The quantification of southern corn leaf blight disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques.

Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm-an unsupervised machine learning model-to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization.

A comparison of three automated root-knot nematode egg counting approaches using machine learning, image analysis, and a hybrid model.

Meloidogyne spp. (root-knot nematodes, RKN) are a major threat to a wide range of agricultural crops worldwide. Breeding crops for RKN resistance is an effective management strategy, yet assaying large numbers of breeding lines requires laborious bioassays that are time-consuming and require experienced researchers. In these bioassays, quantifying nematode eggs through manual counting is considered the current standard for quantifying establishing resistance in plant genotypes. Counting RKN eggs is highly laborious, and even experienced researchers are subject to fatigue or misclassification, leading to potential errors in phenotyping. Here, we present three automated egg counting models that rely on machine learning and image analysis to quantify RKN eggs extracted from tobacco and sweetpotato plants. The first method relied on convolutional neural networks trained using annotated images to identify eggs (M. enterolobii R2 = 0.899, M. incognita R2 = 0.927, M. javanica R2 = 0.886), while a second contour-based approach used image analysis to identify eggs from their morphological characteristics and did not rely on neural networks (M. enterolobii R2 = 0.977, M. incognita R2 = 0.990, M. javanica R2 = 0.924). A third hybrid model combined these approaches and was able to detect and count eggs nearly as well as human raters (M. enterolobii R2 = 0.985, M. incognita R2 = 0.992, M. javanica R2 = 0.983). These automated counting protocols have the potential to provide significant time and resource savings annually for breeders and nematologists, and may be broadly applicable to other nematode species.

Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry.

Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health and accelerating breeding programs. A common challenge when capturing images in the field relates to the spectral reflection of sunlight (glare) from crop leaves that, at certain solar incidences and sensor viewing angles, presents unwanted signals. The research presented here involves the convergence of 2 parallel projects to develop a facile algorithm that can use polarization data to decouple light reflected from the surface of the leaves and light scattered from the leaf's tissue. The first project is a mast-mounted hyperspectral imaging polarimeter (HIP) that can image a maize field across multiple diurnal cycles throughout a growing season. The second project is a multistatic fiber-based Mueller matrix bidirectional reflectance distribution function (mmBRDF) instrument which measures the polarized light-scattering behavior of individual maize leaves. The mmBRDF data was fitted to an existing model, which outputs parameters that were used to run simulations. The simulated data were then used to train a shallow neural network which works by comparing unpolarized 2-band vegetation index (VI) with linearly polarized data from the low-reflectivity bands of the VI. Using GNDVI and red-edge reflection ratio we saw an improvement of an order of magnitude or more in the mean error (ϵ) and a reduction spanning 1.5 to 2.7 in their standard deviation (ϵσ) after applying the correction network on the HIP sensor data.

Hybrid spatial-temporal Mueller matrix imaging spectropolarimeter for high throughput plant phenotyping.

Many correlations exist between spectral reflectance or transmission with various phenotypic responses from plants. Of interest to us are metabolic characteristics, namely, how the various polarimetric components of plants may correlate to underlying environmental, metabolic, and genotypic differences among different varieties within a given species, as conducted during large field experimental trials. In this paper, we overview a portable Mueller matrix imaging spectropolarimeter, optimized for field use, by combining a temporal and spatial modulation scheme. Key aspects of the design include minimizing the measurement time while maximizing the signal-to-noise ratio by mitigating systematic error. This was achieved while maintaining an imaging capability across multiple measurement wavelengths, spanning the blue to near-infrared spectral region (405-730 nm). To this end, we present our optimization procedure, simulations, and calibration methods. Validation results, which were taken in redundant and non-redundant measurement configurations, indicated that the polarimeter provides average absolute errors of (5.3±2.2)×10-3 and (7.1±3.1)×10-3, respectively. Finally, we provide preliminary field data (depolarization, retardance, and diattenuation) to establish baselines of barren and non-barren Zea maize hybrids (G90 variety), as captured from various leaf and canopy positions during our summer 2022 field experiments. Results indicate that subtle variations in retardance and diattenuation versus leaf canopy position may be present before they are clearly visible in the spectral transmission.

Flexible sensor patch for continuous carbon dioxide monitoring.

Monitoring and measurement of carbon dioxide (CO2) is critical for many fields. The gold standard CO2 sensor, the Severinghaus electrode, has remained unchanged for decades. In recent years, many other CO2 sensor formats, such as detection based upon pH-sensitive dyes, have been demonstrated, opening the door for relatively simple optical detection schemes. However, a majority of these optochemical sensors require complex sensor preparation steps and are difficult to control and repeatably execute. Here, we report a facile CO2 sensor generation method that suffers from none of the typical fabrication issues. The method described here utilizes polydimethylsiloxane (PDMS) as the flexible sensor matrix and 1-hydroxypyrene-3,6,8-trisulfonate (HPTS), a pH-sensitive dye, as the sensing material. HPTS, a base (NaOH), and glycerol are loaded as dense droplets into a thin PDMS layer which is subsequently cured around the droplet. The fabrication process does not require prior knowledge in chemistry or device fabrication and can be completed as quickly as PDMS cures (∼2 h). We demonstrate the application of this thin-patch sensor for in-line CO2 quantification in cell culture media. To this end, we optimized the sensing composition and quantified CO2 in the range of 0-20 kPa. A standard curve was generated with high fidelity (R 2 = 0.998) along with an analytical resolution of 0.5 kPa (3.7 mm Hg). Additionally, the sensor is fully autoclavable for applications requiring sterility and has a long working lifetime. This flexible, simple-to-manufacture sensor has a myriad of potential applications and represents a new, straightforward means for optical carbon dioxide measurement.

Practical spectral photography II: snapshot spectral imaging using linear retarders and microgrid polarization cameras.

Despite recent advances, customized multispectral cameras can be challenging or costly to deploy in some use cases. Complexities span electronic synchronization, multi-camera calibration, parallax and spatial co-registration, and data acquisition from multiple cameras, all of which can hamper their ease of use. This paper discusses a generalized procedure for multispectral sensing using a pixelated polarization camera and anisotropic polymer film retarders to create multivariate optical filters. We then describe the calibration procedure, which leverages neural networks to convert measured data into calibrated spectra (intensity versus wavelength). Experimental results are presented for a multivariate and channeled optical filter. Finally, imaging results taken using a red, green, and blue microgrid polarization camera and the channeled optical filter are presented. Imaging experiments indicated that the calculated spectra's root mean square error is highest in the region where the camera's red, green, and blue filter responses overlap. The average error of the spectral reflectance, measured of our spectralon tiles, was 6.5% for wavelengths spanning 425-675 nm. This technique demonstrates that 12 spectral channels can be obtained with a relatively simple and robust optical setup, and at minimal cost beyond the purchase of the camera.

Multistatic fiber-based system for measuring the Mueller matrix bidirectional reflectance distribution function.

Bidirectionality effects can be a significant confounding factor when measuring hyperspectral reflectance data. The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectivity of surfaces to correct remote sensing measurements. However, measuring BRDFs can be time-consuming, especially when collecting Mueller matrix BRDF (mmBRDF) measurements of a surface via conventional goniometric techniques. In this paper, we present a system for collecting mmBRDF measurements using static optical fiber detectors that sample the hemisphere surrounding an object. The entrance to each fiber contains a polarization state analyzer configuration, allowing for the simultaneous acquisition of the Stokes vector intensity components at many altitudinal and azimuthal viewing positions. We describe the setup, calibration, and data processing used for this system and present its performance as applied to mmBRDF measurements of a ground glass diffuser.

Computer vision for detecting field-evolved lepidopteran resistance to Bt maize.

BACKGROUND: Resistance evolution of lepidopteran pests to Bacillus thuringiensis (Bt) toxins produced in maize and cotton is a significant issue worldwide. Effective toxin stewardship requires reliable detection of field-evolved resistance to enable the implementation of mitigation strategies. Currently, visual estimates of maize injury are used to document changing susceptibility. In this study, we evaluated an existing maize injury monitoring protocol used to estimate Bt resistance levels in Helicoverpa zea (Lepidoptera: Noctuidae). RESULTS: We detected high interobserver variability across multiple injury metrics, suggesting that the precision and accuracy of maize injury detection could be improved. To do this, we developed a computer vision-based algorithm to measure H. zea injury. Algorithm estimates were more accurate and precise than a sample of human observers. Moreover, observer estimates tended to overpredict H. zea injury, which may increase the false-positive rate, leading to prophylactic insecticide application and unnecessary regulatory action. CONCLUSIONS: Automated detection and tracking of lepidopteran resistance evolution to Bt toxins are critical for genetically engineered crop stewardship to prevent the use of additional insecticides to combat resistant pests. Advantages of this computerized screening are: (i) standardized Bt injury metrics in space and time, (ii) preservation of digital data for cross-referencing when thresholds are reached, and (iii) the ability to increase sample sizes significantly. This technological solution represents a significant step toward improving confidence in resistance monitoring efforts among researchers, regulators and the agricultural biotechnology industry.

Mantis shrimp-inspired organic photodetector for simultaneous hyperspectral and polarimetric imaging.

Combining hyperspectral and polarimetric imaging provides a powerful sensing modality with broad applications from astronomy to biology. Existing methods rely on temporal data acquisition or snapshot imaging of spatially separated detectors. These approaches incur fundamental artifacts that degrade imaging performance. To overcome these limitations, we present a stomatopod-inspired sensor capable of snapshot hyperspectral and polarization sensing in a single pixel. The design consists of stacking polarization-sensitive organic photovoltaics (P-OPVs) and polymer retarders. Multiple spectral and polarization channels are obtained by exploiting the P-OPVs' anisotropic response and the retarders' dispersion. We show that the design can sense 15 spectral channels over a 350-nanometer bandwidth. A detector is also experimentally demonstrated, which simultaneously registers four spectral channels and three polarization channels. The sensor showcases the myriad degrees of freedom offered by organic semiconductors that are not available in inorganics and heralds a fundamentally unexplored route for simultaneous spectral and polarimetric imaging.

Organic-based photodetectors for multiband spectral imaging.

Using organic photodetectors for multispectral sensing is attractive due to their unique capabilities to tune spectral response, transmittance, and polarization sensitivity. Existing methods lack tandem multicolor detection and exhibit high spectral cross talk. We exploit the polarization sensitivity of organic photodetectors, together with birefringent optical filters to design single-pixel multispectral detectors that achieve high spectral selectivity and good radiometric performance. Two different architectures are explored and optimized, including the Solc-based and multitwist-retarder-based organic photodetectors. Although the former demonstrated a higher spectral resolution, the latter enables a more compact sensor as well as greater flexibility in device fabrication.

Quantification of gray mold infection in lettuce using a bispectral imaging system under laboratory conditions.

Gray mold disease caused by the fungus Botrytis cinerea damages many crop hosts worldwide and is responsible for heavy economic losses. Early diagnosis and detection of the disease would allow for more effective crop management practices to prevent outbreaks in field or greenhouse settings. Furthermore, having a simple, non-invasive way to quantify the extent of gray mold disease is important for plant pathologists interested in measuring infection rates. In this paper, we design and build a bispectral imaging system for discriminating between leaf regions infected with gray mold and those that remain unharmed on a lettuce (Lactuca spp.) host. First, we describe a method to select two optimal (high contrast) spectral bands from continuous hyperspectral imagery (450-800 nm). We then explain the process of building a system based on these two spectral bands, located at 540 and 670 nm. The resultant system uses two cameras, with a narrow band-pass spectral filter mounted on each, to measure the bispectral reflectance of a lettuce leaf. The two resulting images are combined using a normalized difference calculation that produces a single image with high contrast between the leaves' infected and healthy regions. A classifier was then created based on the thresholding of single pixel values. We demonstrate that this simple classification produces a true-positive rate of 95.25% with a false-positive rate of 9.316% in laboratory conditions.

Internal defect scanning of sweetpotatoes using interactance spectroscopy.

While standard visible-light imaging offers a fast and inexpensive means of quality analysis of horticultural products, it is generally limited to measuring superficial (surface) defects. Using light at longer (near-infrared) or shorter (X-ray) wavelengths enables the detection of superficial tissue bruising and density defects, respectively; however, it does not enable the optical absorption and scattering properties of sub-dermal tissue to be quantified. This paper applies visible and near-infrared interactance spectroscopy to detect internal necrosis in sweetpotatoes and develops a Zemax scattering simulation that models the measured optical signatures for both healthy and necrotic tissue. This study demonstrates that interactance spectroscopy can detect the unique near-infrared optical signatures of necrotic tissues in sweetpotatoes down to a depth of approximately 5±0.5 mm. We anticipate that light scattering measurement methods will represent a significant improvement over the current destructive analysis methods used to assay for internal defects in sweetpotatoes.

Dual-beam potassium Voigt filter for atomic line imaging.

Spectrally narrowband imaging in remote sensing applications can be advantageous for detecting atomic emission features. This is especially useful in detecting specific constituents within rocket plumes, which are challenging to discern from naturally occurring sunglints. In this paper, we demonstrate a dual-beam technique, implemented with a Wollaston prism, for calibrating a Voigt magneto-optical filter for a linear polarizer's finite extinction ratio, as well as optical misalignment between the linear polarizers' transmission axes. Such a strategy would be key towards expanding the filter's field of view while maintaining its classification capabilities. Validation of the potassium Voigt filter is demonstrated using the simulation tool ElecSus in combination with a potassium hollow cathode lamp. RMS error between the filter's temperature response and that of the simulation was approximately 2%. We then demonstrate the detection of a potassium model rocket motor outdoors alongside a sunglint. Results indicate a 20-fold increase in contrast when using our dual-beam calibration strategy.

Optical crosstalk and off-axis modeling of an intrinsic coincident polarimeter.

Polarimeters have broad applications in remote sensing, astronomy, and biomedical imaging to measure the emitted, reflected, or transmitted state of polarization. An intrinsic coincident (IC) full-Stokes polarimeter was previously demonstrated by our group, in a free space configuration, by using stain-aligned polymer-based organic photovoltaics. To minimize the model's complexity, these were tilted to avoid crosstalk from back-reflections. We present a theoretical model of a monolithic IC polarimeter that considers the back-reflection's influence for on-axis light. The model was validated using a monolithic four-detector polarimeter, which achieved an error of less than 3%. Additionally, an off-axis model was produced and validated for a simpler two detector polarimeter, demonstrating an error between the TM and TE polarized components of less than 3% for angles spanning an 18° incidence cone.

Dual-beam cross-correlation spectrometer for radial velocity measurements.

Measuring the radial velocity of an object can be achieved by quantifying the Doppler shift of Fraunhofer lines. Measurements are typically made using high-resolution conventional spectroscopy, in which the Doppler shift is calculated numerically on a computer. An alternative technique includes cross-correlation spectroscopy, which performs an optical correlation of the incident spectrum against a reference spectrum embedded in the instrument. Many existing correlation spectrometers leverage a chrome mask and obtain a single beam measurement, making the sensors more sensitive to atmospheric turbulence without moving parts. In this paper, we present a static dual-beam polarization-based technique for acquiring cross-correlation spectra that is insensitive to atmospheric turbulence and contains no moving parts. The instrument is based on acquiring light both inside and outside of the solar Fraunhofer lines using a twisted nematic liquid-crystal spatial light modulator. Correlation spectra can be calculated as a ratio of these two components. A model of the dual-beam cross-correlation spectrometer is presented and subsequently validated with experimental observations of Venus. Radial velocity accuracies, as calculated against reference ephemerides, yielded an absolute error less than 0.24%.

Microbolometer with a multi-aperture polymer thin-film array for neural-network-basedtarget identification.

Infrared imaging spectrometers are frequently used for detecting chemicals at standoff distances. Cost, size, and sensitivity are common tradeoffs in this regime, particularly when deploying infrared imaging arrays. In this work, we develop and characterize an infrared snapshot computational imaging spectrometer that leverages a multi-aperture filtered design. A theoretical model is developed, describing the multiplexed encoding technique. The experimental system is then described, including filter optimization and fabrication. Finally, the performance of the system is tested, leveraging a neural-network-based calibration approach, for various indoor and outdoor detection scenarios involving liquid contaminants. The results of our testing demonstrate that the system can detect room-temperature liquid contaminants under cold sky downwelling radiance conditions. We achieve a false positive rate (FPR) of 0.12% at a true positive rate (TPR) of 95% for silicon oil on sand at 18°C and a FPR of 2% at a TPR of 95% for silicon oil on various substrates at 23°C. Results support the efficacy of using uncooled polymer absorption filters for infrared imaging liquid contaminant detectors.

Snapshot channeled imaging spectrometer using geometric phase holograms.

In this paper, we present the design and experimental demonstration of a snapshot imaging spectrometer based on channeled imaging spectrometry (CIS) and channeled imaging polarimetry (CIP). Using a geometric phase microlens array (GPMLA) with multiple focal lengths, the proposed spectrometer selects wavelength components within its designed operating waveband of 450-700 nm. Compared to other snapshot spectral imagers, its key components are especially suitable for roll-to-roll (R2R) rapid fabrication, which gives the spectrometer potential for low-cost mass production. The principles and proof-of-concept experimental system of the sensor are described in detail, followed by lab validation and outdoor measurement results which demonstrate the sensor's ability to resolve spectral and spatial contents under both experimental and natural illumination conditions.

Shear-Enhanced Transfer Printing of Conducting Polymer Thin Films.

Polymer conductors that are solution-processable provide an opportunity to realize low-cost organic electronics. However, coating sequential layers can be hindered by poor surface wetting or dissolution of underlying layers. This has led to the use of transfer printing where solid film inks are transferred from a donor substrate to partially fabricated devices using a stamp. This approach typically requires favorable adhesion differences between the stamp, ink, and receiving substrate. Here, we present a shear-assisted organic printing (SHARP) technique that employs a shear load on a post-less polydimethylsiloxane (PDMS) elastomer stamp to print large-area polymer films that can overcome large unfavorable adhesion differences between the stamp and receiving substrate. We explore the limits of this process by transfer printing poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) films with varied formulation that tune the adhesive fracture energy. Using this platform, we show that the SHARP process is able to overcome a 10-fold unfavorable adhesion differential without the use of a patterned PDMS stamp, enabling large-area printing. The SHARP approach is then used to print PEDOT:PSS films in the fabrication of high-performance semitransparent organic solar cells.

Imaging linear and circular polarization features in leaves with complete Mueller matrix polarimetry.

Spectropolarimetry of intact plant leaves allows to probe the molecular architecture of vegetation photosynthesis in a non-invasive and non-destructive way and, as such, can offer a wealth of physiological information. In addition to the molecular signals due to the photosynthetic machinery, the cell structure and its arrangement within a leaf can create and modify polarization signals. Using Mueller matrix polarimetry with rotating retarder modulation, we have visualized spatial variations in polarization in transmission around the chlorophyll a absorbance band from 650 nm to 710 nm. We show linear and circular polarization measurements of maple leaves and cultivated maize leaves and discuss the corresponding Mueller matrices and the Mueller matrix decompositions, which show distinct features in diattenuation, polarizance, retardance and depolarization. Importantly, while normal leaf tissue shows a typical split signal with both a negative and a positive peak in the induced fractional circular polarization and circular dichroism, the signals close to the veins only display a negative band. The results are similar to the negative band as reported earlier for single macrodomains. We discuss the possible role of the chloroplast orientation around the veins as a cause of this phenomenon. Systematic artefacts are ruled out as three independent measurements by different instruments gave similar results. These results provide better insight into circular polarization measurements on whole leaves and options for vegetation remote sensing using circular polarization.

Mueller matrix polarimetry on plasma sprayed thermal barrier coatings for porosity measurement.

Yttria-stabilized zirconia (YSZ) is the most widely used material for thermal plasma sprayed thermal barrier coatings (TBCs) used to protect gas turbine engine parts in demanding operation environments. The superior material properties of YSZ coatings are related to their internal porosity level. By quantifying the porosity level, tighter control on the spraying process can be achieved to produce reliable coatings. Currently, destructive measurement methods are widely used to measure the porosity level. In this paper, we describe a novel nondestructive approach that is applicable to classify the porosity level of plasma sprayed YSZ TBCs via Mueller matrix polarimetry. A rotating retarder Mueller matrix polarimeter was used to measure the polarization properties of the plasma sprayed YSZ coatings with different porosity levels. From these measurements, it was determined that a sample's measured depolarization ratio is dependent on the sample's surface roughness and porosity level. To this end, we correlate the depolarization ratio with the samples' surface roughness, as measured by a contact profilometer, as well as the total porosity level, in percentage measured using a micrograph and stereological analysis. With the use of this technique, a full-field and rapid measurement of porosity level can be achieved.