Decay properties of the $X(3872)$ through the Fierz rearrangement

We systematically construct all the tetraquark currents of J(PC)=1(++) with the quark configurations [c q][cbar qbar], [cbar q][qbar c], and [cbar c][qbar q] (q=u/d). Their relations are derived using the Fierz rearrangement of the Dirac and color indices, through which we study decay properties of the X(3872) under both the compact tetraquark and hadronic molecule interpretations. We propose to search for the X(3872) -> χc0-π, ηc-π-π, and χc1-π-π decay processes in particle experiments.

A Low-Cost Digital Colorimetry Setup to Investigate the Relationship between Water Color and Its Chemical Composition

Developments in digital image acquisition technologies and citizen science lead to more water color observations and broader public participation in environmental monitoring. However, the implications of the use of these simple water color indices for water quality assessment have not yet been fully evaluated. In this paper, we build a low-cost digital camera colorimetry setup to investigate quantitative relationships between water color indices and concentrations of optically active constituents (OACs). As proxies for colored dissolved organic matter (CDOM) and phytoplankton, humic acid and algae pigments were used to investigate the relationship between water chromaticity and concentration. We found that the concentration fits an ascending relationship with xy chromaticity values and a descending relationship with hue angle. Our investigations permitted us to increase the information content of simple water color observations, by relating them to chemical constituent concentrations in observed waters.

Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery

Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R2 = 0.76–0.93) with wheat grain yield. They gave the highest prediction power (R2 = 0.92–0.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R2 = 0.52–0.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R2 = 0.00–0.40 and R2 = 0.55–0.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R2 = 0.93, RMSE = 32.18 g/m2 and R2 = 0.89, RMSE = 39.82 g/m2) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.

Prediction of Ratoon Sugarcane Family Yield and Selection Using Remote Imagery

Remote sensing techniques and the use of Unmanned Aerial Systems (UAS) have simplified the estimation of yield and plant health in many crops. Family selection in sugarcane breeding programs relies on weighed plots at harvest, which is a labor-intensive process. In this study, we utilized UAS-based remote sensing imagery of plant-cane and first ratoon crops to estimate family yields for a second ratoon crop. Multiple families from the commercial breeding program were planted in a randomized complete block design by family. Standard red, green, and blue imagery was acquired with a commercially available UAS equipped with a Red–Green–Blue (RGB) camera. Color indices using the CIELab color space model were estimated from the imagery for each plot. The cane was mechanically harvested with a sugarcane combine harvester and plot weights were obtained (kg) with a field wagon equipped with load cells. Stepwise regression, correlations, and variance inflation factors were used to identify the best multiple linear regression model to estimate the second ratoon cane yield (kg). A multiple regression model, which included family, and five different color indices produced a significant R2 of 0.88. This indicates that it is possible to make family selection predictions of cane weight without collecting plot weights. The adoption of this technology has the potential to decrease labor requirements and increase breeding efficiency.

Genotypic and Environmental Variations in Kernel Color Indices in the Main Almond (Prunus dulcis (Mill.) D.A. Webb) Cultivars Grown in North-Eastern Morocco

Color indices are important quality traits that define the consumer visual acceptance and agroindustrial preferences. Little is known regarding these properties in the commercial almond cultivars grown in Morocco. This work aimed at investigating kernel color indices in five cultivars, namely, “Fournat de Brézenaud,” “Tuono,” “Ferragnès,” “Ferraduel,” and “Marcona.” Color indices consisted in the following: brightness (L∗), redness index (a∗), yellowness index (b∗), chroma (C∗), hue (H∗), and metric saturation (S∗). Measurements were performed over three consecutive growing seasons (2016–2018) across five different sites from northern (Aknoul, Bni Hadifa, and Tahar Souk) and eastern (Rislane and Sidi Bouhria) Morocco. All factors (cultivar, growing season, and site) affected significantly studied color properties; however, genotype was the main variability source. Wide variabilities were found among cultivars. “Marcona” showed the highest L∗, while “Ferragnès” and “Ferraduel” displayed greater scores of a∗, b∗, C∗, and S∗. Sidi Bouhria presented the lowest L∗ but higher a∗, H∗, and S∗. Moreover, Bni Hadifa displayed higher L∗, b∗, and C∗. 2016 (drier growing season) had the highest values of most indices. Principal component analyses (PCA) discriminate all factors through the first three components: PC1 (61%, genetic component) and PC2 (30%) and PC3 (7%) which were of environmental nature since they separate sites and growing seasons, respectively. Despite environmental effects, we suggested a possible discrimination among the studied cultivars based on their kernel color indices. Drought conditions during fruit development seemed to improve kernel quality via synthesis of pigments resulting in higher a∗ and b∗.

Smartphone-assisted real-time estimation of chlorophyll and carotenoid contents in spinach following the inversion of red and green color features

PurposeChlorophyll (Chl) content is a reliable indicator of leaf nitrogen content and plant health status. Currently available methods for image-based Chl estimation require complex mathematical derivations and high-throughput imaging set-up along with multiplex image-preprocessing steps. Further, the influence of carotenoid (CAR) content has been largely ignored in the process. The present study describes a smartphone-based leaf image analysis method for real-time estimation of Chl content and Chl/CAR ratio.MethodsColor features were obtained from RGB (red, green, blue) images of spinach leaves using a smartphone, and inverse R and G values were calculated. Correlation analysis of color indices and photosynthetic pigment (PP) contents was performed, followed by principal component analysis (PCA). Linear mathematical modeling was performed for describing regression equations for predicting PP contents.Results1/R and 1/G showed strong positive linear correlation (0.93 < r2 < 0.96) with Chl and CAR contents, respectively. Furthermore, 1/R+1/G and [1/R]/[1/G] presented strong positive linear correlation with Chl + CAR (r2 = 0.95) and Chl/CAR (r2 = 0.88), respectively. PCA confirmed the association of color indices with the respective PP features, which were subsequently estimated using the correlation models. A smartphone-based companion application was developed using the linear models for non-invasive, real-time estimation of Chl content and Chl/CAR ratio.ConclusionThe ratios 1/R and 1/G indicate the contents of Chl and CAR via linear models. The smartphone application developed using the linear models enables real-time estimation of Chl content and Chl/CAR ratio without complicated image preprocessing steps or mathematical derivations.

Development of smartphone-based imaging techniques for the estimation of chlorophyll content in lettuce leaves

Leaf color is a good indicator of plant’s health status. In this study, a new image acquisition technique was developed to estimate chlorophyll content of lettuce leaves. The images of lettuce leaves grown under artificial light were acquired using a smartphone. Leaves images was captured by directly attached the leaves to the camera lens with the aid of background illumination from SMD LED. Red, green, blue (RGB) color indices were extracted from leaves color images and some vegetation indices were also calculated. Then, the correlation between these indices and chlorophyll content obtained from SPAD502 chlorophyll meter were evaluated. Significant correlation was found between all the image indices and chlorophyll content with the R2 ranging from 0.63 to 0.85 except for G and B indices from RGB component. Highly significant correlation was found between vegetation indices (VI) and chlorophyll content (R2 = 0.85) with the lowest root mean square error (RMSE) of 8.07 g of chlorophyll/100 g fresh tissue. This demonstrated that the chlorophyll content of lettuce leaves can be successfully estimated using regular smartphone with added background light illumination from SMD LED.