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Macaque monkey is a rare substitute which plays an important role for human beings in relation to psychological and spiritual science research. It is essential for these studies to accurately estimate the pose information of macaque monkeys. Many large-scale models have achieved state-of-the-art results in pose macaque estimation. However, it is difficult to deploy when computing resources are limited. Combining the structure of high-resolution network and the design principle of light-weight network, we propose the attention-refined light-weight high-resolution network for macaque monkey pose estimation (HR-MPE). The multi-branch parallel structure is adopted to maintain high-resolution representation throughout the process. Moreover, a novel basic block is designed by a powerful transformer structure and polarized self-attention, where there is a simple structure and fewer parameters. Two attention refined blocks are added at the end of the parallel structure, which are composed of light-weight asymmetric convolutions and a triplet attention with almost no parameter, obtaining richer representation information. An unbiased data processing method is also utilized to obtain an accurate flipping result. The experiment is conducted on a macaque dataset containing more than 13,000 pictures. Our network has reached a 77.0 AP score, surpassing HRFormer with fewer parameters by 1.8 AP.

Soil disinfection is an important agronomic measure to prevent soil-borne diseases, insects, weeds and other hazards. Based on the premise of being environmentally friendly, microwave soil disinfection can improve crop yield and quality in a pollution-free, residue-free and green way. The law of microwave soil heating is the theoretical basis of microwave soil disinfection. Therefore, in this paper, loess soil of North China and black soil of Northeast China are used as test materials to explore the law of soil heating under the action of microwaves. First, COMSOL Multiphysics software was used to simulate the temperature field change in the microwave-irradiated soil, and a simulation model of the temperature field of the microwave-irradiated soil was constructed to analyze the effective working range and temperature distribution characteristics of the microwave-irradiated soil. Second, using the 2.45 GHz microwave treatment, the following conditions were tested: soil moisture conditions of 10%, 15%, 20% and 23% (within the natural moisture content range). The loess and black soil were treated by microwave irradiation for 1~12 min, respectively (1 min/time increment). A single-factor experiment was designed to explore the influence of these factors on the soil heating law. The results show that the two soil surface temperatures are positively correlated with the soil moisture content, both of which satisfy Tsurface23% > Tsurface20% > Tsurface15% > Tsurface10%, and the surface temperature of black soil is higher than that of loess. According to the experimental results of the internal temperature distribution of loess and black soil irradiated by microwaves, the surface equations of “irradiation time–soil depth-soil temperature” and “irradiation time–soil moisture content–soil temperature” were constructed by surface fitting. When the irradiation time and moisture conditions are the same, the average temperature inside the irradiation area satisfies T¯α black soil > T¯α loess. The results of long-term microwave soil heating preliminarily confirmed the feasibility of microwave soil disinfection and the optimal conditions of microwave irradiation in loess of North China and Northeast black soil, which provides a certain reference for the study of soil-borne diseases inactivation at high temperature.

Background: Bloodstream infection (BSI) is one of the most common serious bacterial infections worldwide and also a major contributor to in-hospital mortality. Determining the predictors of mortality is crucial for prevention and improving clinical prognosis in patients with nosocomial BSI.Methods: A nationwide prospective cohort study was conducted from 2007 until 2016 in 16 teaching hospitals across China. Microbiological results, clinical information, and patient outcomes were collected to investigate the pathogenic spectrum and mortality rate in patients with BSI and identify outcome predictors using multivariate regression, prediction model, and Kaplan–Meier analysis.Results: No significant change was observed in the causative pathogen distribution during the 10-year period and the overall in-hospital mortality was 12.83% (480/3,741). An increased trend was found in the mortality of patients infected with Pseudomonas aeruginosa or Acinetobacter baumannii, while a decreased mortality rate was noted in Staphylococcus aureus-related BSI. In multivariable-adjusted models, higher mortality rate was significantly associated with older age, cancer, sepsis diagnosis, ICU admission, and prolonged hospital stay prior to BSI onset, which were also determined using machine learning-based predictive model achieved by random forest algorithm with a satisfactory performance in outcome prediction.Conclusions: Our study described the clinical and microbiological characteristics and mortality predictive factors in patients with BSI. These informative predictors would inform clinical practice to adopt effective therapeutic strategies to improve patient outcomes.

Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or tractor-based high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R2 = 0.96-0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.

Tea is an economically important crop. Evaluating the suitability of tea can better optimize the regional layout of the tea industry and provide a scientific basis for tea planting plans, which is also conducive to the sustainable development of the tea industry in the long run. Driving force analysis can be carried out to better understand the main influencing factors of tea growth. The main purpose of this study was to evaluate the suitability of tea planting in the study area, determine the prioritization of tea industry development in this area, and provide support for the government’s planning and decision making. This study used Sentinel image data to obtain the current land use data of the study area. The results show that the accuracy of tea plantation classification based on Sentinel images reached 86%, and the total accuracy reached 92%. Then, we selected 14 factors, including climate, soil, terrain, and human-related factors, using the analytic hierarchy process and spatial analysis technology to evaluate the suitability of tea cultivation in the study area and obtain a comprehensive potential distribution map of tea cultivation. The results show that the moderately suitable area (36.81%) accounted for the largest proportion of the tea plantation suitability evaluation, followed by the generally suitable area (31.40%), the highly suitable area (16.91%), and the unsuitable area (16.23%). Among these areas, the highly suitable area is in line with the distribution of tea cultivation at the Yingde municipal level. Finally, to better analyze the contribution of each factor to the suitability of tea, the factors were quantitatively evaluated by the Geodetector model. The most important factors affecting the tea cultivation suitability evaluation were temperature (0.492), precipitation (0.367), slope (0.302), and elevation (0.255). Natural factors influence the evaluation of the suitability of tea cultivation, and the influence of human factors is relatively minor. This study provides an important scientific basis for tea yield policy formulation, tea plantation site selection, and adaptation measures.

Papaver somniferum (opium poppy) is not only a source of raw material for the production of medical narcotic analgesics but also the major raw material for certain psychotropic drugs. Therefore, it is stipulated by law that the cultivation of Papaver somniferum must be authorized by the government under stringent supervision. In certain areas, unauthorized and illicit Papaver somniferum cultivation on private-owned lands occurs from time to time. These illegal Papaver somniferum cultivation sites are dispersedly-distributed and highly-concealed, therefore becoming a tough problem for government supervision. The low-altitude inspection of Papaver somniferum cultivation by unmanned aerial vehicles has the advantages of high efficiency and time saving, but the large amount of image data collected needs to be manually screened, which not only consumes a lot of manpower and material resources but also easily causes omissions. In response to the above problems, this paper proposed a two-stage (target detection and image classification) method for the detection of Papaver somniferum cultivation sites. In the first stage, the YOLOv5s algorithm was used to detect Papaver somniferum images for the purpose of identifying all the suspicious Papaver somniferum images from the original data. In the second stage, the DenseNet121 network was used to classify the detection results from the first stage, so as to exclude the targets other than Papaver somniferum and retain the images containing Papaver somniferum only. For the first stage, YOLOv5s achieved the best overall performance among mainstream target detection models, with a Precision of 97.7%, Recall of 94.9%, and mAP of 97.4%. For the second stage, DenseNet121 with pre-training achieved the best overall performance, with a classification accuracy of 97.33% and a Precision of 95.81%. The experimental comparison results between the one-stage method and the two-stage method suggest that the Recall of the two methods remained the same, but the two-stage method reduced the number of falsely detected images by 73.88%, which greatly reduces the workload for subsequent manual screening of remote sensing Papaver somniferum images. The achievement of this paper provides an effective technical means to solve the problem in the supervision of illicit Papaver somniferum cultivation.

It is an urgent task to improve the applicability of the cucumber disease classification model in greenhouse edge-intelligent devices. The energy consumption of disease diagnosis models designed based on deep learning methods is a key factor affecting its applicability. Based on this motivation, two methods of reducing the model’s calculation amount and changing the calculation method of feature extraction were used in this study to reduce the model’s calculation energy consumption, thereby prolonging the working time of greenhouse edge devices deployed with disease models. First, a cucumber disease dataset with complex backgrounds is constructed in this study. Second, the random data enhancement method is used to enhance data during model training. Third, the conventional feature extraction module, depthwise separable feature extraction module, and the squeeze-and-excitation module are the main modules for constructing the classification model. In addition, the strategies of channel expansion and = shortcut connection are used to further improve the model’s classification accuracy. Finally, the additive feature extraction method is used to reconstruct the proposed model. The experimental results show that the computational energy consumption of the adder cucumber disease classification model is reduced by 96.1% compared with the convolutional neural network of the same structure. In addition, the model size is only 0.479 MB, the calculation amount is 0.03 GFLOPs, and the classification accuracy of cucumber disease images with complex backgrounds is 89.1%. All results prove that our model has high applicability in cucumber greenhouse intelligent equipment.

Extremely low nucleotide diversity of modern horse Y-chromosome has been reported, and only poor phylogenetic resolution could be resulted from limited Y-chromosome markers. In this study, three types of horse Y-chromosome markers, including Single-nucleotide polymorphisms (SNPs), copy number variants (CNVs), and allele-specific CNVs, were developed by screening more than 300 male horses from 23 indigenous Chinese horse populations and 4 imported horse breeds. Fourteen segregating sites including a novel SNP in the AMELY gene were found in approximately 53 kb of male-specific Y-chromosome sequences. CNVs were detected at 11 of 14 sites, while allele-specific CNVs at 6 polymorphic sites in repeated fragments were also determined. The phylogenetic analyses with the SNPs identified in this study and previously published 51 SNPs obtained mainly from European horses showed that indigenous Chinese horses exhibit much deeper divergence than European and Middle Eastern horses, while individuals of Chinese horses with the C allele of the AMELY gene constituted the most ancient group. Via SNPs, CNVs, and allele-specific CNVs, much higher diversity of paternal lines can be detected than those identified with merely SNPs. Our results indicated that there are ancient paternal horse lines preserved in southwestern China, which sheds new light on the domestication and immigration of horses, and suggest that the priorities of the conservation should be given to the ancient and rare paternal lines. These three marker types provided finer phylogenetic resolution of horse patrilineal lines, and the strategies used in the present study also provide valuable reference for the genetic studies of other mammalian patrilineages.

For the calculation model of high-speed angular contact bearing has many variables, the large root difference exists, and the Newton iterative method solving the convergence depends on the initial value problems; thus, the simplified calculation model is proposed and the algorithm is improved. Firstly, based on the nonlinear equations of variables recurrence method of the high-speed angular contact ball bearing calculation model, it is proved that the ultimate fundamental variables of calculation model are the actual inner and outer contact angles, the axial and radial deformations. According to this reason, the nonlinear equations are deformed and deduced, and the number of equations is reduced from 4Z + 2 to 2Z + 2 (Z represents the number of rolling bodies); a simplified calculation model is formed. Secondly, according to the small dependence of the artificial bee colony algorithm on the initial value, an improved artificial bee colony algorithm is proposed for the large root difference characteristics of high-speed ball bearings. The validity of the improved algorithm is verified by standard test function. The algorithm is used to solve the high-speed angular contact ball bearing calculation model. Finally, the deformations of high-speed angular contact ball bearings are compared and verified by experiments, and the results of improved algorithm show good agreement with the experiments results.

The three-dimensional (3D) morphological structure of wheat directly reflects the interrelationship among genetics, environments, and cropping systems. However, the morphological complexity of wheat limits its rapid and accurate 3D modelling. We have developed a 3D wheat modelling method that is based on the progression from skeletons to mesh models. Firstly, we identified five morphological parameters that describe the 3D leaf features of wheat from amounts of 3D leaf digitizing data at the grain filling stage. The template samples were selected based on the similarity between the input leaf skeleton and leaf templates in the constructed wheat leaf database. The leaf modelling was then performed using the as-rigid-as-possible (ARAP) mesh deformation method. We found that 3D wheat modelling at the individual leaf level, leaf group, and individual plant scales can be achieved. Compared with directly acquiring 3D digitizing data for 3D modelling, it saves 79.9% of the time. The minimum correlation R2 of the extracted morphological leaf parameters between using the measured data and 3D model by this method was 0.91 and the maximum RMSE was 0.03, implying that this method preserves the morphological leaf features. The proposed method provides a strong foundation for further morphological phenotype extraction, functional–structural analysis, and virtual reality applications in wheat plants. Overall, we provide a new 3D modelling method for complex plants.

Rotary atomizers are mainly used in agricultural manned aircrafts. Atomization characteristics at high speeds have been studied, but methods to measure the atomization efficiency have not been elucidated. The atomization efficiency of rotary atomizers under high-speed airflow was investigated using an IEA-I high-speed wind tunnel experimental installation, AU5000 rotary atomizer, and a laser diffraction particle size analyzer. Accordingly, a model equation for atomization efficiency measurements was innovatively obtained. When the flow rate, fan blade angle of the atomizer, and wind speed were used as variables, the experimental results showed that the atomization efficiency mainly depended on the fan blade angle. When the fan blade angle was 35°, the atomization efficiency was optimal, regardless of wind speed. In contrast, when the fan blade angle of the atomizer was 65°, it exhibited the worst atomization efficiency, regardless of the wind speed. The experimental data from this study can provide guidance for aerial application in fixed-wing manned aircraft, such as the flow rate, and operating speed.

Robotic High-Throughput Phenotyping (HTP) technology has been a powerful tool for selecting high-quality crop varieties among large quantities of traits. Due to the advantages of multi-view observation and high accuracy, ground HTP robots have been widely studied in recent years. In this paper, we study an ultra-narrow wheeled robot equipped with RGB-D cameras for inter-row maize HTP. The challenges of the narrow operating space, intensive light changes, and messy cross-leaf interference in rows of maize crops are considered. An in situ and inter-row stem diameter measurement method for HTP robots is proposed. To this end, we first introduce the stem diameter measurement pipeline, in which a convolutional neural network is employed to detect stems, and the point cloud is analyzed to estimate the stem diameters. Second, we present a clustering strategy based on DBSCAN for extracting stem point clouds under the condition that the stem is shaded by dense leaves. Third, we present a point cloud filling strategy to fill the stem region with missing depth values due to the occlusion by other organs. Finally, we employ convex hull and plane projection of the point cloud to estimate the stem diameters. The results show that the R2 and RMSE of stem diameter measurement are up to 0.72 and 2.95 mm, demonstrating its effectiveness.

To improve the surface corrosion resistance of ductile iron, Ni-based alloy coatings were prepared using a high-speed laser cladding technology with different levels of laser power. The microstructure, phases, and corrosion properties of the coatings were investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and an electrochemical workstation. Variations in laser power did not change the main phases of the coatings, which were composed of γ-Ni, Ni3B, Ni2Si, and Cr23C6. With an increase in power, the degree of segregation in the coating decreased, sufficient melting between elements was achieved, and the chemical composition became more uniform. Enhancement of the laser power resulted in more energy being injected into the cladding, which allowed adequate growth of tissue, and dendrites continued to grow in size as the power increased. The self-corrosion potentials of the coatings at laser power levels of 1.6, 2.0, and 2.4 kW were −625.7, −526.5, and −335.7 mV, respectively. The corrosion potential of the 2.4 kW coating was the highest, and the corroded surface of the cladding layer included mainly sizeable continuous structures with a light degree of corrosion and the highest corrosion resistance.

Precise localization of occluded fruits is crucial and challenging for robotic harvesting in orchards. Occlusions from leaves, branches, and other fruits make the point cloud acquired from Red Green Blue Depth (RGBD) cameras incomplete. Moreover, an insufficient filling rate and noise on depth images of RGBD cameras usually happen in the shade from occlusions, leading to the distortion and fragmentation of the point cloud. These challenges bring difficulties to position locating and size estimation of fruit for robotic harvesting. In this paper, a novel 3D fruit localization method is proposed based on a deep learning segmentation network and a new frustum-based point-cloud-processing method. A one-stage deep learning segmentation network is presented to locate apple fruits on RGB images. With the outputs of masks and 2D bounding boxes, a 3D viewing frustum was constructed to estimate the depth of the fruit center. By the estimation of centroid coordinates, a position and size estimation approach is proposed for partially occluded fruits to determine the approaching pose for robotic grippers. Experiments in orchards were performed, and the results demonstrated the effectiveness of the proposed method. According to 300 testing samples, with the proposed method, the median error and mean error of fruits’ locations can be reduced by 59% and 43%, compared to the conventional method. Furthermore, the approaching direction vectors can be correctly estimated.

Abstract Background The biomechanical properties of maize stalks largely determine their lodging resistance, which affects crop yield per unit area. However, the quantitative and qualitative relationship between micro-phenotypes and the biomechanics of maize stalks is still under examined. In particular, the roles of the number, geometry, and distribution of vascular bundles of stalks in maize lodging resistance remain unclear. Research on these biomechanical properties will benefit from high-resolution micro-phenotypic image acquisition capabilities, which have been improved by modern X-ray imaging devices such as micro-CT and the development of micro-phenotyping analysis software. Hence, high-throughput image analysis and accurate quantification of anatomical phenotypes of stalks are necessary. Results We have updated VesselParser version 1.0 to version 2.0 and have improved its performance, accuracy, and computation strategies. Anatomical characteristics of the second and third stalk internodes of the cultivars ‘Jingke968’ and ‘Jingdan38’ were analyzed using VesselParser 2.0. The relationships between lodging resistance and anatomical phenotypes of stalks between the two different maize varieties were investigated. The total area of vascular bundles in the peripheral layer, auxiliary axis diameter, and total area of vascular bundles were revealed to have the highest correlation with mechanical properties, and anatomical phenotypes of maize stalk were better predictors of mechanical properties than macro features observed optically from direct measurement, such as diameter and perimeter. Conclusions This study demonstrates the utility of VesselParser 2.0 in assessing stalk mechanical properties. The combination of anatomical phenotypes and mechanical behavior research provides unique insights into the problem of stalk lodging, showing that micro phenotypes of vascular bundles are good predictors of maize stalk mechanical properties that may be important indices for the evaluation and identification of the biomechanical properties to improve lodging resistance of future maize varieties.

Objective An experiment was conducted to study the association between the single nucleotide polymorphisms (SNPs) in 5′-untranslated regions (5′-UTR) of equine chorionic gonadotropin (eCG) genes and the serum eCG levels. Methods SNPs in 5′-UTR of eCG genes were screened across 10 horse breeds, including 7 Chinese indigenous breeds and 3 imported breeds using iPLEX chemistry, and the association between the serum eCG levels of 174 pregnant Da’an mares and their serum eCG levels (determined with ELISA) was analyzed. Results Four SNPs were identified in the 5′-UTR of the eCGα gene, and one of them was unique in the indigenous breeds. There were 2 SNPs detected at the 5′ end of the eCGβ subunit gene, and one of them was only found in the Chinese breeds. The SNP g.39948246T>C at the 5′-UTR of eCGα was associated significantly with eCG levels of 75-day pregnant mare serum (pC mutation causes appearance of the specific binding site of hepatocyte nuclear factor 3 forkhead homolog 2 (HFH-2), which is a transcriptional repressor belonging to the forkhead protein family of transcription factors. Conclusion The SNP g.39948246T>C at the 5′-UTR of eCGα is associated with eCG levels of 75-day pregnant mare serum (p

Abstract Background Streptococcus pneumoniae, the leading pathogen of bacterial infections in infants and the elderly, is responsible for pneumococcal diseases with severe morbidity and mortality. Emergence of drug-resistant strains presented new challenges for treatment and prevention. Vaccination has proven to be an effective means of preventing pneumococcal infection worldwide. Detailed epidemiological information of antibiotic susceptibilities and serotype distribution will be of great help to the management of pneumococcal infections. Methods A total of 881 S. pneumoniae isolates were collected from patients at 23 teaching hospitals in 17 different cities from 2011 to 2016. The main specimen types included sputum, blood, broncho-alveolar lavage fluid, pharyngeal swabs, and cerebrospinal fluid. Minimum inhibitory concentrations (MICs) were determined using the agar dilution method. Capsular serotypes were identified using latex agglutination and quellung reaction test. Molecular epidemiology was investigated using multilocus sequence typing. Results S. pneumoniae isolates were highly resistant to macrolides, tetracycline, and trimethoprim/sulfamethoxazole. The rate of resistance to penicillin was 51.6% (oral breakpoint). However, levofloxacin and moxifloxacin maintained excellent antimicrobial activity and all of the isolated strains were susceptible to vancomycin. Twenty-two serotypes were identified among the 881 isolates. Prevalent serotypes were 19F (25.7%), 19A (14.0%), 15 (6.8%), 6B (3.6%), 6A (3.0%), and 17 (2.8%). The overall vaccine coverage rates for 7- and 13-valent pneumococcal conjugate vaccines were 37.5% and 58.3%, respectively. Vaccine coverage rates in young children and economically underdeveloped regions were higher than those in older adults and developed regions. Vaccine-covered serotypes demonstrated higher resistance compared with uncovered serotypes. Molecular epidemiological typing demonstrated that S. pneumoniae showed significant clonal dissemination and that ST271 (120, 28.3%), ST320 (73, 17.2%) and ST81 (27, 6.6%) were the major STs. Conclusions High resistance to clinical routine antibiotics was observed for all 881 S. pneumoniae strains. Drug resistance varied among different serotypes and age groups. Prevalent serotypes among the isolates were 19F, 19A, 15, 6B, 6A, and 17. Community-acquired strains should also be included in future studies to gain a better understanding of the prevalence and resistance of S. pneumoniae in China.

Microwave treatment is a green and pollution-free soil disinfection method. The application of microwaves to disinfect soil before cultivation is highly important to increase crop yields and protect the ecological environment. The electromagnetic field is an important parameter influencing the soil temperature field in the process of microwave soil treatment, and the change in soil temperature directly affects soil disinfection. Therefore, this article carried out research on the heating pattern in North China loess due to microwave treatment. First, COMSOL software was employed to simulate the microwave soil treatment process to analyze microwave penetration into soil. Second, with the application of microwaves at the designed frequency produced with a 2.45-GHz tunable microwave generating microdevice, soil with water contents of 0%, 10%, 20%, and 30% was treated for 10~60 s (at 10-s time intervals), and experiments on the influence of the microwave output power, treatment time, and soil moisture content on the soil temperature were performed via the controlled variable method. The simulation results indicate that with increasing soil moisture content, the microwave frequency inside the soil model increases, and the electric field intensity value decreases in the model at the same depth. After microwaves traverse through the 20-cm soil model, the incident field strength is three orders of magnitude lower than the outgoing field strength. The results of the microwave soil treatment experiment reveal that: (1) Compared to microwave output power levels of 1.8 and 1.6 kW, a level of 2 kW is more suitable for microwave soil disinfection. (2) After treatment, the highest temperature occurs on the soil surface, not within the soil. (3) The location of the highest soil internal temperature after microwave treatment increasingly approaches the soil surface with increasing soil moisture content, and the microwave output power does not affect the location of the highest soil internal temperature. Combining the electromagnetic field simulation and microwave soil treatment experiment results, it was found that the higher the field strength is, the higher the temperature value, and the highest soil internal temperature after microwave treatment often occurs at the first electromagnetic wave peak.

The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.

This paper presents the communication strategy for second-order multi-agent systems with nonlinear dynamics. To address the problem of the scarcity of communication channel resources and get rid of using continuous signals among the followers in lead-follower multi-agent systems, a novel event-triggered communication mechanism is proposed in this paper. To avoid employing the centralized information that depends on the Laplacian matrix spectrum, a network protocol with updated coupling gains is proposed, as well as an event-triggered strategy with updated thresholds. To eliminate the ill effects of inter-node communicating noise, relative positions are employed by the protocol instead of absolute positions. By a Lyapunov–Krasovskii functional, it is rigorously proven that the leader-following consensus of MASs is achieved without Zeno behavior, under the control of the proposed protocol with an event-triggered mechanism communication. The effectiveness of the proposed protocol is verified through numerical examples.

Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (R2 = 0.82, RMSE = 79.80 g/m2, NRMSE = 11.12%) and the PLSR method (R2 = 0.86, RMSE = 72.28 g/m2, NRMSE = 10.07%). The results indicate that PLSR more accurately estimates AGB than MLR, with R2 increasing by 0.04, root mean square error (RMSE) decreasing by 7.52 g/m2, and normalized root mean square error (NRMSE) decreasing by 1.05%. In addition, the AGB is more accurately estimated by combining LiDAR with multispectral data than LiDAR and multispectral data alone, with R2 increasing by 0.13 and 0.30, respectively, RMSE decreasing by 22.89 and 54.92 g/m2, respectively, and NRMSE decreasing by 4.46% and 7.65%, respectively. This study improves the prediction accuracy of AGB and provides a new guideline for monitoring based on the fusion of multispectral and LiDAR data.

Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called ‘phenomics.’ The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.

In this study, laser cladding technology was used to prepare Fe-based alloy coating on a 27SiMn hydraulic support, and a turning treatment was used to obtain samples of the upper and middle regions of the cladding layer. The influence of microstructure, phase composition, hardness, and wear resistance in different areas of the cladding layer was studied through scanning electron microscopy (SEM), X-ray diffractometry (XRD), friction and wear tests, and microhardness. The results show that the bcc phase content in the upper region of the cladding layer is less than that in the middle region of the cladding layer, and the upper region of the cladding layer contains more metal compounds. The hardness of the middle region of the cladding layer is higher than that of the upper region of the cladding layer. At the same time, the main wear mechanism of the upper region of the cladding layer is adhesive wear and abrasive wear. The wear mechanism of the middle region of the cladding layer is mainly abrasive wear, with better wear resistance than the upper region of the cladding layer.

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data.

Poppy is a special medicinal plant. Its cultivation requires legal approval and strict supervision. Unauthorized cultivation of opium poppy is forbidden. Low-altitude inspection of poppy illegal cultivation through unmanned aerial vehicle is featured with the advantages of time-saving and high efficiency. However, a large amount of inspection image data collected need to be manually screened and analyzed. This process not only consumes a lot of manpower and material resources, but is also subjected to omissions and errors. In response to such a problem, this paper proposed an inspection method by adding a larger-scale detection box on the basis of the original YOLOv3 algorithm to improve the accuracy of small target detection. Specifically, ResNeXt group convolution was utilized to reduce the number of model parameters, and an ASPP module was added before the small-scale detection box to improve the model’s ability to extract local features and obtain contextual information. The test results on a self-created dataset showed that: the mAP (mean average precision) indicator of the Global Multiscale-YOLOv3 model was 0.44% higher than that of the YOLOv3 (MobileNet) algorithm; the total number of parameters of the proposed model was only 13.75% of that of the original YOLOv3 model and 35.04% of that of the lightweight network YOLOv3 (MobileNet). Overall, the Global Multiscale-YOLOv3 model had a reduced number of parameters and increased recognition accuracy. It provides technical support for the rapid and accurate image processing in low-altitude remote sensing poppy inspection.

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

Traditional sprayers adopt large-area uniform pesticide application, resulting in a low effective utilisation of pesticide and a harmful effect to the natural environment. Variable-rate spray is the key point to precision chemical application. However, it is inevitable that the spray pressure sharply fluctuates during variable-rate spray, which will definitely influence the spray characteristics, such as spray droplet sizes, spray angles, spray droplet velocities, etc., and reduce the efficiency of pesticide applications. Therefore, the research on how to keep the spray pressure constant during the process of variable- rate spray has practical significance to precision pesticide applications. In order to achieve the stability of spray pressure for variable- rate spray, a sprayer with constant-pressure control was set up using a closed-loop proportion integration differentiation (PID) controller of constant-pressure water-supply, which employed the techniques of single-phase alternating current (AC) chopper variablevoltage control and PID feedback regulation. Using hollow-cone nozzles, the spray volume was changed by adjusting spray pressure, frequency, and duty cycle of electromagnetic valve switching. The spray features concerning the spray angle and the spray volume distribution were studied in laboratory. The conclusions are as follows: i) for a given input spray pressure, the closed-loop controller with AC chopper and PID feedback regulation can effectively control the pressure fluctuations during the variable-rate spray through varying the frequency and the duty cycle; ii) the spray angle was slightly affected by the flow-rate change through adjusting the frequency and the duty cycle of electromagnetic valves or changing the number of open nozzles. The spray angle changes were controlled within the range of the maximum deviation 0.87° from mean value and the minimum deviation 0.03° from mean value; iii) when the spray pressure was set as 0.3 Mpa, the peak radial position of the spray volume distribution basically unchanged and the spray volume and the peak values of the spray distribution gradually increased with the increasing duty cycle. When the setting pressure was increased, the spray volume increased and the peak radial position of the spray distribution gradually expanded toward outside, however the peak value of the spray distribution decreased a little.

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

The planting year of apple orchard not only determines the fruit output but also provides information for the governmental management of the fruit industry. However, considering that different orchards use different management and cultivation methods, this may result in some trees having similar outlines but different planting years, and it is, therefore, difficult to effectively determine the actual planting year based on textural or structural characteristics. Therefore, the monitoring method provided in this paper is not to monitor the growing year positively from the planting of orchard seedlings but to use time series remote sensing data to reverse determine the continuous growth age of each existing orchard. The city of Qixia, Shandong Province, China, was used as a case study. Firstly, the spatial distribution of apple orchards was accurately extracted using the Sentinel-2 normalized difference vegetation index (NDVI) spatiotemporally fused images and phenological vegetation information. Secondly, using region of interest (ROI) data for different vegetation types obtained from a field survey, NDVI time series were extracted from the Sentinel-2 NDVI spatiotemporally fused image. Among them, three characteristic phenological periods were selected, and the NDVI time series for apple orchards was used as a template to extract the apple orchard distribution area from 2000 to 2017. Then, the distribution area of apple orchards was defined as the area of interest in the planting year, combined with the Landsat NDVI time series image composed of three characteristic phenological periods each year from 2000 to 2017, and the apple orchard phenological curve. Subsequently, a Euclidean distance (ED) method was used to calculate the distribution area of apple orchards for each year between 2000 and 2017. Finally, a pixel-by-pixel inverse time series calculation method was used to obtain the planting year of apple orchards in the study area. This study provides a new way to accurately identify the planting year of apple orchards using satellite remote sensing images.

Objectives There are large populations of indigenous horse (Equus caballus) in China and some other parts of East Asia. However, their matrilineal genetic diversity and origin remained poorly understood. Using a combination of mitochondrial DNA (mtDNA) and hypervariable region (HVR-1) sequences, we aim to investigate the origin of matrilineal inheritance in these domestic horses. Methods To investigate patterns of matrilineal inheritance in domestic horses, we conducted a phylogenetic study using 31 de novo mtDNA genomes together with 317 others from the GenBank. In terms of the updated phylogeny, a total of 5,180 horse mitochondrial HVR-1 sequences were analyzed. Results Eightteen haplogroups (Aw-Rw) were uncovered from the analysis of the whole mitochondrial genomes. Most of which have a divergence time before the earliest domestication of wild horses (about 5,800 years ago) and during the Upper Paleolithic (35–10 KYA). The distribution of some haplogroups shows geographic patterns. The Lw haplogroup contained a significantly higher proportion of European horses than the horses from other regions, while haplogroups Jw, Rw, and some maternal lineages of Cw, have a higher frequency in the horses from East Asia. The 5,180 sequences of horse mitochondrial HVR-1 form nine major haplogroups (A-I). We revealed a corresponding relationship between the haplotypes of HVR-1 and those of whole mitochondrial DNA sequences. The data of the HVR-1 sequences also suggests that Jw, Rw, and some haplotypes of Cw may have originated in East Asia while Lw probably formed in Europe. Conclusions Our study supports the hypothesis of the multiple origins of the maternal lineage of domestic horses and some maternal lineages of domestic horses may have originated from East Asia.

To investigate the epidemiology and genetic structure of Staphylococcus aureus bacteremia in China, a total of 416 isolates from 22 teaching hospitals in 12 cities from 2013 and 2016 were characterized by antibiogram analysis, multilocus sequence typing (MLST), spa typing and staphylococcal cassette chromosome mec (SCCmec) typing. The predominant meticillin-susceptible (MSSA) genotypes in 2013 were ST188 (19.1%), ST7 (8.7%), and ST398 (7.8%), respectively, and they continued to be the main genotypes in 2016. The prevalence of meticillin-resistant S. aureus (MRSA) were 36.5% (66/181) and 36.6% (86/235) in 2013 and 2016, respectively. Interestingly, the susceptibility rates of MRSA to rifampicin and fluoroquinolones increased significantly from 2013 to 2016 (P < 0.01), and this was associated with changes in genetic structure. ST239-t030-MRSA, the predominant genotype among all MRSAs in 2013 (34.8%), was replaced by ST59-t437-MRSA (15.1%) in 2016. Further analysis revealed that the ST239-t030-MRSA were more resistant to rifampicin, tetracycline and fluoroquinolones than ST59-t437-MRSA (P < 0.01). To further gain insight into the mechanisms underlying the changes of genetic structure, in vitro competition and fitness measurements were performed. Importantly, ST239-t030-MRSA displayed lower growth rate and lower competitive advantage compared to ST59-t437-MRSA. Together, our findings reveal that fitness advantage of ST59-t437-MRSA over ST239-t030-MRSA may lead to changes in genetic structure and increased susceptibility of MRSA to rifampicin and fluoroquinolones in Chinese patients with S. aureus bacteremia. Our study supports temporal dynamics in MRSA clone diversities, further providing critical insights into the importance of continued monitoring of MRSA.

The effects of thickness reduction, feed ratio, and ball diameter, and their coupling effects, on the average relative stress triaxiality during spinning are discussed via simulation results. The relationships among the parameters and the average value of relative stress triaxiality (AVRST) are fitted with multiple nonlinear functions to calculate the optimal process parameters. According to the trend of stress triaxiality, the corresponding process parameters are calculated for the minimum average value of relative stress triaxiality (AVRST). Room temperature experiments performed on an AZ31 magnesium alloy thin-walled tube with the optimal parameters reveal an improvement of cracking of the tube surface. The study reveals changes in the minimum AVRST and aids in selecting the process parameters to improve plastic performance.

Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) to estimate LAI and biomass with combined OSVIs and RPPs (the product of OSVIs and RPPs (COSVI-RPPs)), (3) to use multiple stepwise regression (MSR) and partial least squares regression (PLSR) to test and compare the estimations of LAI and biomass in winter wheat, respectively. The results showed that LAI and biomass were highly correlated with several OSVIs (the enhanced vegetation index (EVI) and modified triangular vegetation index 2 (MTVI2)) and RPPs (the radar vegetation index (RVI) and double-bounce eigenvalue relative difference (DERD)). The product of MTVI2 and DERD (R2 = 0.67 and RMSE = 0.68, p < 0.01) and that of MTVI2 and RVI (R2 = 0. 68 and RMSE = 0.65, p < 0.01) were strongly related to LAI, and the product of the optimized soil adjusted vegetation index (OSAVI) and DERD (R2 = 0.79 and RMSE = 148.65 g/m2, p < 0.01) and that of EVI and RVI (R2 = 0. 80 and RMSE = 146.33 g/m2, p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.

Spray deposition and distribution are affected by many factors, one of which is nozzle flow distribution. A two-dimensional automatic measurement system, which consisted of a conveying unit, a system control unit, an ultrasonic sensor, and a deposition collecting dish, was designed and developed. The system could precisely move an ultrasonic sensor above a pesticide deposition collecting dish to measure the nozzle flow distribution. A sensor sleeve with a PVC tube was designed for the ultrasonic sensor to limit its beam angle in order to measure the liquid level in the small troughs. System performance tests were conducted to verify the designed functions and measurement accuracy. A commercial spray nozzle was also used to measure its flow distribution. The test results showed that the relative error on volume measurement was less than 7.27% when the liquid volume was 2 mL in trough, while the error was less than 4.52% when the liquid volume was 4 mL or more. The developed system was also used to evaluate the flow distribution of a commercial nozzle. It was able to provide the shape and the spraying width of the flow distribution accurately.

The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve simulated output of the grain yield and protein content of winter wheat. The results show that the modified simple ratio (MSR) and normalized difference red edge (NDRE) better estimated LAI and CNA, respectively, compared with the other possible vegetation indices. The integration of both LAI and CNA resulted in a more robust DSSAT-CERES models with than each one alone. The R2 and RMSE values, respectively, of the regression between the simulated (using the two assimilation variables method) and measured LAI were 0.828 and 0.494, and for CNA were 0.808 and 20.26 kg N∙ha−1. These two assimilation variables resulted in grain yield and protein content estimates of winter wheat with a high precision and R2 and RMSE values of 0.698 and 0.726 ton∙ha−1, and 0.758% and 1.16%, respectively. This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data.

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

Improper postsowing compaction negatively affects soil temperature and thereby cold and frost tolerance, particularly in extreme cold weather. In North China Plain, the temperature falls to 5 degrees below zero, even lower in winter, which is period for winter wheat growing. Thus improving temperature to promote wheat growth is important in this area. A field experiment from 2013 to 2016 was conducted to evaluate effects of postsowing compaction on soil temperature and plant population of wheat at different stages during wintering period. The effect of three postsowing compaction methods—(1) compacting wheel (CW), (2) crosskill roller (CR), and (3) V-shaped compacting roller after crosskill roller (VCRCR)—on winter soil temperatures and relation to wheat shoot growth parameters were measured. Results showed that the highest soil midwinter temperature was in the CW treatment. In the 20 cm and 40 cm soil layer, soil temperatures were ranked in the following order of CW > VCRCR > CR. Shoot numbers under CW, CR, and VCRCR treatments were statistically 12.40% and 8.18% higher under CW treatment compared to CR or VCRCR treatments at the end of wintering period. The higher soil temperature under CW treatment resulted in higher shoot number at the end of wintering period, apparently due to reduced shoot death by cold and frost damage.

Spatial monitoring of the sowing date plays an important role in crop yield estimation at the regional scale. The feasibility of using polarimetric synthetic aperture radar (SAR) data for early season monitoring of the sowing dates of oilseed rape (Brassica napus L.) fields is explored in this paper. Polarimetric SAR responses of six parameters, relying on polarization decomposition methods, were investigated as a function of days after sowing (DAS) during the entire growing season, by means of five consecutive Radarsat-2 images. A near-continuous temporal evolution of these parameters was observed, based on 88 oilseed rape fields. It provided a solid basis for determining the suitable temporal window and the best polarimetric parameters for sowing date monitoring. A high sensitivity of all polarimetric parameters to the DAS at different growing stages was shown. Simple linear models could be calibrated to estimate sowing dates at early growth stages and were validated on an independent data set. Although Volume and Span parameters provided the highest sowing date estimation accuracy at the earlier growth stages, the other four parameters (Volume/Total, Odd/Total, Entropy and Alpha) were more accurate for a wider temporal window. These results demonstrate the capability and high potential of polarimetric SAR data for monitoring the sowing date of crops in the early season.

High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.

Plant nitrogen (N) information has widely been estimated through empirical techniques using hyperspectral data. However, the physical model inversion approach on N spectral response has seldom developed and remains a challenge. In this study, an N-PROSAIL model based on the N-based PROSPECT model and the SAIL model canopy model was constructed and used for retrieving crop N status both at leaf and canopy scales. The results show that the third parameter (3rd-par) retrieving strategy (leaf area index (LAI) and leaf N density (LND) optimized where other parameters in the N-PROSAIL model are set at different values at each growth stage) exhibited the highest accuracy for LAI and LND estimation, which resulted in R2 and RMSE values of 0.80 and 0.69, and 0.46 and 21.18 µg·cm−2, respectively. It also showed good results with R2 and RMSE values of 0.75 and 0.38% for leaf N concentration (LNC) and 0.82 and 0.95 g·m−2 for canopy N density (CND), respectively. The N-PROSAIL model retrieving method performed better than the vegetation index regression model (LNC: RMSE = 0.48 − 0.64%; CND: RMSE = 1.26 − 1.78 g·m−2). This study indicates the potential of using the N-PROSAIL model for crop N diagnosis on leaf and canopy scales in wheat.

Cotton (Gossypium hirsutum L.) is an economically important crop that is highly susceptible to cotton root rot. Remote sensing technology provides a useful and effective means for detecting and mapping cotton root rot infestations in cotton fields. This research assessed the potential of 10-m Sentinel-2A satellite imagery for cotton root rot detection and compared it with airborne multispectral imagery using unsupervised classification at both field and regional levels. Accuracy assessment showed that the classification maps from the Sentinel-2A imagery had an overall accuracy of 94.1% for field subset images and 91.2% for the whole image, compared with the airborne image classification results. However, some small cotton root rot areas were undetectable and some non-infested areas within large root rot areas were incorrectly classified as infested due to the images’ coarse spatial resolution. Classification maps based on field subset Sentinel-2A images missed 16.6% of the infested areas and the classification map based on the whole Sentinel-2A image for the study area omitted 19.7% of the infested areas. These results demonstrate that freely-available Sentinel-2 imagery can be used as an alternative data source for identifying cotton root rot and creating prescription maps for site-specific management of the disease.

Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix) for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix). In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix). The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed that (1) the The proposed ClassPair_ScatterMatrix method could better alleviate the problem of selecting mutually redundant features, compared to the AllClass_ScatterMatrix method; (2) compared with the representative mutual information-based feature selection methods, the scatter-matrix-based feature selection methods generally achieved higher classification accuracies, and the ClassPair_ScatterMatrix method especially, produced the highest classification accuracies with respect to both data sets (87.2% and 90.1%); and (3) the proposed integrated scheme produced higher classification accuracy, compared with the decision fusion of spectral and spatial features and the methods only involving spectral features or spatial features. The comparative experiments demonstrate the effectiveness of the proposed scheme.

Mn co-precipitation method combined with Raman spectroscopy were used to determine trace heavy metals (copper, zinc, cadmium and lead) in water sample. Different concentrations of heavy metals including copper, zinc, cadmium and lead in water samples were separated and enriched by Mn2+-phen-SCN- ternary complex co-precipitation procedure. The Raman spectra of co-precipitation sediments were collected using confocal micro-Raman spectrometry. Different preprocessing treatments and regression calibration methods were compared. The best models using partial least squares regression (PLS) of copper, zinc, cadmium and lead were built with a correlation coefficient of prediction (Rp) of 0.979, 0.964, 0.956 and 0.972, respectively, and the root mean square error of prediction (RMSEP) of 6.587, 9.046, 9.998 and 7.751 μg/kg, respectively. The co-precipitation procedure combined with Raman spectroscopy method are feasible to detect the amount of heavy metals in water.

Streptococcus pneumoniae is an important pathogen in both children and the elderly, but previous studies in China have provided limited information about invasive pneumococcal disease (IPD). A total of 240 IPD S. pneumoniae strains (from 105 children and 135 adults) were collected from 12 cities in China in 2005-2011. Their phenotypes and genetic characteristics were analyzed. Streptococcus pneumoniae remained highly resistant to macrolides, tetracycline, and cotrimoxazole each year. Serotypes were assigned to the 240 isolates, and 19A (22.1%), 19F (21.7%), 14 (7.5%), 3 (7.1%), and 23F (5.4%) were the most prevalent, accounting for 63.8% of all strains. Serogroup 19 strains were significantly more common among children than among adults (58.7% vs 32.4%, respectively; P < 0.001). Serotypes 19F and 19A demonstrated higher resistance to β-lactams and cephalosporins than the other serotypes. The coverage of PCV13 was superior to that calculated for PCV7 and PCV10 (77.9% vs 40.8% and 47.1%, respectively), and coverage was higher in children than in adults (85.6% vs 72.1%, respectively; P = 0.012). A multilocus sequence typing analysis revealed great diversity, with nine clonal complexes and 83 singletons among all the strains. Specifically, CC271 was more common in children, whereas singletons were more prevalent in adults. Among the serogroup 19 strains, 84.7% were ST271, ST320, or ST236, belonging to CC271. The homogeneous genetic background of 19F and 19A, together with the high resistance of these strains, suggests that clonal spread is responsible for the high prevalence of serogroup 19 in IPD. This is the first large study to investigate IPD strains in both children and adults in China.

Heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA) is associated with clinical treatment failure. However, the resistance mechanism of hVISA has not been fully clarified. In the present study, comparative proteomics analysis of two pairs of isogenic vancomycin-susceptible S. aureus (VSSA) and hVISA strains isolated from two patients identified five differentially expressed proteins, IsaA, MsrA2, Asp23, GpmA, and AhpC, present in both isolate pairs. All the proteins were up-regulated in the hVISA strains. These proteins were analyzed in six pairs of isogenic VSSA and hVISA strains, and unrelated VSSA (n = 30) and hVISA (n = 24) by real-time quantitative reverse transcriptase-PCR (qRT-PCR). Of the six pairs of isogenic strains, isaA, msrA2 and ahpC were up-regulated in all six hVISA strains; whereas asp23 and gpmA were up-regulated in five hVISA strains compared with the VSSA parental strains. In the unrelated strains, statistical analyses showed that only isaA was significantly up-regulated in the hVISA strains. Analysis of the five differentially expressed proteins in 15 pairs of persistent VSSA strains by qRT-PCR showed no differences in the expression of the five genes among the persistent strains, suggesting that these genes are not associated with persistence infection. Our results indicate that increased expression of isaA may be related to hVISA resistance.

A comparative genomic microarray comprising 2,457 genes from two whole genomes of S. aureus was employed for the comparative genome hybridization analysis of 50 strains of divergent clonal lineages, including methicillin-resistant S. aureus (MRSA), methicillin-susceptible S. aureus (MSSA), and swine strains in China. Large-scale validation was confirmed via polymerase chain reaction in 160 representative clinical strains. All of the 50 strains were clustered into seven different complexes by phylogenetic tree analysis. Thirteen gene clusters were specific to different S. aureus clones. Ten gene clusters, including seven known (vSa3, vSa4, vSaα, vSaβ, Tn5801, and phage ϕSa3) and three novel (C8, C9, and C10) gene clusters, were specific to human MRSA. Notably, two global regulators, sarH2 and sarH3, at cluster C9 were specific to human MRSA, and plasmid pUB110 at cluster C10 was specific to swine MRSA. Three clusters known to be part of SCCmec, vSa4 or Tn5801, and vSaα as well as one novel gene cluster C12 with homology with Tn554 of S. epidermidis were identified as MRSA-specific gene clusters. The replacement of ST239-spa t037 with ST239-spa t030 in Beijing may be a result of its acquisition of vSa4, phage ϕSa1, and ϕSa3. In summary, thirteen critical gene clusters were identified to be contributors to the evolution of host specificity and antibiotic resistance in Chinese S. aureus.