Explore the vast range of titles available.

Accurate and rapid estimation of the crop yield is essential to precision agriculture. Critical to crop improvement, yield is a primary index for selecting excellent genotypes in crop breeding. Recently developed unmanned aerial vehicle (UAV) platforms and advanced algorithms can provide powerful tools for plant breeders. Genotype category information such as the maturity group information (M) can significantly influence soybean yield estimation using remote sensing data. The objective of this study was to improve soybean yield prediction by combining M with UAV-based multi-sensor data using machine learning methods. We investigated three types of maturity groups (Early, Median and Late) of soybean, and collected the UAV-based hyperspectral and red–green–blue (RGB) images at three key growth stages. Vegetation indices (VI) and texture features (Te) were extracted and combined with M to predict yield using partial least square regression (PLSR), Gaussian process regression (GPR), random forest regression (RFR) and kernel ridge regression (KRR). The results showed that (1) the method of combining M with remote sensing data could significantly improve the estimation performances of soybean yield. (2) The combinations of three variables (VI, Te and M) gave the best estimation accuracy. Meanwhile, the flowering stage was the optimal single time point for yield estimation (R2 = 0.689, RMSE = 408.099 kg/hm2), while using multiple growth stages produced the best estimation performance (R2 = 0.700, RMSE = 400.946 kg/hm2). (3) By comparing the models constructed by different algorithms for different growth stages, it showed that the models built by GPR showed the best performances. Overall, the results of this study provide insights into soybean yield estimation based on UAV remote sensing data and maturity information.

The breeding of high-yield varieties is a core objective of soybean breeding programs, and phenotypic trait-based selection offers an effective pathway to achieve this goal. The aim of this study was to identify the key phenotypic traits of high-yield soybean varieties and to utilize these traits for screening high-yield soybean varieties. In this study, the UAV (unmanned aerial vehicle)- and field-based phenotypic data were collected from 1923 and 1015 soybean breeding plots at the Xuzhou experimental site in 2022 and 2023, respectively. First, the soybean varieties were grouped by using a self-organizing map and K-means clustering to investigate the relationships between various traits and soybean yield and to identify the key ones for selecting high-yield soybean varieties. It was shown that the duration of canopy coverage remaining above 90% (Tcc90) was a critical phenotypic trait for selecting high-yield varieties. Moreover, high-yield soybean varieties typically exhibited several key phenotypic traits such as rapid development of canopy coverage (Tcc90r, the time when canopy coverage first reached 90%), prolonged duration of high canopy coverage (Tcc90), a delayed decline in canopy coverage (Tcc90d, the time when canopy coverage began to decline below 90%), and moderate-to-high plant height (PH) and hundred-grain weight (HGW). Based on these findings, a method for screening high-yield soybean varieties was proposed, through which 87% and 72% of high-yield varieties (top 5%) in 2022 and 2023, respectively, were successfully selected. Additionally, about 9% (in 2022) and 10% (in 2023) of the low-yielding (bottom 60%) were misclassified as high-yielding. This study demonstrates the benefit of high-throughput phenotyping for soybean yield-related traits and variety screening and provides helpful insights into identifying high-yield soybean varieties in breeding programs.

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data.

High-performance p-type silicon target materials of Co-doped B and Al elements were produced using Ga and P contaminated upgraded metallurgical-grade silicon (UMG-Si) at the industrial scale. The purity of silicon ingots is above 5.5 N after the directional solidification process, which meets market demand. The segregation behavior of elements and compensation effect on the resistivity are discussed. The effective segregation coefficients of B, Al, Ga, and P for ingot No. 1 were approximately 0.66, 0.14, 0.38, and 0.49, respectively. The segregation coefficients of P, Ga, and Al become larger, the segregation effect tends to become smaller, which is attributed to the doped and contaminated elements that have the recombination effect on the holes and electrons. The distribution of resistivity can be regulated precisely by the compensation difference [NA–ND] along the solidified fraction. The mean resistivity of the ingots is approximately 0.013 Ω cm. Prolonging melting time is conducive to the uniform distribution of doping elements.

Large temperature gradient was introduced to improve the removal rate of metal impurity in silicon ingot during direction solidification. The concentration of metal impurities in the silicon ingot with a large temperature gradient is 0.96 ppmw. The solidification time is reduced by 20% due to the fast speed of crystal growth improved; meanwhile the purity is increased by 64%.

A silicon target material with the purity of 99.999 wt% (99.999%) was prepared by adding Al–B master alloy in directional solidification. The segregation behavior of the dopant and the effect on the resistivity were studied in this work. It was revealed that the AlB 2 particles in the Al–B master alloy will generate the clusters of [B] and [Al] in molten silicon at 1723 K spontaneously. The concentrations of B and Al were increasing gradually along the solidified fraction in the silicon ingot. The measured values of B were in good agreement with the curve of the Scheil’s equation below 85% of the solidified fraction. The measured values of Al were fitting well with the curve of the Scheil’s equation when the effective segregation coefficient is 0.00378. It was found that the resistivity of the silicon target material was regulated by B co-doped Al simultaneously in directional solidification.

The silicon target material was prepared by adding Al-6B master alloy in directional solidification. The microstructure was characterized and the resistivity was studied in this work. The results showed that the purity of the silicon target material was more than 99.999% (5N). The resistivity was ranges from 0.002 to 0.030 Ω·cm along the ingot height. It was revealed that the particles of AlB2 in Al-6B master alloy would react spontaneously and generate clusters of [B] and [Al] in molten silicon at 1723 K. After directional solidification, the content of B and Al were increasing gradually with the increase of solidified fraction. The measured values of B were in good agreement with the curve of the Scheil equation below 80% of the ingot height. The mean concentration of B was about 17.20 ppmw and the mean concentration of Al was about 8.07 ppmw after directional solidification. The measured values of Al were fitting well with the curve of values which the effective segregation coefficient was 0.00378. It was observed that B co-doped Al in directional solidification polysilicon could regulate resistivity mutually. This work provides the theoretical basis and technical support for industrial production of the silicon target material.