Hyperspectral Fluorescence Research Articles

Water Stress Early Detection of Eggplant Plants by Hyperspectral Fluorescence Spectroscopy

Water stress early detection is essential for precision farming to improve crop productivity and product quality. The methods usually used are destructive, long and expensive. In this work, we used hyperspectral chlorophyll fluorescence technology as a rapid, non-destructive approach to detect the water deficiency of eggplant plants using their spectral footprint. So, an experiment was made on 54 eggplant plants subjected to three water treatments: normal irrigation (T100), intermediate irrigation (T50) and no irrigation (T0). The fluorescence spectra were acquired in vivo and in situ using a USB4000 spectrometer from Ocean optics. For the classification of the plants subjected to three water treatments, we used three pretreatments of the raw hyperspectral data in order to suppress the non-informative variability present in these spectra and to obtain robust models. These are the Savitzky-Golay smoothing (SG), the standard normal variable (SNV) and the first derivative of Savitzky-Golay (SG-D1). The preprocessed data were then subjected to two partial least squares discriminant analyses (PLS-DA): Hard PLS-DA and Soft PLS-DA. These statistical approaches are suitable for large samples as it reduces the dimensionality of the data but improves the accuracy of the prediction. The SG-D1 combined with the Soft PLS-DA gave the best discrimination of plants with scores of sensitivity, specificity and total efficiency respectively of 97.33%, 94% and 95% for calibration, 6 days after hydric stress induction. For the plants used for the prediction, the scores are 86%, 91% and 90% respectively. This study shows that hyperspectral chlorophyll fluorescence spectroscopy is a fast and non-destructive technology allowing early detection of water stress in eggplant plants.

Detection of Rice Yellow Mottle at the Asymptomatic Stage by Hyperspectral Fluorescence and Reflectance Spectroscopies

Detection of Rice Yellow Mottle at the Asymptomatic Stage by Hyperspectral Fluorescence and Reflectance Spectroscopies

Evaluation of phenotypic and photosynthetic indices to detect water stress in perennial grass species using hyperspectral, multispectral and chlorophyll fluorescence imaging

Evaluation of phenotypic and photosynthetic indices to detect water stress in perennial grass species using hyperspectral, multispectral and chlorophyll fluorescence imaging

Using Colour as a Marker for Coral ‘Health’: A Study on Hyperspectral Reflectance and Fluorescence Imaging of Thermally Induced Coral Bleaching

Rising oceanic temperatures create more frequent coral bleaching events worldwide and as such there exists a need for rapid, non-destructive survey techniques to gather greater and higher definition information than that offered by traditional spectral based monitoring systems. Here, we examine thermally induced laboratory bleaching of Montipora capricornis and Montipora confusa samples, utilising hyperspectral data to gain an understanding of coral bleaching from a spectral standpoint. The data revealed several characteristic spectral peaks that can be used to make health determinations. The fluorescence peaks are attributed to fluorescent proteins (FPs) and Chlorophyll-a fluorescence. The reflectance peaks can be attributed to Chlorophyll absorption and accessory pigments such as Peridinin and Diadinoxanthin. Each characteristic spectral peak or ‘marker’ allows for observation of each aspect of coral health and hence, simultaneous monitoring of these markers using hyperspectral imaging techniques provides an opportunity to better understand the processes occurring during bleaching and the rates at which they occur relative to one another.

Experimental Tests for Fluorescence LIDAR Remote Sensing of Submerged Plastic Marine Litter

Marine plastic litter has become a global challenge, affecting all regions of the planet, with massive plastic input to the marine environment every year. Novel remote sensing methods can greatly contribute to face this complex issue with their ability to provide large-scale data. Here we present experimental tests exploring the potential of the hyperspectral fluorescence LIDAR technique for the detection and characterization of plastics when plunged into a layer of natural water. The experiments were carried out in the laboratory by using an in-house developed fluorescence hyperspectral LIDAR with 355 nm excitation from a distance of 11 m on weathered commercial plastic samples plunged into natural water. Results showed the capability of the technique to detect the fluorescence features of several types of plastics, also when plunged into water, and to decouple it from the fluorescence due to colored dissolved organic matter and from Raman scattering due to water molecules. Discrimination of plastics against other marine debris, e.g., vegetation and wood, has also been discussed. The study lays a basis for fluorescence LIDAR remote sensing of plastics in marine environment and paves the way to the detection of MPL also in conditions (e.g., submerged or transparent plastics) that are likely to be challenging by using other passive remote sensing techniques.

3D-Printed Fluorescence Hyperspectral Lidar for Monitoring Tagged Insects

Insects play crucial roles in ecosystems, and how they disperse within their habitat has significant implications for our daily life. Examples include foraging ranges for pollinators, as well as the spread of disease vectors and pests. Despite technological advances with radio tags, isotopes, and genetic sequencing, insect dispersal and migration range remain challenging to study. The gold standard method of mark-recapture is tedious and inefficient. This paper demonstrates the construction of a compact, inexpensive hyperspectral fluorescence lidar. The system is based on off-the-shelf components and 3D printing. After evaluating the performance of the instrument in the laboratory, we demonstrate its efficient range-resolved fluorescence spectra in situ. We present daytime remote ranging and fluorescent identification of auto-powder-tagged honey bees. We also showcase range-, temporally- and spectrally-resolved free-flying mosquitoes, which were tagged through feeding on fluorescent-dyed sugar water. We conclude that violet light can efficiently excite administered sugar meals imbibed by flying insects. Our field experiences provide realistic expectations of signal-to-noise levels, which can be used in future studies. The technique is generally applicable and can efficiently monitor several tagged insect groups in parallel for comparative ecological analysis. This technique opens up a range of ecological experiments, which were previously unfeasible.

Nondestructive Testing Model of Mango Dry Matter Based on Fluorescence Hyperspectral Imaging Technology

The dry matter test of mango has important practical significance for the quality classification of mango. Most of the common fruit and vegetable quality nondestructive testing methods based on fluorescence hyperspectral imaging technology use a single algorithm in algorithms such as Uninformative Variable Elimination (UVE), Random Frog (RF), Competitive Adaptive Reweighted Sampling (CARS) and Continuous Projection Algorithm (SPA) to extract feature spectral variables, and the use of these algorithms alone can easily lead to the insufficient stability of prediction results. In this regard, a nondestructive detection method for the dry matter of mango based on hyperspectral fluorescence imaging technology was carried out. Taking the ‘Keitt’ mango as the research object, the mango samples were numbered in sequence, and their fluorescence hyperspectral images in the wavelength range of 350–1100 nm were collected, and the average spectrum of the region of interest was used as the effective spectral information of the sample. Select SPXY algorithm to divide samples into a calibration set and prediction set, and select Orthogonal Signal Correction (OSC) as preprocessing method. For the preprocessed spectra, the primary dimensionality reduction (UVE, SPA, RF, CARS), the primary combined dimensionality reduction (UVE + RF, CARS + RF, CARS + SPA), and the secondary combined dimensionality reduction algorithm ((CARS + SPA)-SPA, (UVE + RF)-SPA) and other 12 algorithms were used to extract feature variables. Separately constructed predictive models for predicting the dry matter of mangoes, namely, Support Vector Regression (SVR), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN) model, were used; The results show that (CARS + RF)-SPA-BPNN has the best prediction performance for mango dry matter, its correlation coefficients were RC2 = 0.9710, RP2 = 0.9658, RMSEC = 0.1418, RMSEP = 0.1526, this method provides a reliable theoretical basis and technical support for the non-destructive detection, and precise and intelligent development of mango dry matter detection.

Denoising multiplexed microscopy images in n-dimensional spectral space.

Hyperspectral fluorescence microscopy images of biological specimens frequently contain multiple observations of a sparse set of spectral features spread in space with varying intensity. Here, we introduce a spectral vector denoising algorithm that filters out noise without sacrificing spatial information by leveraging redundant observations of spectral signatures. The algorithm applies an n-dimensional Chebyshev or Fourier transform to cluster pixels based on spectral similarity independent of pixel intensity or location, and a denoising convolution filter is then applied in this spectral space. The denoised image may then undergo spectral decomposition analysis with enhanced accuracy. Tests utilizing both simulated and empirical microscopy data indicate that denoising in 3 to 5-dimensional (3D to 5D) spectral spaces decreases unmixing error by up to 70% without degrading spatial resolution.

(Invited) Hyperspectral Detection of the Fluorescence Shift between Enantiomers of Empty and Water-Filled Single-Wall Carbon Nanotubes

The unusually chiral-structure-dependent properties of single-wall carbon nanotubes (SWCNTs) are also strongly influenced by their internal and external environment, which can therefore be investigated through optical spectroscopy.[1,2] In order to study this influence in detail, we developed a hyperspectral IR fluorescence imaging setup based on a microscope with a liquid crystal tunable filter. By resolving the spectra of individual SWCNTs, and even along the length of SWCNTs, the effect of inhomogeneous broadening is largely eliminated, and spectral details can be resolved which are inaccessible in bulk spectroscopy. An automated image processing scheme is used to obtain statistics on large numbers of individual SWCNTs. In particular, we show that not only the spectral shift in emission between empty and water-filled[1] chirality sorted SWCNTs can be resolved, but even separate emission peaks are observed for the left- and right-handed enantiomers, which interact slightly differently with the chiral surfactant with which they are coated. The approach is particularly promising for the quantification of enantio-selective separation results.[1] W. Wenseleers et al., Adv. Mater. 19, 2274 (2007); S. Cambré and W. Wenseleers, Angew. Chem. 50, 2764 (2011); S. Cambré et al., ACS Nano 6, 2649 (2012). [2] J. Campo et al., ACS Nano 2021, 15, 2301−2317.

Abstract 2461: Integrated lifetime and spectral phasor imaging of biomarker expression and metabolism in hormone receptor positive breast cancer models

Abstract In HR+ (hormone receptor positive) breast cancer, endocrine therapies that directly antagonize estrogen receptor or reduce synthesis of its ligand can be highly effective, but endocrine-resistance occurs frequently. Metabolic dysregulation is a mechanistic driver of endocrine-resistant HR+ breast cancer, but most existing methods cannot be multiplexed with assays for biomarker expression. Spectral phasor imaging of multiplex immunofluorescence (mIF) staining for expression of hormone receptors and other biomarkers combined with phasor lifetime analysis of NADH to quantify metabolic state will provide integrated spatial maps of metabolism and biomarker expression in endocrine-resistant HR breast cancer. We used the HCI-013 (013) and HCI-013EI (013EI) patient-derived xenograft (PDX) system, derived from a HR+, invasive lobular metastatic breast cancer which harbors the Y537S ESR1 mutation. Tumors were stained with a multiplex immunofluorescence (mIF) panel for pan-cytokeratin, ER, PR, HER2, and Ki67, detected by spectrally separate OPAL dyes. Hyperspectral phasor fluorescence spectra were obtained using successive single photon excitation at 488 nm, 561 nm and 640 nm and using multiphoton excitation at 1024 nm. Single photon excitation excites specific dyes, and multiphoton excites the entire panel of dyes simultaneously. The emission was collected using a Zeiss 880 laser scanning microscope equipped with a 32 channel spectral detector. Phasor coordinates of each component were resolved into fractional intensities of the five genes. In the same tissues and sections, phasor fluorescence lifetime imaging (FLIM) of NADH was used to quantify metabolic heterogeneity. NADH is present in two forms: free and protein-bound NADH. They have different fluorescence lifetimes and the ratio of these two species are correlated with NAD+/NADH ratio. Free NADH is associated with increased glycolysis. We observed greater metabolic heterogeneity in 013EI PDX samples, with similar levels of free NADH (glycolysis) at the tumor border, but significantly increased bound NADH (oxidative phosphorylation) in the tumor core. Spectral phasor data show differential expression of PR and PanCK (for 640 nm excitation), and HER2 and ERalpha (for 561 nm excitation) in the edge and center of cancer in 013 and 013EI variants. Our results show how a combination of spectral phasor and phasor-FLIM can be used to define metabolic changes in endocrine-resistant, HR+ PDX models and correlate them with biomarker expression. Ongoing work is quantitatively mapping these measurements at single-pixel resolution, and applying this imaging workflow to RNAscope in situ hybridization assays for gene expression. Combining multiphoton spectral (RNAscope) and FLIM (NADH) will allow pixelwise correlation of the gene expression and metabolic changes. Citation Format: Belen Torrado, Alexander Vallmitjana, Ayodeji Olukoya, Shaymaa Bahnassy, Hillary Stires, Aaron Rozeboom, Rebecca B. Riggins, Suman Ranjit. Integrated lifetime and spectral phasor imaging of biomarker expression and metabolism in hormone receptor positive breast cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2461.

Filament-induced nonlinear hyperspectral fluorescence imaging

Hyperspectral fluorescence imaging is promising for many applications including gas sensing and imaging. However, achieving spectrally and spatially resolved images for quantitative multi-gas and multi-parameter analysis remains challenging, especially in a non-cooperative environment. Here, we report a simple technique, based on filament-induced nonlinear spectroscopy, for information-rich gas imaging. We obtained three-dimensional (3D) hyperspectral images for an alcohol/air flame with sub-millimeter spatial resolutions. For each hypercube, a broadband fluorescence fingerprint spectrum, containing 2500 spectral elements (350–600 nm at a spectral resolution of 0.1 nm), was acquired. This technique, with backward fluorescence detection, enabled high-spatial-resolution point-wise imaging in a non-cooperative situation, e.g., in a sealed tube. Without meticulously arranged cameras, the technique may open up new opportunities for remote gas imaging and tomography.

Versatile compressive microscope for hyperspectral transmission and fluorescence lifetime imaging.

Increasing demand for multimodal characterization and imaging of new materials entails the combination of various methods in a single microscopic setup. Hyperspectral imaging of transmission spectra or photoluminescence (PL) decay imaging count among the most used methods. Nevertheless, these methods require very different working conditions and instrumentation. Therefore, combining the methods into a single microscopic system is seldom implemented. Here we demonstrate a novel versatile microscope based on single-pixel imaging, where we use a simple optical configuration to measure the hyperspectral information, as well as fluorescence lifetime imaging (FLIM). The maps are inherently spatially matched and can be taken with spectral resolution limited by the resolution of the used spectrometer (3 nm) or temporal resolution set by PL decay measurement (120 ps). We verify the system's performance by its comparison to the standard FLIM and non-imaging transmission spectroscopy. Our approach enabled us to switch between a broad field-of-view and micrometer resolution without changing the optical configuration. At the same time, the used design opens the possibility to add a variety of other characterization methods. This article demonstrates a simple, affordable way of complex material studies with huge versatility for the imaging parameters.

Room-temperature emitters in wafer-scale few-layer hBN by atmospheric pressure CVD

Hexagonal boron nitride (hBN) is a two-dimensional, wide band gap semiconductor material suitable for several technologies. 2D hBN appeared as a viable platform to produce bright and optically stable single photon emitters (SPEs) at room temperature, which are in demand for quantum technologies. In this context, one main challenge concerns the upscaling of 2D hBN with uniform spatial and spectral distribution of SPE sources. In this work we optimized the atmospheric-pressure chemical vapor deposition (APCVD) growth and obtained large-area 2D hBN with uniform fluorescence emission properties. We characterized the hBN films by a combination of electron microscopy, Raman and X-ray photoelectron spectroscopy techniques. The extensive characterization revealed few-layer, polycrystalline hBN films (∼3 nm thickness) with balanced stoichiometry and uniformity over 2″ wafer scale. We studied the fluorescence emission properties of the hBN films by multidimensional hyperspectral fluorescence microscopy. We measured simultaneously the spatial position, intensity, and spectral properties of the emitters, which were exposed to continuous illumination over minutes. Three main emission peaks (at 538, 582, and 617 nm) were observed, with associated replica peaks red-shifted by ∼53 nm. A surface emitter density of ∼0.1 emitters/μm2 was found. A comparative test with pristine hBN nanosheets produced by liquid-phase exfoliation (LPE) was performed, finding that CVD and LPE hBN possess analogous spectral emitter categories in terms of peak position/intensity and density. Overall, the line-shape and wavelength of the emission peaks, as well as the other measured features, are consistent with single-photon emission from hBN. The results indicate that APCVD hBN might proficiently serve as a SPE platform for quantum technologies.

Retrieval of Chlorophyll-a Concentrations of Class II Water Bodies of Inland Lakes and Reservoirs Based on ZY1-02D Satellite Hyperspectral Data

Chlorophyll-a is an important parameter that characterizes the eutrophication of water bodies. The advantage of ZY1-02D hyperspectral satellite subdivision in the visible light and near-infrared bands is that it highlights the unique characteristics of water bodies in the spectral dimension, and it helps to assess the Class II water bodies of inland lakes and reservoirs, making it an important tool for refined remote sensing detection of the environment. In this study, the Baiyangdian Nature Reserve in northern China, which contains a typical inland lake and wetland, was chosen as the study area. Using ZY1-02D hyperspectral synchronization transit images and in situ measured chlorophyll-a concentration as the data source, remote sensing of the chlorophyll-a concentration of inland lakes was conducted. By analyzing the correlation between the spectral reflectance of the ZY1-02D hyperspectral image and the chlorophyll-a concentration and using algorithms such as the single band, band ratio, and three bands to compare and filter characteristic wavelengths, a quantitative hyperspectral model of the chlorophyll-a concentration was established to determine the chlorophyll-a concentration of Baiyangdian Lake. The dynamic monitoring of the water body and the assessment of the nutritional status of the water body were determined. The results revealed that the estimation of the chlorophyll-a concentration of Baiyangdian Lake based on the hyperspectral Fluorescence Line Height (FLH) model was ideal, with an R2 value of 0.78. The FLH model not only comprehensively considers the effects of suspended solids, yellow substances, and backscattering of the water body on the estimation of the chlorophyll-a concentration, but also considers the influence of the elastic scattering efficiency of the chlorophyll. Based on the ZY1-02D hyperspectral data, a spatial distribution map of the chlorophyll-a concentration of Baiyangdian Lake was created to provide new ideas and technical support for monitoring inland water environments.

Cellular Photosynthetic Pigment and Structural Change in Festuca arundinacea (Tall Fescue) after Exposure to Acute and Chronic Chromium and Copper Stress

Expanded industrial globalization has resulted in the release of high concentrations of heavy metals into environmental water sources and soils. Phytoremediation may help to remove these heavy metals from contaminated soils. Tall fescue (Festuca arundinacea Shreb.) exhibits phytoremediation potential due to its endurance and high stress tolerances. Here, we report photochemical and structural responses in tall fescue to acute and chronic doses of heavy metals, copper (Cu) and hexavalent chromium (Cr(VI)). Visual signs of stress and decreased photosynthetic yield measurements were detected for both the acute and chronic exposures. To gain insight into stress responses at the cellular level, structural and pigment changes in tall fescue in response to Cu and Cr(VI) stress were assessed with brightfield and confocal fluorescence imaging. While brightfield images showed qualitative changes in plant tissue structure, the quantification of changes were not statistically significant due to high variability between leaf blades. Fluorescence imaging confirmed decreasing total chlorophyll content in tall fescue cross-sections in response to Cr(VI) and Cu exposure. To spectrally separate the closely related chlorophyll pigments (Chl-a, Chl-b, and Chl in photosystem I) and visualize their relative localizations within the plant tissue, hyperspectral confocal fluorescence microscopy was conducted with multivariate curve resolution (MCR) analysis of the data. These results determined that Chl-a and Chl-b were more significantly reduced than Chl associated with photosystem I. Additionally, a new spectral component was identified. A broad autofluorescence (AF) feature appeared in the late stress response of both acute and chronically exposed tall fescue and was localized in globular bodies. While the identity of the broad AF feature remains to be identified, we hypothesize that it may be associated with degraded chlorophyll components in autophagic bodies. If confirmed, this would indicate that autophagy is a stress response to heavy metal exposure in tall fescue.

Deep-learning-assisted Fourier transform imaging spectroscopy for hyperspectral fluorescence imaging

Hyperspectral fluorescence imaging is widely used when multiple fluorescent probes with close emission peaks are required. In particular, Fourier transform imaging spectroscopy (FTIS) provides unrivaled spectral resolution; however, the imaging throughput is very low due to the amount of interferogram sampling required. In this work, we apply deep learning to FTIS and show that the interferogram sampling can be drastically reduced by an order of magnitude without noticeable degradation in the image quality. For the demonstration, we use bovine pulmonary artery endothelial cells stained with three fluorescent dyes and 10 types of fluorescent beads with close emission peaks. Further, we show that the deep learning approach is more robust to the translation stage error and environmental vibrations. Thereby, the He-Ne correction, which is typically required for FTIS, can be bypassed, thus reducing the cost, size, and complexity of the FTIS system. Finally, we construct neural network models using Hyperband, an automatic hyperparameter selection algorithm, and compare the performance with our manually-optimized model.

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit non-destructively. Firstly, the SPXY algorithm was used to divide samples into training and prediction sets and three different algorithms were used to preprocess the raw spectral data. Secondly, algorithms such as the iteratively retaining information variables (IRIV), the variable iterative space shrinkage approach (VISSA), the model adaptive space shrinkage (MASS), the random frog (RF), and their combination (i.e., IRIV + VISSA + MASS + RF, IVMR) were used to extract effective variables from the preprocessed spectral data. Moreover, the second extractions, such as IRIV-VISSA and IRIV-MASS, and the third extraction (i.e., IVMR-VISSA-IRIV) were used to further reduce the redundant variables. Based on the effective variables, four regression models—random forest (RF), partial least square (PLSR), extreme learning machines (ELM), and multiple-kernel support vector regression (MK-SVR)—were built and compared for predicting. The results show that IVMR-VISSA-IRIV-MK-SVR had the best prediction results, with RP2, RC2 and RPD of 0.8512, 0.8580, and 2.66, respectively, which verifies that hyperspectral fluorescence imaging technology is reliable for predicting the pH value of kiwifruit non-destructively.

Linear Combination Properties of the Phasor Space in Fluorescence Imaging.

The phasor approach to fluorescence lifetime imaging, and more recently hyperspectral fluorescence imaging, has increased the use of these techniques, and improved the ease and intuitiveness of the data analysis. The fit-free nature of the phasor plots increases the speed of the analysis and reduces the dimensionality, optimization of data handling and storage. The reciprocity principle between the real and imaginary space—where the phasor and the pixel that the phasor originated from are linked and can be converted from one another—has helped the expansion of this method. The phasor coordinates calculated from a pixel, where multiple fluorescent species are present, depends on the phasor positions of those components. The relative positions are governed by the linear combination properties of the phasor space. According to this principle, the phasor position of a pixel with multiple components lies inside the polygon whose vertices are occupied by the phasor positions of these individual components and the distance between the image phasor to any of the vertices is inversely proportional to the fractional intensity contribution of that component to the total fluorescence from that image pixel. The higher the fractional intensity contribution of a vertex, the closer is the resultant phasor. The linear additivity in the phasor space can be exploited to obtain the fractional intensity contribution from multiple species and quantify their contribution. This review details the various mathematical models that can be used to obtain two/three/four components from phasor space with known phasor signatures and then how to obtain both the fractional intensities and phasor positions without any prior knowledge of either, assuming they are mono-exponential in nature. We note that other than for blind components, there are no restrictions on the type of the decay or their phasor positions for linear combinations to be valid—and they are applicable to complicated fluorescence lifetime decays from components with intensity decays described by multi-exponentials.

End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses.

Herbicides and heavy metals are hazardous substances of environmental pollution, resulting in plant stress and harming humans and animals. Identification of stress types can help trace stress sources, manage plant growth, and improve stress-resistant breeding. In this research, hyperspectral imaging (HSI) and chlorophyll fluorescence imaging (Chl-FI) were adopted to identify the rice plants under two types of herbicide stresses (butachlor (DCA) and quinclorac (ELK)) and two types of heavy metal stresses (cadmium (Cd) and copper (Cu)). Visible/near-infrared spectra of leaves (L-VIS/NIR) and stems (S-VIS/NIR) extracted from HSI and chlorophyll fluorescence kinetic curves of leaves (L-Chl-FKC) and stems (S-Chl-FKC) extracted from Chl-FI were fused to establish the models to detect the stress of the hazardous substances. Novel end-to-end deep fusion models were proposed for low-level, middle-level, and high-level information fusion to improve identification accuracy. Results showed that the high-level fusion-based convolutional neural network (CNN) models reached the highest detection accuracy (97.7%), outperforming the models using a single data source (<94.7%). Furthermore, the proposed end-to-end deep fusion models required a much simpler training procedure than the conventional two-stage deep learning fusion. This research provided an efficient alternative for plant stress phenotyping, including identifying plant stresses caused by hazardous substances of environmental pollution.

Narcissistic self-sorting of n-acene nano-ribbons yielding energy-transfer and electroluminescence at p-n junctions.

The 2,3-didecyloxy derivative of an n-type anthracene (n-BG) and a p-type tetracene (p-R) have been synthesized and their self-assembly into nano-ribbons studied. Hyperspectral fluorescence imaging revealed their narcissistic self-sorting, leading to separated nanoribbons emitting with very different colors (blue or green for n-BG, depending on the growth solvent, and red for p-R). It is unique that the usual origins of self-sorting, such as specific H-bonding, different growth kinetics, or incompatible steric hindrance can be ruled out. Hence, the narcissistic behaviour is herein proposed to originate from a so-far unconsidered cause: the discrepancy between the quadrupolar character of n-BG and dipolar character of p-R. At the p-n junctions of these nanoribbons, inter-ribbon FRET and electro-luminescence switch-on were observed by fluorescence/luminescence microscopy.