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Prediction of crop yield and quality is an essential component of successful implementation of precision agriculture. Given the recent commercialization of low-cost multispectral cameras mounted on unmanned aerial vehicles and advances in machine learning techniques, prediction systems for crop characteristics can be more precisely developed using machine learning techniques. Therefore, the model performances for predicting wheat grain yield and protein content between the machine learning algorithms based on spectral reflectance and plant height (e.g. random forest and artificial neural network) and the traditional linear regression based on vegetation indices were compared. Although the machine learning approaches based on reflectance could not improve the grain yield prediction accuracy, they have great potential for development in predicting protein content. The linear regression model based on a 2-band enhanced vegetation index was capable of predicting the yield with a root-mean-square error (RMSE) of 972 kg ha −1 . The random forest model based on reflectance was capable of predicting the protein content with an RMSE of 1.07%. The reflectance may have been linearly correlated with total biomass; thus, it was also linearly correlated with grain yield. There was a nonlinear relationship between the grain yield and protein content, which may have resulted in the higher model performance of the machine learning approaches in predicting protein content. However, this relationship would be variable according to the environment and agronomic practice. Further, field-scale research is required to assess how this relationship can be varied and affect the model generality, particularly when predicting protein content.

Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (UAV)-based remote sensing studies, but weather data have not been considered in modeling. The aim of this study was to explore the potential of multimodal deep learning on rice yield prediction accuracy using UAV multispectral images at the heading stage, along with weather data. The effects of the CNN architectures, layer depths, and weather data integration methods on the prediction accuracy were evaluated. Overall, the multimodal deep learning model integrating UAV-based multispectral imagery and weather data had the potential to develop more precise rice yield predictions. The best models were those trained with weekly weather data. A simple CNN feature extractor for UAV-based multispectral image input data might be sufficient to predict crop yields accurately. However, the spatial patterns of the predicted yield maps differed from model to model, although the prediction accuracy was almost the same. The results indicated that not only the prediction accuracies, but also the robustness of within-field yield predictions, should be assessed in further studies.

Numerical characterization of synchrotron radiation based on the Wigner function method is explored in order to accurately evaluate the light source performance. A number of numerical methods to compute the Wigner functions for typical synchrotron radiation sources such as bending magnets, undulators and wigglers, are presented, which significantly improve the computation efficiency and reduce the total computation time. As a practical example of the numerical characterization, optimization of betatron functions to maximize the brilliance of undulator radiation is discussed.

This review discusses the development of small-diameter silk-based vascular grafts, based on insights obtained through solid-state NMR structural analysis. With the increasing prevalence of cardiovascular diseases, the demand for vascular grafts with diameters of less than 6 mm is growing. Although synthetic grafts currently used in clinical settings—such as polyethylene terephthalate and expanded polytetrafluoroethylene—are effective, they tend to cause thrombosis and intimal hyperplasia when used as small-diameter vascular grafts. In response to this issue, research has been advancing on new materials that maintain excellent mechanical properties while improving biocompatibility. This review first describes how the detailed structure of silk fibroin (SF) before and after fiber formation was revealed for the first time through solid-state NMR analysis using stable isotope-labeled samples. Then it outlines design criteria for small-diameter SF-based vascular grafts, focusing on fabrication methods like electrospinning. Special attention is given to knitted SF grafts with SF sponge coatings, analyzed via 13C solid-state NMR in the dry and hydrated states of the sponges. In vivo performance in rat and canine models is discussed, along with chemically modified SF grafts such as silk-biodegradable polyurethane sponges and their structural and implantation results.

JMDC, Inc. (JMDC) has created a database, using data collected from health insurance societies in Japan, consisting of ledgers of insureds, claims (for hospitalization, outpatient treatment, drug preparation, and dental treatment), and health checkup results. The earliest data are from the claims in January 2005, except dental claims from December 2009 and health checkup results from April 2008. Currently (the end of June 2020), the number of insureds included is approximately 9.8 million. This database is unique for Japan and has the following characteristics: (a) the basic population can be ascertained; (b) standardization is carried out using a dictionary; and (c) anonymized individual IDs can be followed on the basis of a time‐series over various periods, with the earliest starting date being January 2005. However, it has certain limitations, in that the disease status and test results cannot be ascertained, and there is insufficient access to data for elderly people. JMDC, Inc. (JMDC) has created a database, using data collected from health insurance societies in Japan, consisting of ledgers of insureds, claims (for hospitalization, outpatient treatment, drug preparation, and dental treatment), and health checkup results. This database is unique for Japan and has the following characteristics: (a) the basic population can be ascertained; (b) standardization is carried out using a dictionary; and (c) anonymized individual IDs can be followed on the basis of a time‐series over various periods, with the earliest starting date being January 2005

On-farm experiments can provide farmers with information on more efficient crop management in their own fields. Developments in precision agricultural technologies, such as yield monitoring and variable-rate application technology, allow farmers to implement on-farm experiments. Research frameworks including the experimental design and the statistical analysis method strongly influences the precision of the experiment. Conventional statistical approaches (e.g., ordinary least squares regression) may not be appropriate for on-farm experiments because they are not capable of accurately accounting for the underlying spatial variation in a particular response variable (e.g., yield data). The effects of experimental designs and statistical approaches on type I error rates and estimation accuracy were explored through a simulation study hypothetically conducted on experiments in three wheat fields in Japan. Isotropic and anisotropic spatial linear mixed models were established for comparison with ordinary least squares regression models. The repeated designs were not sufficient to reduce both the risk of a type I error and the estimation bias on their own. A combination of a repeated design and an anisotropic model is sometimes required to improve the precision of the experiments. Model selection should be performed to determine whether the anisotropic model is required for analysis of any specific field. The anisotropic model had larger standard errors than the other models, especially when the estimates had large biases. This finding highlights an advantage of anisotropic models since they enable experimenters to cautiously consider the reliability of the estimates when they have a large bias.

Recent advances in wood treatment include the use of eco-friendly coatings to improve the wood’s dimensional stability and appearance. Assessing coating performance during its service life is critical for establishing a knowledge base for product optimization. Numerous approaches, including microimaging, are available for analyzing coating behavior. In addition to conventional microscopic techniques, high-resolution X-ray microtomography is a tool that provides nondestructive imaging of coatings and their substrates. In this study, we performed two-dimensional (2D) and three-dimensional (3D) visualization of tomographic reconstruction images of two coating types, spray and brush, to observe and assess the distribution of several commercial Japanese coating materials in Fagus crenata . X-ray images and plot profiles were used to determine the penetration depths and thicknesses of coatings. Each coated sample was scanned using X-ray microtomography, which allowed successful visualization and quantification of the coating penetration depth. Chemical content and concentration of the coating materials influenced penetration depth and amount.

In this study, a geometrical model for wood cells and penetrated adhesive resin at the veneer–veneer interface of plywood was developed. On the basis of this model, equations of thermal conductivity and a bound-water diffusion coefficient for resin-rich regions of softwood plywood were theoretically determined. These equations are suitable for use in numerical simulations of unsteady-state nonisothermal moisture diffusion. By introducing them into an existing coupled heat and moisture transfer model, a simple and physically reasonable simulation of moisture transfer through softwood plywood can be achieved.

The on‐tree counting of chestnut fruit (bur) is essential for yield estimation and monitoring of fruit load as well as tree health, which is important in determining the management strategy for orchards, markets, and tree health monitoring. However, the practice is still conducted by manual count or farmers’ intuition. Precise and effective counting method is yet to be established. This study attempted to count chestnut fruits with the object detection algorithm, You Only Look Once (YOLO), using the RGB imagery collected with an unmanned aerial vehicle (UAV). The model trained with 500 images (7866 burs labeled with bounding boxes) in YOLO version 4 (v4) could count the number of burs with a R2 value of 0.98 and a root mean square error of 6.3 (8.3% of the mean) relative to the manual count in the data set for each whole tree (n = 53). The R2 of linear regression between the number of burs obtained with the YOLOv4 model and the total yield was 0.76, with a standard error of 1.08 kg tree−1 (26.4% of coefficient of variation), which was equivalent to the in situ burs count by an expert technician. In addition, the number of burs counted with the YOLOv4 model after the adjustment of trunk circumference was negatively correlated with nut weight. Overall, the study revealed that YOLO algorithm coupled with UAV‐based RGB imagery is a precise and efficient method for on‐tree burs detection, which could be used as an indicator of yield and fruit load. Core Ideas This report aims to develop high‐throughput method for detection of on‐tree chestnut fruits. YOLO algorithm coupled with UAV‐based RGB imagery could detect chestnut fruits precisely and efficiently. The burs estimated by the algorithm could be an indicator of yield and fruit load.

Accurate modeling of site-specific crop yield response is key to providing farmers with accurate site-specific economically optimal input rates (EOIRs) recommendations. Many studies have demonstrated that machine learning models can accurately predict yield. These models have also been used to analyze the effect of fertilizer application rates on yield and derive EOIRs. But models with accurate yield prediction can still provide highly inaccurate input application recommendations. This study quantified the uncertainty generated when using machine learning methods to model the effect of fertilizer application on site-specific crop yield response. The study uses real on-farm precision experimental data to evaluate the influence of the choice of machine learning algorithms and covariate selection on yield and EOIR prediction. The crop is winter wheat, and the inputs considered are a slow-release basal fertilizer NPK 25–6–4 and a top-dressed fertilizer NPK 17–0–17. Random forest, XGBoost, support vector regression, and artificial neural network algorithms were trained with 255 sets of covariates derived from combining eight different soil properties. Results indicate that both the predicted EOIRs and associated gained profits are highly sensitive to the choice of machine learning algorithm and covariate selection. The coefficients of variation of EOIRs derived from all possible combinations of covariate selection ranged from 13.3 to 31.5% for basal fertilization and from 14.2 to 30.5% for top-dressing. These findings indicate that while machine learning can be useful for predicting site-specific crop yield levels, it must be used with caution in making fertilizer application rate recommendations.