Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.

Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD.

Genome-wide identification and expression analysis of SUT gene family members in sugar beet (Beta vulgaris L.).

Sucrose transporters (SUTs) play an important role in the transmembrane transport and distribution of sucrose, and their activity has an important impact on plant growth and crop yield. In this study, the SUT gene family was identified in the whole beet genome using bioinformatics methods, and gene characteristics, subcellular localization prediction, phylogenetic evolution, promoter cis-elements and expression patterns were systematically analyzed. A total of 9 SUT gene family members were identified from in beet genome and divided into 3 different groups (group 1, group 2, and Group 3), which were unevenly distributed on 4 chromosomes. Most SUT family members contained photoresponsive and hormone-regulated response elements. Subcellular localization prediction showed that the BvSUT genes are all located in the inner membrane, and most of the terms identified through GO enrichment analysis are classified as "membrane" related. The results of qRT-PCR showed that the expression level of the BvSUT gene was significantly higher in the tuber enlargement stage (100-140 d) than in other stages. This study is the first to analyze the BvSUT gene family in sugar beet, and it provides a theoretical basis for the functional exploration and application of SUT genes in crop improvement, especially in sugar crops.

Resolution improvement of low frequency AC magnetic field detection for modulated MR sensors.

Magnetic modulation methods especially Micro-Electro-Mechanical System (MEMS) modulation can improve the sensitivity of magnetoresistive (MR) sensors dramatically, and pT level detection of Direct Current (DC) magnetic field can be realized. While in a Low Frequency Alternate Current (LFAC) magnetic field measurement situation, frequency measurement is limited by a serious spectrum aliasing problem caused by the remanence in sensors and geomagnetic field, leading to target information loss because frequency indicates the magnetic target characteristics. In this paper, a compensation field produced with integrated coils is applied to the MR sensor to remove DC magnetic field distortion, and a LFAC magnetic field frequency estimation algorithm is proposed based on a search of the database, which is derived from the numerical model revealing the relationship of the LFAC frequency and determination factor [defined by the ratio of Discrete Fourier Transform (DFT) coefficients]. In this algorithm, an inverse modulation of sensor signals is performed to detect jumping-off point of LFAC in the time domain; this step is exploited to determine sampling points to be processed. A determination factor is calculated and taken into database to figure out frequency with a binary search algorithm. Experimental results demonstrate that the frequency measurement resolution of the LFAC magnetic field is improved from 12.2 Hz to 0.8 Hz by the presented method, which, within the signal band of a magnetic anomaly (0.04-2 Hz), indicates that the proposed method may expand the applications of magnetoresistive (MR) sensors to human healthcare and magnetic anomaly detection (MAD).

Large-Area Nanosphere Self-Assembly by a Micro-Propulsive Injection Method for High Throughput Periodic Surface Nanotexturing.

A high throughput surface texturing process for optical and optoelectric devices based on a large-area self-assembly of nanospheres via a low-cost micropropulsive injection (MPI) method is presented. The novel MPI process enables the formation of a well-organized monolayer of hexagonally arranged nanosphere arrays (NAs) with tunable periodicity directly on the water surface, which is then transferred onto the preset substrates. This process can readily reach a throughput of 3000 wafers/h, which is compatible with the high volume photovoltaic manufacturing, thereby presenting a highly versatile platform for the fabrication of periodic nanotexturing on device surfaces. Specifically, a double-sided grating texturing with top-sided nanopencils and bottom-sided inverted-nanopyramids is realized in a thin film of crystalline silicon (28 µm in thickness) using chemical etching on the mask of NAs to significantly enhance antireflection and light trapping, resulting in absorptions nearly approaching the Lambertian limit over a broad wavelength range of 375-1000 nm and even surpassing this limit beyond 1000 nm. In addition, it is demonstrated that the NAs can serve as templates for replicas of three-dimensional conformal amorphous silicon films with significantly enhanced light harvesting. The MPI induced self-assembly process may provide a universal and cost-effective solution for boosting light utilization, a problem of crucial importance for ultrathin solar cells.