AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings.

BACKGROUND: Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis. RESULTS: Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity. CONCLUSIONS: AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

Depth image conversion model based on CycleGAN for growing tomato truss identification.

BACKGROUND: On tomato plants, the flowering truss is a group or cluster of smaller stems where flowers and fruit develop, while the growing truss is the most extended part of the stem. Because the state of the growing truss reacts sensitively to the surrounding environment, it is essential to control its growth in the early stages. With the recent development of information and artificial intelligence technology in agriculture, a previous study developed a real-time acquisition and evaluation method for images using robots. Furthermore, we used image processing to locate the growing truss to extract growth information. Among the different vision algorithms, the CycleGAN algorithm was used to generate and transform unpaired images using generated learning images. In this study, we developed a robot-based system for simultaneously acquiring RGB and depth images of the growing truss of the tomato plant. RESULTS: The segmentation performance for approximately 35 samples was compared via false negative (FN) and false positive (FP) indicators. For the depth camera image, we obtained FN and FP values of 17.55 ± 3.01% and 17.76 ± 3.55%, respectively. For the CycleGAN algorithm, we obtained FN and FP values of 19.24 ± 1.45% and 18.24 ± 1.54%, respectively. When segmentation was performed via image processing through depth image and CycleGAN, the mean intersection over union (mIoU) was 63.56 ± 8.44% and 69.25 ± 4.42%, respectively, indicating that the CycleGAN algorithm can identify the desired growing truss of the tomato plant with high precision. CONCLUSIONS: The on-site possibility of the image extraction technique using CycleGAN was confirmed when the image scanning robot drove in a straight line through a tomato greenhouse. In the future, the proposed approach is expected to be used in vision technology to scan tomato growth indicators in greenhouses using an unmanned robot platform.

A Hyperspectral Data 3D Convolutional Neural Network Classification Model for Diagnosis of Gray Mold Disease in Strawberry Leaves.

Gray mold disease is one of the most frequently occurring diseases in strawberries. Given that it spreads rapidly, rapid countermeasures are necessary through the development of early diagnosis technology. In this study, hyperspectral images of strawberry leaves that were inoculated with gray mold fungus to cause disease were taken; these images were classified into healthy and infected areas as seen by the naked eye. The areas where the infection spread after time elapsed were classified as the asymptomatic class. Square regions of interest (ROIs) with a dimensionality of 16 × 16 × 150 were acquired as training data, including infected, asymptomatic, and healthy areas. Then, 2D and 3D data were used in the development of a convolutional neural network (CNN) classification model. An effective wavelength analysis was performed before the development of the CNN model. Further, the classification model that was developed with 2D training data showed a classification accuracy of 0.74, while the model that used 3D data acquired an accuracy of 0.84; this indicated that the 3D data produced slightly better performance. When performing classification between healthy and asymptomatic areas for developing early diagnosis technology, the two CNN models showed a classification accuracy of 0.73 with regards to the asymptomatic ones. To increase accuracy in classifying asymptomatic areas, a model was developed by smoothing the spectrum data and expanding the first and second derivatives; the results showed that it was possible to increase the asymptomatic classification accuracy to 0.77 and reduce the misclassification of asymptomatic areas as healthy areas. Based on these results, it is concluded that the proposed 3D CNN classification model can be used as an early diagnosis sensor of gray mold diseases since it produces immediate on-site analysis results of hyperspectral images of leaves.

Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering.

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

Optimization of antioxidant, anti-diabetic, and anti-inflammatory activities and ganoderic acid content of differentially dried Ganoderma lucidum using response surface methodology.

The dried Ganoderma lucidum (GL) has been widely used for its pharmacological properties and bioactive ganoderic acids (GAs). Herein, extraction procedures combining ultra-sonication and heating were optimized using response surface methodology based on four variables (antioxidant activity, anti-diabetic activity, total GAs content, and total polysaccharide content) and principal component analysis. The extraction of freeze-dried GL at temperatures between 64.2 and 70 °C for 1.2 h maximized the antioxidant activity and GA content, whereas the polysaccharide content and anti-diabetic activity were maximized by extraction between 66.8 and 70 °C for more than 2.8 h. Heat-dried GL extracted at 50 °C for 3 h provided the greatest anti-inflammatory activity against HaCaT cells by suppressing the response to inflammation related cytokines at mRNA levels. These results suggest that extraction conditions might be a limiting factor for target-oriented investigations, and optimized extraction methods may improve the potential effect and quality of harvested GL products.

Production of low potassium kale with increased glucosinolate content from vertical farming as a novel dietary option for renal dysfunction patients.

The production of low potassium vegetables arose out of the dietary needs of patients with renal dysfunction. Attempts have been made to reduce potassium content in vegetables and fruits; however, induced potassium deficiency has often resulted in decreased yields. Here, we investigated a new method of producing low potassium kale and present the characteristics of the resulting produce. By substituting potassium nitrate with calcium nitrate in the nutrient solution 2 weeks before harvesting, the potassium content of kale was reduced by 70% without a deterioration in yield and semblance qualities. Despite no relationships being detected between potassium deficiency and anti-oxidative properties, the total glucosinolate content, an indicator of the anti-cancer effect of cruciferous vegetables, was significantly increased by potassium deficiency in kale. This study demonstrates a novel method of producing low potassium kale for patients with renal failure, without a reduction in yield but with beneficial increase in glucosinolates.

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control.

Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R2 of 0.94). In addition, the method using an algorithm based on an OFNN was proposed for optimizing considered six window-opening behavior. Three case studies confirmed the optimization performance of OFNN in the nonlinear model and verified the performance through simulations. Finally, a control system based on this logic was used in a field experiment for six days by comparing two greenhouses driven by conventional control logic and the developed control logic; a comparison of the results showed RMSEs of 3.01 °C and 2.45 °C, respectively. It confirmed the improved control performance in comparison to a conventional ventilation control system.

Random nanohole arrays and its application to crystalline Si thin foils produced by proton induced exfoliation for solar cells.

We report high efficiency cell processing technologies for the ultra-thin Si solar cells based on crystalline Si thin foils (below a 50 µm thickness) produced by the proton implant exfoliation (PIE) technique. Shallow textures of submicrometer scale is essential for effective light trapping in crystalline Si thin foil based solar cells. In this study, we report the fabrication process of random Si nanohole arrays of ellipsoids by a facile way using low melting point metal nanoparticles of indium which were vacuum-deposited and dewetted spontaneously at room temperature. Combination of dry and wet etch processes with indium nanoparticles as etch masks enables the fabrication of random Si nanohole arrays of an ellipsoidal shape. The optimized etching processes led to effective light trapping nanostructures comparable to conventional micro-pyramids. We also developed the laser fired contact (LFC) process especially suitable for crystalline Si thin foil based PERC solar cells. The laser processing parameters were optimized to obtain a shallow LFC contact in conjunction with a low contact resistance. Lastly, we applied the random Si nanohole arrays and the LFC process to the crystalline Si thin foils (a 48 µm thickness) produced by the PIE technique and achieved the best efficiency of 17.1% while the planar PERC solar cell without the Si nanohole arrays exhibit 15.6%. Also, we demonstrate the ultra-thin wafer is bendable to have a 16 mm critical bending radius.

Enhanced efficiency of crystalline Si solar cells based on kerfless-thin wafers with nanohole arrays.

Several techniques have been proposed for kerfless wafering of thin Si wafers, which is one of the most essential techniques for reducing Si material loss in conventional wafering methods to lower cell cost. Proton induced exfoliation is one of promising kerfless techniques due to the simplicity of the process of implantation and cleaving. However, for application to high efficiency solar cells, it is necessary to cope with some problems such as implantation damage removal and texturing of (111) oriented wafers. This study analyzes the end-of-range defects at both kerfless and donor wafers and ion cutting sites. Thermal treatment and isotropic etching processes allow nearly complete removal of implantation damages in the cleaved-thin wafers. Combining laser interference lithography and a reactive ion etch process, a facile nanoscale texturing process for the kerfless thin wafers of a (111) crystal orientation has been developed. We demonstrate that the introduction of nanohole array textures with an optimal design and complete damage removal lead to an improved efficiency of 15.2% based on the kerfless wafer of a 48 µm thickness using the standard architecture of the Al back surface field.

Fabrication of parabolic Si nanostructures by nanosphere lithography and its application for solar cells.

We demonstrated fabrication of a parabola shaped Si nanostructures of various periods by combined approach of nanosphere lithography and a single step CF4/O2 reactive ion etch (RIE) process. Silica nanosphere monolayers in a hexagonal array were well deposited by a solvent controlled spin coating technique based on binary organic solvents. We showed numerically that a parabolic Si nanostructure of an optimal period among various-shaped nanostructures overcoated with a dielectric layer of a 70 nm thickness provide the most effective antireflection. As the simulation results as a design guide, we fabricated the parabolic Si nanostructures of a 520 nm period and a 300 nm height exhibiting the lowest weighted reflectance of 2.75%. With incorporation of such parabolic Si nanostructures, a damage removal process for 20 sec and SiNx antireflection coating of a 70 nm thickness, the efficiency of solar cells increased to 17.2% while that of the planar cells without the nanostructures exhibited 16.2%. The efficiency enhancement of the cell with the Si nanostructures was attributed to the improved photocurrents arising from the broad spectral antireflection which was confirmed by the external quantum efficiency (EQE) measurements.

Photocurrent enhancements of organic solar cells by altering dewetting of plasmonic Ag nanoparticles.

Incorporation of metal nanoparticles into active layers of organic solar cells is one of the promising light trapping approaches. The size of metal nanoparticles is one of key factors to strong light trapping, and the size of thermally evaporated metal nanoparticles can be tuned by either post heat treatment or surface modification of substrates. We deposited Ag nanoparticles on ITO by varying nominal thicknesses, and post annealing was carried out to increase their size in radius. PEDOT: PSS was employed onto the ITO substrates as a buffer layer to alter the dewetting behavior of Ag nanoparticles. The size of Ag nanoparticles on PEDOT: PSS were dramatically increased by more than three times compared to those on the ITO substrates. Organic solar cells were fabricated on the ITO and PEDOT: PSS coated ITO substrates with incorporation of those Ag nanoparticles, and their performances were compared. The photocurrents of the cells with the active layers on PEDOT: PSS with an optimal choice of the Ag nanoparticles were greatly enhanced whereas the Ag nanoparticles on the ITO substrates did not lead to the photocurrent enhancements. The origin of the photocurrent enhancements with introducing the Ag nanoparticles on PEDOT: PSS are discussed.

An equivalent configuration approach for the moiré patterns appearing due to the reflecting surface in display system.

A new moiré pattern appearing in the off-state of a display system with a reflecting surface under illumination of an external ambient light source was analyzed. The origin of the new moiré pattern was attributed to the moiré pattern which is formed on the reflecting surface by external light and plays as a new light source with intensity profile. Configuring an optically equivalent system with no reflecting surface layer was proposed in order to overcome the limitation of new simulation program, which was previously proved to be very efficient in computation time but unable to handle a non-sequential system containing a reflecting surface. It was verified that the new simulation algorithm combined with an equivalent configuration could provide an accurate and computation time-efficient analyses even for a system containing non-sequential stacked layer such as a reflecting surface.

An efficient simulation and analysis method of moiré patterns in display systems.

A precise and fast computational method for the simulation and analysis of moiré patterns is proposed. This new algorithm is based on convolution with superposition of the intensity profile which is transmitted from the optical layers and the point spread function. The computational time is shown to be much faster than that of the ray-tracing algorithm because the new algorithm does not involve a massive calculation. Also, information on the moiré pitch can be extracted directly from the sampling data of the moiré patterns.

Silicon nanodisk array design for effective light trapping in ultrathin c-Si.

The use of ultrathin c-Si (crystalline silicon) wafers thinner than 20 µm for solar cells is a very promising approach to realize dramatic reduction in cell cost. However, the ultrathin c-Si requires highly effective light trapping to compensate optical absorption reduction. Conventional texturing in micron scale is hardly applicable to the ultrathin c-Si wafers; thus, nano scale texturing is demanded. In general, nanotexturing is inevitably accompanied by surface area enlargements, which must be minimized in order to suppress surface recombination of minority carriers. In this study, we demonstrate using optical simulations that periodic c-Si nanodisk arrays of short heights less than 200 nm and optimal periods are very useful in terms of light trapping in the ultrathin c-Si wafers while low surface area enlargements are maintained. Double side texturing with the nanodisk arrays leads to over 90% of the Lambertian absorption limit while the surface area enlargement is kept below 1.5.

Novel aspect in grain size control of nanocrystalline diamond film for thin film waveguide mode resonance sensor application.

Nanocrystalline diamond (NCD) thin film growth was systematically investigated for application for the thin film waveguide mode resonance sensor. The NCD thin film was grown on the Si wafer or on the SiO2-coated sapphire substrate using the hot filament chemical vapor deposition (HFCVD). The structural/optical properties of the samples were characterized by the high-resolution scanning electron microscopy (HRSEM), high-resolution transmission electron microscopy (HRTEM), energy dispersive X-ray spectroscopy (EDS), near edge X-ray absorption fine structure (NEXAFS), X-ray diffraction (XRD), and ultraviolet-visible (UV-vis) spectroscopy. The waveguide modes of the NCD layer were studied by prism coupler technique using laser (wavelength: 632.8 nm) with varying incident angle. A novel aspect was disclosed in the grain size dependence on the growth temperature at the relatively low methane concentration in the precursor gas, which was important for optical property: the grain size increased with decreasing growth temperature, which was contrary to the conventional knowledge prevailing in the microcrystalline diamond (MCD) domain. We have provided discussions to reconcile such observation. An optical waveguide mode resonance was demonstrated in the visible region using the microstructure-controlled transparent NCD thin film waveguide, which provided a strong potential for the waveguide mode resonance sensor applications.

Fiber-optic waveguide coupled surface plasmon resonance sensor.

A novel approach to give an excellent tunability and self-referencing capability was presented by applying a concept of waveguide coupled surface plasmon resonance mode to a fiber-optic sensor. The presence of dielectric waveguide sandwiched between two metal layers made it possible to precisely tune the resonance wavelength in a broad range from visible to infrared region and to generate multiple modes which may be selectively used for suitable applications. Our approach also verified the potential capability of self-referencing based on a remarkable difference in sensitivity between the plasmonic and waveguide modes excited by p- and s-polarized lights, respectively, without using an additional reference channel. Experimental measurement carried out on sucrose solutions with varying concentration demonstrated the feasibility of our approach.

An efficient ray tracing algorithm for the simulation of light trapping effects in Si solar cells with textured surfaces.

Optimizing the design of the surface texture is an essential aspect of Si solar cell technology as it can maximize the light trapping efficiency of the cells. The proper simulation tools can provide efficient means of designing and analyzing the effects of the texture patterns on light confinement in an active medium. In this work, a newly devised algorithm termed Slab-Outline, based on a ray tracing technique, is reported. The details of the intersection searching logic adopted in Slab-Outline algorithm are also discussed. The efficiency of the logic was tested by comparing the computing time between the current algorithm and the Constructive Solid Geometry algorithm, and its superiority in computing speed was proved. The validity of the new algorithm was verified by comparing the simulated reflectance spectra with the measured spectra from a textured Si surface.

Resolution enhancement in surface plasmon resonance sensor based on waveguide coupled mode by combining a bimetallic approach.

In this study, we present and demonstrate a new route to a great enhancement in resolution of surface plasmon resonance sensors. Basically, our approach combines a waveguide coupled plasmonic mode and a kind of Au/Ag bimetallic enhancement concept. Theoretical modeling was carried out by solving Fresnel equations for the multilayer stack of prism/Ag inner-metal layer/dielectric waveguide/Au outer-metal layer. The inner Ag layer couples incident light to a guided wave and makes more fields effectively concentrated on the outer Au surface. A substantial enhancement in resolution was experimentally verified for the model stack using a ZnS-SiO2 waveguide layer.

Toward understanding the mechanism of nonlinear optical characteristics of PbTe thin film for nano-optical memory.

An experimental study is conducted toward understanding the mechanism of nonlinear optical properties of PbTe thin film that were demonstrated potentially usable for nano-optical memory based on super-resolution technology. By way of a real time optical-electrical characterization of a PbTe thin film device, it is found that absorption coefficient decreases with increasing laser power, accompanied by increase in carrier concentration. From z-scan measurements, nonlinear optical coefficient due to a long pulse (1 micros) z-scan is found nearly 3 order of magnitude higher than the one due to a short pulse (30 ps) z-scan when input energy density is relatively comparable. Conceivably, these experimental findings call for a physical model that is able to account for the prevailing role of a thermal contribution within the framework of absorption saturation by band filling. We speculate that the absorption saturation might be enhanced dramatically by making various indirect interband transitions possible via participation of phonons in a photonic excitation process.