by developing high-throughput field phenotyping systems using emerging technologies, including Unmanned Aerial Vehicle (UAV) imagery and deep learning technologies. The goal of this study was to evaluate the performance of a UAV imaging system and convolutional neural network (CNN) in estimating yield of soybean breeding lines. In this study, 972 soybean breeding lines in three maturity groups were planted under rainfed conditions for testing their drought tolerance. Aerial images were taken at the late vegetation (V6), early (R1), and late reproductive (R6-R8) growth stages. Seven image features associated with plant height, canopy colour, and canopy texture were selected to estimate the yield of each breeding line. A mixed CNN model was built to estimate soybean yield by taking the seven image features and two categorical factors, i.e. maturity group and drought tolerance, as predictors. Results show that image features collected at the early and late reproductive growth stages are comparably promising in estimating soybean yield. The prediction model could explain 78% of the measured yield with a root mean square error of 391.0 kg·ha−1 (33.8% to the average yield), indicating that the UAV imagery and deep learning models are promising in estimating yield for soybean breeding purposes.

Seasonal variation in the canopy color of temperate evergreen conifer forests.

Summary Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated.Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics.We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color.These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices.

Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat

BackgroundHigh-throughput phenotyping and genomic selection accelerate genetic gain in breeding programs by advances in phenotyping and genotyping methods. This study developed a simple, cost-effective high-throughput image analysis pipeline to quantify digital images taken in a panel of 286 Iran bread wheat accessions under terminal drought stress and well-watered conditions. The color proportion of green to yellow (tolerance ratio) and the color proportion of yellow to green (stress ratio) was assessed for each canopy using the pipeline. The estimated tolerance and stress ratios were used as covariates in the genomic prediction models to evaluate the effect of change in canopy color on the improvement of the genomic prediction accuracy of different agronomic traits in wheat.ResultsThe reliability of the high-throughput image analysis pipeline was proved by three to four times of improvement in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covariates in the univariate genomic selection models. The higher prediction accuracies were attained for days to maturity when both tolerance and stress ratios were used as fixed effects in the univariate models. The results of this study indicated that the Bayesian ridge regression and ridge regression-best linear unbiased prediction methods were superior to other genomic prediction methods which were used in this study under terminal drought stress and well-watered conditions, respectively.ConclusionsThis study provided a robust, quick, and cost-effective machine learning-enabled image-phenotyping pipeline to improve the genomic prediction accuracy for days to maturity in wheat. The results encouraged the integration of phenomics and genomics in breeding programs.

Phenotypic characteristics of ramie (Boehmeria nivea L) germplasm resources based on UAV remote sensing

Ramie (Boehmeria nivea L.) is one of the main fiber crops in China, integrated with textile, forage, building materials and soil conservation. Germplasm resources of ramie in China is rich, accordingly it is of great significance to study the diversity of phenotypic characters. UAV near-air remote sensing can grasp farmland information in real time and play an increasingly important role in the collection of crop phenotype information. In this study, visible remote sensing images of ramie were acquired by UAV and leaf images were acquired by digital camera. Eight ramie leaf features and 15 canopy color texture features were obtained by image processing. Combined with ramie growth characters, the phenotypic characters of 63 ramie germplasm resources were studied. The results showed that the germplasm resources of ramie had rich variation and wide diversity. The variation coefficients of the 10 growth characters ranged from 10.46% to 47.63%, and the leaf shape, petiole color and root shape varied greatly. The diversity of plant number, plant height and stem diameter was strong. The variation coefficients of leaf shape, canopy color and texture characters were distributed between 1.56% and 30.74%, and the diversity index was distributed between 0.98 and 1.50. Among canopy color characters, RGBVI value variation, saturation (s) genetic diversity index and high variation coefficient can be estimated to be the largest, which can better reflect the difference of different kinds of materials. The entropy (Hom) variation coefficient of ramie growth potential was the highest among the canopy texture characters. Seven principal components were obtained by principal component analysis of 24 characters, with a cumulative contribution rate of 90.48%, which could effectively reflect the main information of each trait. The cluster analysis of 24 phenotypic characters divided 63 experimental materials into four categories.

Modeling canopy senescence to calculate soybean maturity date using NDVI

AbstractSoybeans [Glycine max (L.) Merr.] are the second largest crop in the United States, and cultivar development is important to improve productivity on existing farms. High‐throughput phenotyping can assist breeders in characterizing new lines particularly for phenotypic traits of interest such as crop canopy color, leaf distribution, and seed yield. However, current methods for characterizing the maturity date of soybeans needs to be improved to further leverage high‐throughput phenotyping platforms. The objective of this study was to examine if relative differences in leaf senescence patterns of soybean cultivars could be used to accurately calculate the observed maturity date. A field study was conducted in 2016–2017 at two locations in Ohio. Leaf canopy senescence was measured using the normalized difference vegetation index (NDVI), and observed maturity was measured for each cultivar. Senescence was modeled for each cultivar, and relative differences in senescence calculated from NDVI reductions were compared with the relative differences in observed maturity for each cultivar. The relative differences between cultivars in the date when NDVI was reduced by 85% during senescence (compared with NDVI at the R5 growth stage) were accurate to calculate maturity differences to within 1.5 d of the observed values for 21 of the 24 cultivar × site‐year combinations. This practice has potential to contribute additional data to enhance the high‐throughput phenotyping process using existing equipment (or data being collected) in many programs.

Vegetation indices derived from digital images and stable carbon and nitrogen isotope signatures as indicators of date palm performance under salinity

Date palm is frequently irrigated with brackish water. Developing tools to monitor salinity effects at the single-tree level may assist agronomy and phenotyping. Sixteen elite varieties were grown for 15 years under irrigation with three levels of saline water (5, 10 and 15 dS m−1) at the International Center for Biosaline Agriculture (UAE). Trunk length and diameter, number of branches, and fruit yield per tree were recorded. Different vegetation indices were calculated from single tree-top images taken from the ground with an RGB (Red/Green/Blue) camera. These included indices derived from CIE (Commission Internationale de l’Eclairage) color space models; lightness, together with a* and b* dimensions (CIELab) and u* and v* coordinates (CIELuv); and the HSI color space, referring to the components Hue, Saturation and Intensity. Moreover, Green Area (GA) and the Greener Area (GGA) were also formulated. Also canopy temperature (CT) was measured as an indicator of canopy water status with an infrared thermometer. The carbon isotope composition (δ13C), as a time-integrated indicator of water status, and the nitrogen isotope composition (δ15N) and total nitrogen concentration (N), as nitrogen metabolism indicators, were analyzed in leaflet dry matter. Irrigation conditions and genotypes exhibited significant effects for biomass, fruit yield and all the remote sensing and stable isotope traits evaluated. Hue correlated positively, whereas most of the other RGB vegetation indices along with δ13C and CT correlated negatively with biomass and fruit yield across salinities. Leaf N concentration and δ15N did not correlate with biomass and fruit yield across salinities, but were the only traits correlated with genotypic variability in fruit yield within a given salinity level. Traits that describe canopy color characteristics represent affordable tools for monitoring palm growth and productivity under saline irrigation. However, the results do not support the direct use of RGB indices to phenotype genotypic variability.

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

Relationship between gross primary production and canopy colour indices from digital camera images in a rubber (Hevea brasiliensis) plantation, Southwest China

The widespread increase in the number of digital cameras mounted on flux towers provides an opportunity to better understand the relationship between the seasonality of canopy photosynthesis and canopy phenology. The challenge is due to fewer variations in rubber defoliation of rubber canopy. We examined the relationship between colour indices calculated from digital camera images and gross primary production (GPP) obtained from daily flux tower observations of carbon dioxide over two years in a rubber plantation and used these colour indices to model GPP. According to the results, (1) the strength of green (Sgreen), Hue and GPP exhibited clear seasonal patterns in the rubber plantation, and the relationship between the camera-based indices and GPP appeared to have distinct characteristics at different phenological stages; (2) the peak GPP from the flux tower measurements lagged behind the peak in the colour indices calculated from digital camera imagery; (3) GPP was strongly correlated with Sgreen derived from camera imagery in the rubber plantation, especially in the leaf expansion period; and (4) the GPP simulated by colour indices (Sgreen and Hue) was underestimated, and the fraction of absorbed photosynthetically active radiation (FPAR) was the best parameter for modelling GPP in normal years. Our results indicate that colour indices calculated from digital camera images can be used to model GPP in rubber plantations and to monitor biotic and abiotic stress events. Future research should measure the pigment contents of canopy leaves to precisely quantify the relationship between colour indices and GPP.

Advances in plant nutrition diagnosis based on remote sensing and computer application

Hyperspectral remote sensing, visible light remote sensing and canopy color analysis have been widely concerned for rapid diagnosis of crop growth and nutrition. They are expected to develop into potential nondestructive diagnostic techniques for crop nitrogen nutrition in the new era on account of the advantages of stable, rapid, convenient and nondestructive results, together with the good correlation between canopy color parameter NRI and plant nitrogen nutrition index and yield satisfying the demand for nondestructive diagnosis of nitrogen nutrition, and their feasibility to monitor plant growth status and nitrogen nutrition level in real time and quickly. At present, with the rapid development of remote sensing satellite, unmanned aerial vehicles remote sensing and Internet of things, remote sensing will be more and more widely used in plant nutrition diagnosis.