Search

The accurate large-scale measurement of peach crowns is vital in horticultural science and the optimization of orchard management. Nowadays, numerous crown parameters (e.g., crown area, height, and volume) can be obtained via the analysis of point clouds or photographs. Current laser-based sensors provide the required reliable and accurate information; however, they are costly and time-consuming. Therefore, a simpler approach for crown measurement is required. For this purpose, this study presents a pipeline for the monitoring and clustering of 259 peach tree crowns based on unmanned aerial vehicle (UAV) images of a peach orchard in Southeast China. Considering the limitation that the original aerial image dataset contains little information, a data augmentation process is adopted, and an efficient deep learning architecture based on conditional generative adversarial networks (cGANs) was designed to extract the crown area. Then, the shape of the crown area was clustered using an edge detection process and a $k$ -means algorithm. Finally, an ellipsoid volume method (EVM) was applied to estimate the crown volume. Five indicators—namely, $Q_{\mathrm {seg}}$ , $S_{\mathrm {r}}$ , Precision, Recall, and F-measure—were employed to evaluate the crown extraction effects, and the average results for testing samples were 0.832, 0.847, 0.851, 0.828, and 0.846, respectively. Compared with other approaches—namely, fully convolutional network (FCN), U-Net, SegNet21, the excess green index (ExG), and the color index of vegetation extraction (CIVE)—the proposed cGAN model performs better, achieving an accuracy improvement of 5%–25%. For the estimation of crown volume, using measurements from a light detection and ranging (LIDAR) scanner as a reference, the correlation coefficient and relative-root-mean-square error (R-RMSE) were found to be 0.836% and 14.93%, respectively. Overall, the results demonstrate that the proposed method is feasible for measuring peach tree crowns. The wide application of such technology would facilitate applied research in plant phenotyping and precision horticulture. [ABSTRACT FROM AUTHOR]

In this study, a simple one‐step hydrothermal synthesis of three‐element complex system photocatalyst technology is systematically studied. The results show that due to the complex heterojunction formed by titanium dioxide (TiO2), tungsten trioxide (WO3) and activated carbon (AC) and the strong adsorption performance of AC, the catalytic efficiency and cycle stability of the composite have been significantly improved. The excellent electron transport ability of AC is conducive to the separation of carriers, so that the electron transfer between AC/TiO2 and AC/WO3 can make it achieve a heterojunction effect similar to TiO2/WO3. The rich specific surface area of AC and the pores of the "labyrinth" structure can provide a higher concentration of degradable substances inside the catalyst, and the degraded substances are quickly transferred to the active site for degradation. Therefore, by controlling the mass ratio of AC/TiO2/WO3, a photocatalyst with high catalytic efficiency and strong cycle stability was successfully prepared (the degradation efficiency is above 95 %, and the degradation efficiency after 5 cycles is above 80 %). [ABSTRACT FROM AUTHOR]

Fast and accurate counting of wheat ears in field conditions is a key element for determining wheat yield. To obtain the number of wheat ears in a field, we propose a new counting algorithm based on computer vision. This algorithm counts wheat ears in remote images through semantic segmentation regression network (SSRNet). SSRNet is a multistage convolutional neural network that we propose to achieve counting problems through regression. In SSRNet, first, the original image is cropped to increase the amount of data. This method effectively solves the small sample dataset. Next, based on the cropping results, we build a fully convolutional neural network (FCNN) to segment wheat ears in field conditions. FCNN increases the accuracy of wheat ears counting by accurately segmenting wheat ears in a complex background. Then, we build a regression convolutional neural network (RCNN) to count wheat ears based on the segmentation results of FCNN. In RCNN, we propose a new activation function positive rectification linear unit (PrLU) to process the last layer of the fully connected layer, so that RCNN can effectively count the number of wheat ears in the image. Finally, a counting strategy is proposed to count the number of wheat ears in the original image. To verify the counting performance of SSRNet, we compare the counting result of SSRNet with the real value of manual statistics. The results show that the average accuracy (Acc), $R^{2}$ , and root mean squared error (RMSE) of the SSRNet count results on the test set in this article are 0.980, 0.996, and 9.437, respectively. It can be seen from the results that our proposed method can accurately count wheat ears in field conditions. At the same time, the counting time (0.11 s) shows that SSRNet can quickly estimate the number of wheat ears in field conditions. We concluded that this study can provide important technical support for the high-throughput field wheat ears counting task in large-scale phenotyping work. [ABSTRACT FROM AUTHOR]

Activated carbons with different porosities derived from coffee, which prepared by the potassium hydroxide (KOH) activation method, are used as electrode for supercapacitors. The prepared materials indicated a well‐designed porous structure that provides countless adsorption sites, leading to high supercapacitor performance. Mesopores allow rapid diffusion of electrolyte ions, while micropores usually provide abundant adsorption sites for ions. Therefore, appropriate control over pore‐size distributions is a key factor for good electrochemical performance. The porosity and surface structure of activated coffee grounds (ACGs) are highly dependent on both the activation temperature and KOH/carbonized materials weight ratio. The ACGs were characterized using N2 adsorption at 77 K. The specific surface area increased from 1.75 to 3065 m2/g. Cyclic voltammetry and galvanostatic charge–discharge tests were employed in the evaluation of the electrochemical behavior of each sample. The ACG materials have a high specific capacitance of 127.8 F/g and cycle stability after 2500 cycles. [ABSTRACT FROM AUTHOR]

The accuracy of nitrogen (N) diagnosis is essential to improve N use efficiency. The standard critical N concentration (standard Nc) dilution curves, an expression of the dynamics of N uptake and dry matter accumulation in plants, are widely used to diagnose the N status of crops. Several standard Nc dilution curves were proposed and validated for several crops, based on experiments involving different N fertilizer treatments. However, standard Nc dilution curves are affected by crop water status, e.g., resulting from differences in irrigation management. This paper aimed at developing a N diagnostic model under the coupling effect of irrigation and fertilizer managements. For this purpose, Nc dilution curves were developed under different irrigation rates. Additionally, plant water content (PWC), leaf water content (LWC), leaf area index (LAI), equivalent water thickness (EWT), and leaf area duration (LAD) were introduced into the model, to construct a modified Nc (mNc) dilution curve. The mNc dilution curves were designed using the principle of hierarchical linear model (HLM), introducing aboveground dry biomass (AGB) as the first layer of information, whereas the second layer of information included the different agronomic variables (PWC, LWC, LAI, EWT, and LAD). The results showed that parameters " a " and " b" of the standard Nc dilution curves ranged from 5.17 to 6.52 and −0.69 to −0.38 respectively. Parameter " a " was easily affected by different management conditions. The performance of standard Nc dilution models obtained by the cross-validation method was worse than that of mNc dilution models. The Nc dilution curve based on 4 years of data was described by the negative power equation Nc = 5.05 × AGB–0.47, with R 2 and nRMSE of 0.63 and 0.21, respectively. The mNc dilution curve considers different treatments and was represented by the equation mNc = a×AGB− b , where a = 2.09 × PWC + 3.24, b = −0.02 × LAI + 0.51, with R 2 and nRMSE of 0.79 and 0.13, respectively. For winter wheat, C3 crop, there would be a few problems in using standard Nc dilution methods to guide field management, however, this study provides a reliable method for constructing mNc dilution curves under different water and N fertilizer management. Due to the significant differences in hereditary, CO2 fixation efficiency and N metabolism pathways for C3 and C4 crops, the construction of mNc dilution curve suitable for different N response mechanisms will be conducive to the sustainable N management in crop plants. [ABSTRACT FROM AUTHOR]

The avian EB66® cell line, derived from duck embryonic stem cells, has been widely used for producing human and animal therapeutic proteins and vaccines. In current study we evaluated the potential use of EB66® cell line in a cell culture-derived duck Tembusu virus (DTMUV) vaccine development. After optimizing the growth conditions of DTMUV HB strain in EB66® cells, we successfully generated three batches of viruses with ELD50 titres of 105.9/0.1 ml, 105.3/0.1 ml and 105.5/0.1 ml, respectively, for using in the preparation of inactivated vaccines. The immunogenicity and protective efficacy of these EB66® cells-derived inactivated vaccines were examined in ducks. Results indicated that all three batches of vaccines induced haemagglutination-inhibition (HI) antibody response in immunized birds at 2 weeks after a single immunization. Immunized ducks and ducklings were protected against a virulent challenge at 4 weeks after a booster immunization. The duration of immunity was for 3–4 months after a booster immunization. These results demonstrated the feasibility of using EB66® cell line to grow up DTMUV for vaccine preparation. RESEARCH HIGHLIGHTS Duck Tembusu virus can be propagated in EB66® cells. EB66® cell-derived inactivated DTMUV vaccines are immunogenic and can provide protection against a virulent challenge. A long-lasting immunity is induced after a booster immunization. [ABSTRACT FROM AUTHOR]

The automated harvesting of strawberry brings benefits such as reduced labor costs, sustainability, increased productivity, less waste, and improved use of natural resources. The accurate detection of strawberries in a greenhouse can be used to assist in the effective recognition and location of strawberries for the process of strawberry collection. Furthermore, being able to detect and characterize strawberries based on field images is an essential component in the breeding pipeline for the selection of high-yield varieties. The existing manual examination method is error-prone and time-consuming, which makes mechanized harvesting difficult. In this work, we propose a robust architecture, named "improved Faster-RCNN," to detect strawberries in ground-level RGB images captured by a self-developed "Large Scene Camera System." The purpose of this research is to develop a fully automatic detection and plumpness grading system for living plants in field conditions which does not require any prior information about targets. The experimental results show that the proposed method obtained an average fruit extraction accuracy of more than 86%, which is higher than that obtained using three other methods. This demonstrates that image processing combined with the introduced novel deep learning architecture is highly feasible for counting the number of, and identifying the quality of, strawberries from ground-level images. Additionally, this work shows that deep learning techniques can serve as invaluable tools in larger field investigation frameworks, specifically for applications involving plant phenotyping. [ABSTRACT FROM AUTHOR]

Achieving the non-contact and non-destructive observation of broccoli head is the key step to realize the acquisition of high-throughput phenotyping information of broccoli. However, the rapid segmentation and grading of broccoli head remains difficult in many parts of the world due to low equipment development level. In this paper, we combined an advanced computer vision technique with a deep learning architecture to allow the acquisition of real-time and accurate information about broccoli head. By constructing a private image dataset with 100s of broccoli-head images (acquired using a self-developed imaging system) under controlled conditions, a deep convolutional neural network named "Improved ResNet" was trained to extract the broccoli pixels from the background. Then, a yield estimation model was built based on the number of extracted pixels and the corresponding pixel weight value. Additionally, the Particle Swarm Optimization Algorithm (PSOA) and the Otsu method were applied to grade the quality of each broccoli head according to our new standard. The trained model achieved an Accuracy of 0.896 on the test set for broccoli head segmentation, demonstrating the feasibility of this approach. When testing the model on a set of images with different light intensities or with some noise, the model still achieved satisfactory results. Overall, our approach of training a deep learning model using low-cost imaging devices represents a means to improve broccoli breeding and vegetable trade. [ABSTRACT FROM AUTHOR]

Timely and effective prediction of nitrogen content in summer maize could provide support data for precise fertilization. In this study, the feasibility and expansibility of predicting the nitrogen mechanism model of summer maize leaves through its entire growth period were investigated on the basis of the theory of leaf radiation transmission mechanism. A complete random test of data from two maize varieties and two nitrogen fertilizer applications in 2017 was conducted. Three versions of the leaf optical PROperties SPECTra (PROSPECT) model, namely, PROSPECT-4, PROSPECT-5, and PROSPECT-D were used to link the established leaf nitrogen density (LND) and chlorophyll-a + b (chl-a + b) models, that is, chl-a + b-LND model. A nitrogen response transfer model (N-RTM) was established by linking the optimal PROSPECT and chl-a + b-LND models. Results were as follows. (1) chl-a + b estimation using the PROSPECT-D model yielded the highest accuracy (the coefficient of determination (R2) = 0.774, the normalized root mean squared error (nRMSE) = 13.19%) among the three PROSPECT models, it shows that the model considering more factors can better reflect the internal law of blade, and could be used as the basic model of N-RTM; (2) Established chl-a + b-LND models based on the dataset from each growth stage showed differences using the confidence interval method, and the R2 values of the optimal regression model at V12, VT, and R3 were 0.794, 0.781, and 0.821, respectively. Based on the changes of chl-a + b and LND during the growth period, a piecewise model was constructed; (3) The R2 and nRMSE values between the measured and estimated LNDs were 0.656% and 22.86%, respectively. The validation results are better than the traditional empirical model. The results showed that the segmented model, which considered the interaction of various factors within the leaves and the change of chl-a + b-LND during the growth period, had better performance in nitrogen monitoring. The constructed nitrogen model in this study preliminarily realized the remote sensing prediction of the nitrogen mechanism model and had a certain mechanism. [ABSTRACT FROM AUTHOR]

We carried out a single-step doping reduction, Li4Ti5−xFexO12−y (0 ≤ x ≤ 0.1) with Fe, by a facile solid-phase method with the objective of improving the electrochemical performance of Li4Ti5O12 (LTO). Unlike the conventional method of using an Fe salt as a dopant, elemental Fe is used here as both a reducing agent and a dopant. The Fe first reacts with TiO2 to form Ti3+ and Fe3+ ions; the Fe3+ ions then incorporate into the TiO2 crystal lattice through substitution of Ti by Fe; the amount of Ti3+/Ti4+ is increased as a result of charge compensation, which further improves the conductivity of the LTO, resulting in high electrochemical performance. The investigation of the electrochemical performance of lithium-ion batteries under low-voltage conditions is important for assessing their safety. Because LTO can provide a higher battery voltage and a discharge capacity at a lower voltage, the electrochemical behavior of LTO in the voltage range 0–3 V was also investigated. The modified Li4Ti5−xFexO12−y exhibits a capacity of 228.7 mA h g−1 after 200 cycles, which is substantially higher than that of pure LTO (176.3 mA h g−1). In addition, the band structure and density of states (DOS) of the original and Fe-doped Li4Ti5O12 were calculated by first-principles calculations. The Li4Ti5−xFexO12−y (0 ≤ x ≤ 0.1) can provide a higher voltage, enabling its broad application in lithium-ion batteries because of its large discharge range, good electrochemical performance, and simple synthesis process. [ABSTRACT FROM AUTHOR]

The nitrogen nutrition index (NNI) is a quantitative and reliable indicator of the nitrogen nutrition distribution or status of crops. The timely and accurate estimation of the NNI is crucial in agriculture management. In this study, the quantitative analysis and hyperspectral remote sensing modelling of the NNI were conducted, in which the hyperspectral remote sensing data and NNI data at different growth stages of winter wheat were measured using ground and unmanned aerial vehicle (UAV) carrying high spectrometer equipment. First, the NNIs of the four growth stages of winter wheat were calculated and statistically analyzed. Then, the hyperspectral characteristics at different growth stages and various NNIs were examined. Second, the representation wavebands of the hyperspectral data, which were sensitive to the NNI of winter wheat, were acquired and evaluated. In addition, hyperspectral models were established and comparatively assessed for the NNI estimation. Finally, the hyperspectral characteristics and the remote sensing estimation of the NNIs were determined on the basis of UAV-based hyperspectral data. The results are as follows. (1) As the NNIs of winter wheat changed, the characteristic of the red shift, the variations in the red edge position, and the near-infrared waveband range of the hyperspectral data became apparent. (2) The green band, red edge, and near-infrared were sensitive to the NNIs of winter wheat, and they could be effectively used for estimating the NNI. Moreover, the multiple statistical regression models, which were based on representative wavebands, performed well in estimating the NNI results for the different growth stages of winter wheat. [ABSTRACT FROM AUTHOR]

The application of high-throughput phenotyping (HTP) techniques based on unmanned aerial vehicle (UAV) remote-sensing platforms to study large-scale population breeding opens the way to more efficient acquisition of dynamic phenotypic traits and provides new tools that should help close the gap between genotyping and traditional field-phenotyping methods. Toward this end we used a field UAV-HTP platform to deploy a RGB high-resolution camera to acquire time-series images. By using three-dimensional reconstructed point cloud models, we developed a repeatable processing workflow to extract plant height from time-series images. The plant height determined by the UAV-HTP platform correlated strongly with that measured manually. The plant heights estimated at various growth stages form temporal profiles that give insights into changes and trends in genotyping. Based on fuzzy c-means clustering analysis, we extract the typical dynamic patterns in phenotypic traits (i.e., plant height, average rate of growth of plant height, and rate of contribution of plant height) hidden in the temporal profiles. The fuzzy c-means clustering and set-intersection operation were first applied to analyze the temporal profile to identify how plant-height patterns change and to detect differences in phenotypic variability among the genotypes. The results revealed the capacity of UAV remote sensing to easily evaluate field traits on multiple timescales, for a few breeding plots or for 1000s of breeding plots. [ABSTRACT FROM AUTHOR]

Leaf coverage is an indicator of plant growth rate and predicted yield, and thus it is crucial to plant-breeding research. Robust image segmentation of leaf coverage from remote-sensing images acquired by unmanned aerial vehicles (UAVs) in varying environments can be directly used for large-scale coverage estimation, and is a key component of high-throughput field phenotyping. We thus propose an image-segmentation method based on machine learning to extract relatively accurate coverage information from the orthophoto generated after preprocessing. The image analysis pipeline, including dataset augmenting, removing background, classifier training and noise reduction, generates a set of binary masks to obtain leaf coverage from the image. We compare the proposed method with three conventional methods (Hue-Saturation-Value, edge-detection-based algorithm, random forest) and a frontier deep-learning method called DeepLabv3+. The proposed method improves indicators such as Qseg, Sr, Es and mIOU by 15% to 30%. The experimental results show that this approach is less limited by radiation conditions, and that the protocol can easily be implemented for extensive sampling at low cost. As a result, with the proposed method, we recommend using red-green-blue (RGB)-based technology in addition to conventional equipment for acquiring the leaf coverage of agricultural crops. [ABSTRACT FROM AUTHOR]

Fe2O3, TiO2, TiO2‐Fe2O3 and TiO2‐Fe2O3/Cu nanomaterials were synthesized by hydrothermal methods. The crystal structure and chemical valence of the samples were characterized by X‐ray diffractometry (XRD), transmission electron microscopy (TEM), photoelectron spectroscopy (XPS) and ultraviolet‐visible diffuse reflectance spectroscopy (UV‐vis DRS). The results show that the TiO2‐Fe2O3/Cu nanocomposites have been successfully synthesized and have good UV‐Vis absorption properties. The electrocatalytic properties of the samples were characterized by cyclic voltammetry (CV), electrochemical impedance spectra(EIS) and Tafel polarization curve (Taf). The TiO2‐Fe2O3/Cu nanocomposites have the highest cathode potential and current density, and the charge transfer resistance in EIS is TiO2 > Fe2O3 > TiO2‐Fe2O3 > TiO2‐Fe2O3/Cu. It is indicated that TiO2‐Fe2O3/Cu nanocomposites had good electrocatalytic properties. Finally, the photocatalytic properties of Fe2O3, TiO2, TiO2‐Fe2O3 and TiO2‐Fe2O3/Cu for the degradation of methyl orange (MO) were studied under UV‐vis irradiation. The results show that the reaction rate constants k of TiO2‐Fe2O3/Cu is the relatively highest(12.91×10−3 min−1), indicating the synthetic of TiO2‐Fe2O3/Cu have good photocatalytic properties, which are due to the highest light utilization of TiO2‐Fe2O3/Cu and the best charge transfer performance. The TiO2‐Fe2O3/Cu nanocomposites were successfully synthesized by one‐step hydrothermal method.The presence of Fe element causes a large red shift in the UV‐visible absorption spectrum, Cu element could be benefit the charge separation. It is known from electrochemical and catalytic tests that the TiO2‐Fe2O3/Cu nanocomposites exhibit good photoelectrocatalytic activity. [ABSTRACT FROM AUTHOR]

Wheat is one of the staple crops of the world. With the wide application of remote-sensing methods in agriculture, the use of data from synthetic aperture radar has attracted increasing attention for monitoring wheat growth. Most of previous studies estimated wheat biomass based on a water cloud model (WCM). However, when no data are available on soil moisture content, the applicability of such models is greatly reduced because of insufficient parameters. Thus, this study proposed a new polarized water cloud model (PWCM) called APWCM, which is a physical model and no auxiliary ground data was needed including soil moisture data. APWCM and WCM model was compared to estimate the above ground biomass of wheat in two different study areas. The results revealed that the WCM has a slightly lower root mean squared error (RMSE = 131.63 g m−2, and 645.17 g m−2) in two different study areas. However, the APWCM has lower relative error (RE = 17.91%, and 13.53%) for wheat biomass estimation in areas with higher biomsass. The final result indicates that the APWCM can replace the WCM for estimating wheat biomass based on Gaofen-3 (GF-3) data when soil-moisture data are not available. [ABSTRACT FROM AUTHOR]

Phenotyping under field environmental conditions is often considered as a bottleneck in crop breeding. Unmanned aerial vehicle high throughput phenotypic platform (UAV-HTPP) mounted with multi-sensors offers an efficiency, non-invasive, flexible and low-cost solution in large-scale breeding programs compared to ground investigation, especially where measurements are time-sensitive. This study was conducted at the research station of the Xiao Tangshan National Precision Agriculture Research Center of China. Using the UAV-HTPP, RGB and multispectral images were acquired during four critical growth stages of maize. We present a method of extracting plant height (PH) at the plot scale using UAV-HTPP based on the spatial structure of the maize canopy. The core steps of this method are segmentation and spatial Kriging interpolation based on multiple neighboring maximum pixels from multiple plants in a plot. Then, the relationships between the PH extracted from imagery collected using UAV-HTPP and the ground truth were examined. We developed a semi-automated pipeline for extracting, analyzing and evaluating multiple phenotypic traits: canopy cover (CC), normalized vegetation index (NDVI), PH, average growth rate of plant height (AGRPH), and contribution rate of plant height (CRPH). For these traits, we identify genotypic differences and analyze and evaluate dynamics and development trends during different maize growth stages. Furthermore, we introduce a time series data clustering analysis method into breeding programs as a tool to obtain a novel representative trait: typical curve. We classified and named nine types of typical curves of these traits based on curve morphological features. We found that typical curves can detect differences in the genetic background of traits. For the best results, the recognition rate of an NDVI typical curve is 59%, far less than the 82.3% of the CRPH typical curve. Our study provides evidence that the PH trait is among the most heritable and the NDVI trait is among the most easily affected by the external environment in maize. [ABSTRACT FROM AUTHOR]

Methods to obtain accurate phenotypic data of the seedling stage of maize are receiving ever-increasing research attention because such data are very important for crop growth and for estimating crop yield. To obtain such data, we propose herein an algorithm that uses computer vision to accurately recognize maize seedlings from a digital image. First, the red–green–blue (RGB) images acquired by a manned ground vehicle (MGV) and an unmanned aerial vehicle (UAV) are transformed into grayscale image to clarify the details of the images, and the Otsu threshold-segmentation method, which is based on threshold optimization, is used to separate the maize seedlings from the soil background. Next, the external pressure method is used to segment the results of skeleton extraction. After removing the skeleton burr by using the skeleton-deburring method based on saliency theory, the principal component analysis is used to determine the direction of the principal axis of the maize-seedling skeleton. The principal axis is then introduced as a reference to identify the direction angle of the principle axis in binary images. This process allows us to obtain from RGB images the multiple plant parameters that characterize the maize seedling stage. To verify these statistical computer-generated results, we compare them with field measurements of the maize plots. The result of applying the proposed algorithm to the MGV and UAV data sets correlate strongly ($R = 0.77$ –0.86) with the manually collected data. An average of 0.014 s is required to calculate the number of maize seedlings in a plot from two images showing different vegetation coverage, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on maize seedlings. [ABSTRACT FROM AUTHOR]

MnO 2 /biomass-derived porous carbon (BPC) composites have been prepared by a hydrothermal method, in which the BPC 3D porous carbon structure was based on a banana peel. The banana peel, after freeze drying, can maintain its hierarchical natural porous structure, which provides enough growth space for MnO 2 and reduces the agglomeration of MnO 2 particles. The MnO 2 /BPC composites were characterized by XRD, FT-IR, XPS, TGA, SEM, TEM, BET. The electrochemical performance of the composites was tested in three-electrode supercapacitors using 1 M Na 2 SO 4 aqueous solution as an electrolyte. Due to the large amounts of hierarchical pores and large pore volume, the as-prepared composites exhibited good electrochemical performance. Electrochemical measurements indicated that the MnO 2 /BPC composites applied in supercapacitors had a specific capacitance of 139.6 F g −1 at 300 mA g −1 , and exhibited a good cycling stability with a capacitance retention ratio of 92.3% after 1000 cycles (at 1 A g −1 ). The MnO 2 /BPC composites with 3D porous structure are promising materials in the application of supercapacitors. [ABSTRACT FROM AUTHOR]

Lake Taihu is a large shallow eutrophic lake with frequent recurrence of cyanobacterial bloom which has high variable distribution in space and time. Based on the field observations and remote sensing monitoring of cyanobacterial bloom occurrence, in conjunction with laboratory controlled experiments of mixing effects on large colony formation and colonies upward moving velocity measurements, it is found that the small or moderate wind-induced disturbance would increase the colonies size and enable it more easily to overcome the mixing and float to water surface rapidly during post-disturbance. The proposed mechanism of wind induced mixing on cyanobacterial colony enlargement is associated with the presence of the extracellular polysaccharide (EPS) which increased the size and buoyancy of cyanobacteria colonies and promote the colonies aggregate at the water surface to form bloom. Both the vertical movement and horizontal migration of cyanobacterial colonies were controlled by the wind induced hydrodynamics. Because of the high variation of wind and current coupling with the large cyanobacterial colony formation make the bloom occurrence as highly mutable in space and time. This physical factor determining cyanobacterial bloom formation in the large shallow lake differ from the previously documented light-mediated bloom formation dynamics. [ABSTRACT FROM AUTHOR]

Water productivity (WP) is a key element of agricultural water management in agricultural irrigated regions. The objectives of this study were: (i) to estimate biomass of winter wheat using spectral indices; (ii) integrate the estimation of biomass data with the AquaCrop model using a lookup table for higher accuracy biomass simulation; (iii) show estimation accuracy of the data assimilation method in yield and WP. Spectral variables and concurrent biomass, yield and WP of samples were acquired at the Xiaotangshan experimental site in Beijing, China, during the 2008/2009, 2009/2010, 2010/2011 and 2011/2012 winter wheat growing seasons. The results showed that all spectral indices had a highly significant relationship with biomass, especially normalized difference matter index, with R and RMSE values of 0.84 and 1.43 t/ha, respectively. Simulation of biomass and yield by the AquaCrop model were in good agreement with the measured biomass and yield of winter wheat. The results showed that the data assimilation method (R = 0.79 and RMSE = 0.12 kg/m) could be used to estimate WP. The result indicated that the AquaCrop model could be used to estimate yield and WP with the aid of remote sensing for improving agricultural water resources management. [ABSTRACT FROM AUTHOR]

To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP) is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram-Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT), and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models. [ABSTRACT FROM AUTHOR]

Process-based models can be usefully employed for the assessment of field and regional-scale impact of drought on crop yields. However, in many instances, especially when they are used at the regional scale, it is necessary to identify the parameters and input variables that most influence the outputs and to assess how their influence varies when climatic and environmental conditions change. In this work, two different crop models, able to represent yield response to water, Aquacrop and SAFYE, were compared, with the aim to quantify their complexity and plasticity through Global Sensitivity Analysis (GSA), using Morris and EFAST (Extended Fourier Amplitude Sensitivity Test) techniques, for moderate to strong water limited climate scenarios. Although the rankings of the sensitivity indices was influenced by the scenarios used, the correlation among the rankings, higher for SAFYE than for Aquacrop, assessed by the top-down correlation coefficient (TDCC), revealed clear patterns. Parameters and input variables related to phenology and to water stress physiological processes were found to be the most influential for Aquacrop. For SAFYE, it was found that the water stress could be inferred indirectly from the processes regulating leaf growth, described in the original SAFY model. SAFYE has a lower complexity and plasticity than Aquacrop, making it more suitable to less data demanding regional scale applications, in case the only objective is the assessment of crop yield and no detailed information is sought on the mechanisms of the stress factors affecting its limitations. [ABSTRACT FROM AUTHOR]

Crop evapotranspiration (ET c ) is an important indicator used in managing agriculture water and monitoring crop growth. The objectives of this study were to: (1) analyze the seasonal dynamics of crop coefficients (K c ) and basal crop coefficient (K cb ) derived from vegetation indices (VIs) based on a time series of Huanjing (HJ) satellite images during 2011 and 2013; (2) investigate daily and monthly variations of ET c at key growth stages of winter wheat using lysimeter or eddy covariance systems; (3) compare the performance of two Huanjing-1A/B satellite-based FAO-56 models (the FAO-56 dual-crop coefficient model and the vegetation indices-reference evapotranspiration (VIs-ET o ) method) to the ET c measurements; (4) select the best ET c model for estimating daily ET c (mm/day) at the Xiaotangshan experimental site and its surrounding farmland in conjunction with HJ satellite overpasses from March to May 2011. The VIs and concurrent ET c were acquired at the Xiaotangshan experimental site, Beijing, China, during the 2011 and 2013 winter wheat growing seasons. The results showed that the overall tendencies of crop coefficient patterns (K cb and K c ), ET c and ET o first increased and then decreased at key growth stages of winter wheat. The cumulative ET c of water consumption was highest at the heading-filling stage in May. Similar changes in cumulative ET c were found during April–May 2011 and 2013. The estimation accuracy of ET c was better based on FAO-56 dual-crop coefficient model ( R 2 = 0.88 and RMSE = 1.06 mm/day in 2011 and R 2 = 0.84 and RMSE = 0.55 mm/day in 2013) than the VI-ET o method ( R 2 = 0.77 and RMSE = 1.22 mm/day in 2011 and R 2 = 0.67 and RMSE = 0.81 mm/day in 2013). The results indicated that the FAO-56 dual-crop coefficient model and VI-ET o methods were used to estimate ET c in winter wheat. Two Huanjing-1A/B satellite-based FAO-56 models were used to timely estimate ET c during the winter wheat mid-season, and ET c was used to adjust agricultural water management practices. [ABSTRACT FROM AUTHOR]

Hierarchical carbon foams ( HCFs) with micro-, meso-, and macropores were successfully synthesized via a two-step process: (1) polymerization in oil-in-water (O/W) emulsions without any hard templates and (2) carbonization at 850°C. With the aim of both enhancing the stability of the emulsion and forming a micro- and mesoporous structure during the carbonization process, potassium citrate was introduced in an aqueous solution of resorcinol and formaldehyde. A series of HCFs were fabricated by changing the mass ratio of potassium citrate to total carbon sources from 0.25 to 1.5. The effect of potassium citrate on the porous structure of HCFs was investigated through nitrogen sorption tests. The prepared HCFs exhibited well-developed porous structures of micro-, meso- and macropores and high surface areas. The structural characteristics of the HCFs, including pore size distribution, surface area, and porosity, were significantly dependent on the amount of potassium citrate. It was concluded that potassium citrate greatly contributed to the formation of carbon foams with nano-sized pore structures and high porosity. Interestingly, it was found that when the mass ratio of potassium citrate to total carbon sources was 0.5, the HCFs showed the highest specific surface area (~1360 m2/g). Furthermore, the capacitive performances of the HCFs were evaluated in a 6.0 M KOH aqueous solution using typical electrochemical methods such as cyclic voltammetry and galvanostatic charge/discharge tests. The capacitance of the HCFs tended to increase with the increase in surface area. In particular, the HCFs with the highest surface area also exhibited excellent electrochemical properties (high capacitance of 224 F/g at 1.0 A/g, high rate capability of 191 F/g at 10.0 A/g). These features may be attributed to both the resulting interconnected pore structure that is easily accessible to ions and the high surface area. We believe that this synthesis strategy can be easily extended to the preparation of hierarchical porous carbon materials with a desirable pore structure by tuning the organic monomer. [ABSTRACT FROM AUTHOR]

In the graph of a complex network, the algebraic connectivity is the second smallest eigenvalue of a Laplacian matrix. In this paper, we present a cut algorithm based on edge centrality by minimizing the algebraic connectivity of graph. The edge centrality cut algorithm (ECCA) cuts edges at a time in order to reduce temporal complexity, the algebraic connectivity of which experiences the fastest decline. To prevent nodes from overcutting, each edge sets the weight. We use the advanced ECCA (AECCA) to detect overlapping communities by calculating the correlation coefficients of the nodes. This paper also proposes upper, lower and weaker lower bounds of algebraic connectivity. We demonstrate that our algorithms are effective and accurate at discovering community structure in both artificial and real-world network data and that the algebraic connectivity of the cut algorithm lies between the upper and lower bounds. Our algorithms offer new insights into community detection by calculating the edge centrality. [ABSTRACT FROM AUTHOR]

Vertical heterogeneity of the canopy is being increasingly recognized in remote estimates of vegetative properties. Given the current limited knowledge of this issue, this paper investigated the effects of different vertical distributions of crop structure [e.g., leaf angle (LA) and leaf area index (LAI)] and biochemical parameters [e.g., chlorophyll a and b content (\textChl{a+{b}}) and water content (Wc)] on canopy reflectance and vegetation indices (VIs). A recently developed multiple-layer canopy reflectance model (MRTM) was tested for winter wheat and used to run a simulation analysis of different canopy scenarios. The results showed that the MRTM performed well to model winter wheat canopy reflectance with regard to spikes and vertical distributions of leaf properties. The vertical profiles of LA and LAI influenced canopy reflectance at almost all wavelengths, whereas the vertical profile of \textChl{a+{b}} mainly affected reflectance in the visible region, and that of Wc only affected reflectance in the near-infrared region. Changes in vertical distribution of the LA resulted in clear variations in VIs related to the LA, LAI, and \textChl{a+{b}} estimates. The vertical LAI and \textChl{a+{b}} profiles mainly influenced the VIs related to the LAI and \textChl{a+{b}} estimates. The Wc vertical profile primarily affected the VIs used to estimate crop water properties. The sensitivities of the VIs were mainly associated with the spectral responses and penetration characteristics of the bands they used. These findings suggest that the sensitivity of VIs to the vertical distributions of crop parameters should be considered when establishing models for remote crop monitoring. [ABSTRACT FROM PUBLISHER]

BACKGROUND Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. RESULTS Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. CONCLUSION A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Wind waves are responsible for some of the spatio-temporal gradients observed in the biotic and abiotic variables in large shallow lakes. However, their effects on the phytoplankton community composition are still largely unexplored especially in freshwater systems such as lakes. In this paper, using field observations and mesocosm bioassay experiments, we investigated the impact of turbulence generated by wind waves on the phytoplankton community composition (especially on harmful cyanobacteria) in Lake Taihu, a large, shallow eutrophic lake in China. The composition of the phytoplankton community varied with the intensity of wind waves in the different areas of the lake. During summer, when wind waves were strong in the central lake, diatoms and green algae seemed to dominate while harmful cyanobacteria dominated in the weakly influenced Meiliang Bay. Turbulence bioassays also showed that diatoms and green algae were favoured by turbulent mixing. The critical time for the shift of the phytoplankton community composition was approximately 10 days under turbulent conditions. However, short-term (6 days) turbulence is rather beneficial for the dominance of cyanobacteria. This study suggests that the duration of wind events and their associated hydrodynamics are key factors to understanding the temporal and spatial changes of phytoplankton communities. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) and leaf chlorophyll content (LCC) are major considerations in management decisions, agricultural planning, and policy-making. When a radiative transfer model (RTM) was used to retrieve these biophysical variables from remote-sensing data, the ill-posed problem was unavoidable. In this study, we focused on the use of agronomic prior knowledge (APK), constructing the relationship between LAI and LCC, to restrict and mitigate the ill-posed inversion results. For this purpose, the inversion results obtained using the SAILH+PROSPECT (PROSAIL) canopy reflectance model alone (no agronomic prior knowledge, NAPK) and those linked with APK were compared. The results showed that LAI inversion had high accuracy. The validation results of the root mean square error (RMSE) between measured and estimated LAI were 0.74 and 0.69 for NAPK and APK, respectively. Compared with NAPK, APK improved LCC estimation; the corresponding RMSE values of NAPK and APK were 13.36 µg cm–2and 9.35 µg cm–2, respectively. Our analysis confirms the operational potential of PROSAIL model inversion for the retrieval of biophysical variables by integrating APK. [ABSTRACT FROM PUBLISHER]

The feasibility of monitoring lodging of wheat fields by exploiting fully polarimetric C-band radar images has been investigated in this paper. A set of backscattering intensity features and polarimetric features, derived by target decomposition techniques, was extracted from 5 consecutive Radarsat-2 images. The temporal evolutions of these features of lodging wheat fields were investigated as a function of DAS (day after sowing) during the entire growing season. The temporal behavior was compared between typical lodging fields and normal fields in different growing stages. It was found that polarimetric feature from synthetic aperture radar (SAR) data was very sensitive to wheat lodging. Then a method called polarimetric index, availing the sensitivity of polarimetry to the structure, was put forward to monitor wheat lodging. The method was validated by two sets of in situ data collected in Shangkuli Farmland area, Inner Mongolia, China, at heading and ripe stages of spring wheat. Almost all the lodging fields were successfully distinguished from normal fields. Furthermore, the result revealed that the polarimetric index can reflect the intrinsic feature of lodging wheat with good anti-inference ability such as wheat growth difference. While optical sensors relied on its spectral features to monitor crop lodging, the proposed method based on radar data utilized polarimetric features to monitor crop lodging. [ABSTRACT FROM AUTHOR]

The total leaf chlorophyll content (TLCC) provides valuable information about the physiological status of crops. The objectives of this study were 1) to analyze the leaf area index (LAI) and soil factors that influences the estimation of TLCC using the PROSAIL model, which is a combination of the PROSPECT leaf model and the SAIL canopy model; 2) to propose newly combined spectral indices that reduce the influence of LAI and soil factors in order to improve the TLCC estimation; and 3) to test and validate the relationship between TLCC and the newly combined spectral indices. Ground-based hyperspectral data and concurrent TLCC parameters of samples were acquired at the Shihezi University Experiment Site, Xinjiang Province, China, during the 2009 and 2010 cotton growing seasons. The results showed that the newly combined spectral indices [double-peak canopy nitrogen index I (DCNI I), the ratio of the structure insensitive pigment index to the ratio vegetation index III (SIPI/RVI III), the ratio of the plant pigment ratio to the normalized difference vegetation index (PPR/NDVI), and the modified MERIS terrestrial chlorophyll index (MMTCI)] were more sensitive to chlorophyll and more resistant to LAI than the PPR, SIPI, and MERIS terrestrial chlorophyll index alone. In this study, DCNI I proved to be the best spectral index for estimating chlorophyll content, with determination coefficients (R^2) and root mean square error (RMSE) values of 0.80 and 8.31\;\mu\g\cdot cm^- 2, respectively. PPR/NDVI was also strongly correlated with chlorophyll content, with corresponding R^2 and RMSE values of 0.79 and 9.45\;\mu\g \cdot cm^-2, respectively. This study concluded that DCNI I and PPR/NDVI, in association with indices related to nitrogen, have good potential for assessing nitrogen content. [ABSTRACT FROM PUBLISHER]

The prevalence of powdery mildew (PM) in winter wheat field has a severe impact on crop production. An effective and timely forecast of the disease at a regional scale is necessary to control and prevent it. In this study, both meteorological and remotely sensed observations associated with crop characteristics and habitat traits were integrated for modeling the PM occurrence probability. With an effective feature selection procedure, four meteorological factors, including precipitation, temperature, sun radiation, humidity, and two remotely sensed features including reflectance of red band (R_R) demonstrate that the disease risk maps were able to depict the approximately spatial distribution of PM and its temporal dynamic in the study area. Compared with the model constructed with meteorological data only, the integrated model constructed with both remote sensing and meteorological data has produced a higher accuracy (increasing overall accuracy from 69% to 78%) of forecasting the PM occurrence. This suggests that there would be a great potential for predicting the PM occurrence probability by integrating both meteorological and remote sensing data at a regional scale. In the future, multiple forms of information (e.g., Web sensors networks data) are expected to be incorporated in the disease-forecasting model to further improve its performance for forecasting the disease occurrence (e.g., PM) at a regional scale. [ABSTRACT FROM PUBLISHER]

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation. [ABSTRACT FROM AUTHOR]