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Unmanned aerial vehicle (UAV)-based multispectral sensors have great potential in crop monitoring due to their high flexibility, high spatial resolution, and ease of operation. Image preprocessing, however, is a prerequisite to make full use of the acquired high-quality data in practical applications. Most crop monitoring studies have focused on specific procedures or applications, and there has been little attempt to examine the accuracy of the data preprocessing steps. This study focuses on the preprocessing process of a six-band multispectral camera (Mini-MCA6) mounted on UAVs. First, we have quantified and analyzed the components of sensor error, including noise, vignetting, and lens distortion. Next, different methods of spectral band registration and radiometric correction were evaluated. Then, an appropriate image preprocessing process was proposed. Finally, the applicability and potential for crop monitoring were assessed in terms of accuracy by measurement of the leaf area index (LAI) and the leaf biomass inversion under variable growth conditions during five critical growth stages of winter wheat. The results show that noise and vignetting could be effectively removed via use of correction coefficients in image processing. The widely used Brown model was suitable for lens distortion correction of a Mini-MCA6. Band registration based on ground control points (GCPs) (Root-Mean-Square Error, RMSE = 1.02 pixels) was superior to that using PixelWrench2 (PW2) software (RMSE = 1.82 pixels). For radiometric correction, the accuracy of the empirical linear correction (ELC) method was significantly higher than that of light intensity sensor correction (ILSC) method. The multispectral images that were processed using optimal correction methods were demonstrated to be reliable for estimating LAI and leaf biomass. This study provides a feasible and semi-automatic image preprocessing process for a UAV-based Mini-MCA6, which also serves as a reference for other array-type multispectral sensors. Moreover, the high-quality data generated in this study may stimulate increased interest in remote high-efficiency monitoring of crop growth status.

Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC-TCVI and LNC-FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAVbased RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.

Bacterial wilt severely jeopardizes plant growth and causes enormous economic loss in the production of many crops, including tobacco (Nicotiana tabacum). Here, we first demonstrated that the roots of bacterial wilt-resistant tobacco mutant KCB-1 can limit the growth and reproduction of Ralstonia solanacearum. Secondly, we demonstrated that KCB-1 specifically induced an upregulation of naringenin content in root metabolites and root secretions. Further experiments showed that naringenin can disrupt the structure of R. solanacearum, inhibit the growth and reproduction of R. solanacearum, and exert a controlling effect on bacterial wilt. Exogenous naringenin application activated the resistance response in tobacco by inducing the burst of reactive oxygen species and salicylic acid deposition, leading to transcriptional reprogramming in tobacco roots. Additionally, both external application of naringenin in CB-1 and overexpression of the Nicotiana tabacum chalcone isomerase (NtCHI) gene, which regulates naringenin biosynthesis, in CB-1 resulted in a higher complexity of their inter-root bacterial communities than in untreated CB-1. Further analysis showed that naringenin could be used as a marker for resistant tobacco. The present study provides a reference for analyzing the resistance mechanism of bacterial wilt-resistant tobacco and controlling tobacco bacterial wilt. Bacterial wilt-resistant mutant KCB-1 restricts colonization and growth of Ralstonia solanacearum by specifically inducing increased naringenin content. [ABSTRACT FROM AUTHOR]

The aggregation-induced emission (AIE) effect exhibits a significant influence on the development of luminescent materials and has made remarkable progress over the past decades. The advancement of high-performance AIE materials requires fast and accurate predictions of their photophysical properties, which is impeded by the inherent limitations of quantum chemical calculations. In this work, we present an accurate machine learning approach for the fast predictions of quantum yields and wavelengths to screen out AIE molecules. A database of about 563 organic luminescent molecules with quantum yields and wavelengths in the monomeric/aggregated states was established. Individual/combined molecular fingerprints were selected and compared elaborately to attain appropriate molecular descriptors. Different machine learning algorithms combined with favorable molecular fingerprints were further screened to achieve more accurate prediction models. The simulation results indicate that combined molecular fingerprints yield more accurate predictions in the aggregated states, and random forest and gradient boosting regression algorithms show the best predictions in quantum yields and wavelengths, respectively. Given the successful applications of machine learning in quantum yields and wavelengths, it is reasonable to anticipate that machine learning can serve as a complementary strategy to traditional experimental/theoretical methods in the investigation of aggregation-induced luminescent molecules to facilitate the discovery of luminescent materials. [ABSTRACT FROM AUTHOR]

Wind turbines and floating platform upsizing are major trends in the current offshore wind development. However, harsh environmental conditions increase the risk of anchor dragging and mooring failure when deploying large offshore floating wind turbines. Therefore, it is necessary to design a mooring system for the specific deployment site. This study aims to perform the mooring system design of a floating offshore wind turbine (FOWT) operated in the South China Sea, which is a combination of the IEA 15 MW wind turbine and UMaine VolturnUS-S floating platform. Hydrodynamic coefficients were calculated based on the potential flow theory, considering the environmental loads in the South China Sea. Additionally, the hydrodynamic coefficients were imported into AQWA to calculate the time-domain mooring tension. The mooring design parameters, such as mooring line length, nominal sizes, and anchor point, were determined using the criterion of anchor uplift, maximum breaking strength, and fatigue life, respectively. The design criterion required that the anchor uplift is not more than the allowable value, the long-term breaking limit of mooring with a 100-year return period should be less than the maximum breaking limit, and the fatigue damage accumulation in 50 years should be safe. The mooring design procedure provides a reference for mooring system design and safe operation of large floating wind turbines in the South China Sea. [ABSTRACT FROM AUTHOR]

Summary: Although microwave‐assisted thermal sterilisation (MATS) held great potential to sterilise ready‐to‐eat (RTE) products compared with traditional retort sterilisation, the quality improvement effect of MATS treatment on shelf‐stable RTE products has not been extensively studied. This study was performed to evaluate the efficacy and applicability of MATS combined with olive oil vacuum impregnation against lipid oxidation and microbial growth in RTE Pacific saury during storage. MATS combined with olive oil vacuum impregnation (MTSO) effectively reduced processing time (by 57%) compared to retort‐treated RTE Pacific saury at the same thermal lethality value. The lower cooking loss (6.19%) and colour damage indicated that MTSO enhanced water retention and the stability of tissue structure compared to retort‐treated group (9.88%) or no olive oil group (7.11%). Lipid oxidation and protein degradation were effectively inhibited in MATS groups compared to retort‐treated ones during storage; such effect was more effective in olive oil impregnated group. Likewise, microbial activity and electronic sensory evaluations showed that the shelf life of MTSO group was extended by 1 week compared to retort‐treated groups. Overall, MTSO had considerable industrial potential to yield RET Pacific saury and even RTE products with better quality attributes than retort‐treated ones. [ABSTRACT FROM AUTHOR]