Search

The real-time monitoring of wheat phenology variations among different varieties and their adaptive responses to environmental conditions is essential for advancing breeding efforts and improving cultivation management. Many remote sensing efforts have been made to relieve the challenges of key phenophase detection. However, existing solutions are not accurate enough to discriminate adjacent phenophases with subtle organ changes, and they are not real-time, such as the vegetation index curve-based methods relying on entire growth stage data after the experiment was finished. Furthermore, it is key to improving the efficiency, scalability, and availability of phenological studies. This study proposes a two-stage deep learning framework called PhenoNet for the accurate, efficient, and real-time classification of key wheat phenophases. PhenoNet comprises a lightweight encoder module (PhenoViT) and a long short-term memory (LSTM) module. The performance of PhenoNet was assessed using a well-labeled, multi-variety, and large-volume dataset (WheatPheno). The results show that PhenoNet achieved an overall accuracy (OA) of 0.945, kappa coefficients (Kappa) of 0.928, and F1-score (F1) of 0.941. Additionally, the network parameters (Params), number of operations measured by multiply-adds (MAdds), and graphics processing unit memory required for classification (Memory) were 0.889 million (M), 0.093 Giga times (G), and 8.0 Megabytes (MB), respectively. PhenoNet outperformed eleven state-of-the-art deep learning networks, achieving an average improvement of 3.7% in OA, 5.1% in Kappa, and 4.1% in F1, while reducing average Params, MAdds, and Memory by 78.4%, 85.0%, and 75.1%, respectively. The feature visualization and ablation analysis explained that PhenoNet mainly benefited from using time-series information and lightweight modules. Furthermore, PhenoNet can be effectively transferred across years, achieving a high OA of 0.981 using a two-stage transfer learning strategy. Furthermore, an extensible web platform that integrates WheatPheno and PhenoNet and ensures that the work done in this study is accessible, interoperable, and reusable has been developed (https://phenonet.org/). [ABSTRACT FROM AUTHOR]

A miniaturized quad-band bandpass filter (BPF) using compact quad-mode stepped impedance resonator (IQMSIR) is proposed. Based on the symmetric presented by the resonator, four modes are deduced using even-and odd-mode technique. Design formulas are also derived and they are used to guide the filter design. Multiple coupling circuit technique is employed to realize the four passbands. Independent design for four passbands are achieved due to the physical dimensions and additional cross slots. Upper stopband is performed by adding open loop SIR at I/O ports. Transmission zeros among each passbands are generated, resulting in high isolation and selectivity. A quad-band filter is designed, fabricated and measured. The operating central frequencies of the fabricated filter are at 2.54/3.36/5.24/6.6 GHz. The measured 3-dB fractional bandwidths of the four passbands are about 3.9/6.4/4/4.6% and the minimum insertion losses are about 2.3/0.89/3.2/2.3 dB. Measurements correlate well with the simulation results. [ABSTRACT FROM AUTHOR]

A novel dual-band bandpass filter (DB-BPF) based on SIW dual-mode resonant cavity is proposed in this study. This resonant cavity works in the degenerate mode TE102 mode and TE201 mode. The two modes of operation are coupled by opening a window between the resonant cavities, and the passband frequency of the DB-BPF is adjusted by controlling two degenerate modes. This filter works at 9 GHz and 9.5 GHz and has a 3-dB fractional bandwidth of 2.2% and 3.5% GHz, respectively, and has three transmission zeros (TZs) which improve out-of-band rejection and isolation between high-passband and low-passband. The return loss in the high-passband and low-passband is below 13 dB; the insertion loss of the high-passband and low-passband is 2.32 dB and 3.16 dB, respectively. The measured results agree well with the simulation results, which proves the feasibility of the design method. [ABSTRACT FROM AUTHOR]

Canopy height (CH) is an important trait for crop breeding and production. The rapid development of 3D sensing technologies shed new light on high-throughput height measurement. However, a systematic comparison of the accuracy and heritability of different 3D sensing technologies is seriously lacking. Moreover, it is questionable whether the field-measured height is as reliable as believed. This study uncovered these issues by comparing traditional height measurement with four advanced 3D sensing technologies, including terrestrial laser scanning (TLS), backpack laser scanning (BLS), gantry laser scanning (GLS), and digital aerial photogrammetry (DAP). A total of 1920 plots covering 120 varieties were selected for comparison. Cross-comparisons of different data sources were performed to evaluate their performances in CH estimation concerning different CH, leaf area index (LAI), and growth stage (GS) groups. Results showed that 1) All 3D sensing data sources had high correlations with field measurement (r > 0.82), while the correlations between different 3D sensing data sources were even better (r > 0.87). 2) The prediction accuracy between different data sources decreased in subgroups of CH, LAI, and GS. 3) Canopy height showed high heritability from all datasets, and 3D sensing datasets had even higher heritability (H2 = 0.79–0.89) than FM (field measurement) (H2 = 0.77). Finally, outliers of different datasets are analyzed. The results provide novel insights into different methods for canopy height measurement that may ensure the high-quality application of this important trait. Highlights: The effects of canopy height, leaf area index, and growth stage on the accurate monitoring of canopy height with different 3D sensors were systematically evaluated. Field-measured canopy height may not be as accurate as believed, especially in the plots with higher canopy height and at later growth stages. 3D sensing methods achieved higher heritable canopy height estimation than field measurement. [ABSTRACT FROM AUTHOR]

Internal short circuit (ISC) has been identified as a major cause of thermal runaway in lithium-ion (Li-ion) battery systems, making the investigation of ISC fault diagnosis a focal research topic in electric vehicles and battery energy storage systems. Recently, several studies have applied multivariate analysis techniques to analyze abnormal behavior in cell voltage measurements and achieved an accurate diagnosis of ISC faults. However, model parameter variations under different operating conditions and state of charge (SOC), which results in unacceptable false alarm rates, is yet to be fully considered. To effectively address this problem, this study introduces a recursive approach on the basis of principal component analysis (PCA) to update the monitoring model and adapt to parameter changes. Moreover, an adaptive threshold with fusion factors is devised to reduce fault false alarm rates. In particular, this study employs sliding window techniques to amplify weak fault features to tackle the challenge of early-stage detection difficulty for ISC faults and provides an optimal window width determination algorithm based on fault-free data. Experimental results on a real Li-ion battery pack test platform under various operating conditions and SOC levels demonstrate that the proposed method maintains extremely high fault detection rates for ISC faults of different intensities. Furthermore, it is established that the adaptive updating of the model in the absence of faults greatly reduces false alarm rates. [ABSTRACT FROM AUTHOR]

Plant phenomics is a new avenue for linking plant genomics and environmental studies, thereby improving plant breeding and management. Remote sensing techniques have improved high-throughput plant phenotyping. However, the accuracy, efficiency, and applicability of three-dimensional (3D) phenotyping are still challenging, especially in field environments. Light detection and ranging (lidar) provides a powerful new tool for 3D phenotyping with the rapid development of facilities and algorithms. Numerous efforts have been devoted to studying static and dynamic changes of structural and functional phenotypes using lidar in agriculture. These progresses also improve 3D plant modeling across different spatial–temporal scales and disciplines, providing easier and less expensive association with genes and analysis of environmental practices and affords new insights into breeding and management. Beyond agriculture phenotyping, lidar shows great potential in forestry, horticultural, and grass phenotyping. Although lidar has resulted in remarkable improvements in plant phenotyping and modeling, the synthetization of lidar-based phenotyping for breeding and management has not been fully explored. We identify three main challenges in lidar-based phenotyping development: 1) developing low cost, high spatial–temporal, and hyperspectral lidar facilities, 2) moving into multi-dimensional phenotyping with an endeavor to generate new algorithms and models, and 3) embracing open source and big data. [ABSTRACT FROM AUTHOR]

Ecological resources are an important material foundation for the survival, development, and self-realization of human beings. In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society. Advances in observation technology have improved the ability to acquire long-term, cross-scale, massive, heterogeneous, and multi-source data. Ecological resource research is entering a new era driven by big data. Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data. Deep learning is a method for automatically extracting complex high-dimensional nonlinear features, which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data. To promote the application of deep learning in the field of ecological resource research, here, we first introduce the relationship between deep learning theory and research on ecological resources, common tools, and datasets. Second, applications of deep learning in classification and recognition, detection and localization, semantic segmentation, instance segmentation, and graph neural network in typical spatial discrete data are presented through three cases: species classification, crop breeding, and vegetation mapping. Finally, challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning. It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data, improve the universality and interpretability of algorithms, and enrich applications with the development of hardware. [ABSTRACT FROM AUTHOR]

Background: Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms. Results: In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R2 was greater than 0.80), and biomass estimation at leaf group level was the most precise (R2 = 0.97, RMSE = 2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well. Conclusion: We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices. [ABSTRACT FROM AUTHOR]

Separating structural components is important but also challenging for plant phenotyping and precision agriculture. Light detection and ranging (LiDAR) technology can potentially overcome these difficulties by providing high quality data. However, there are difficulties in automatically classifying and segmenting components of interest. Deep learning can extract complex features, but it is mostly used with images. Here, we propose a voxel-based convolutional neural network (VCNN) for maize stem and leaf classification and segmentation. Maize plants at three different growth stages were scanned with a terrestrial LiDAR and the voxelized LiDAR data were used as inputs. A total of 3000 individual plants (22 004 leaves and 3000 stems) were prepared for training through data augmentation, and 103 maize plants were used to evaluate the accuracy of classification and segmentation at both instance and point levels. The VCNN was compared with traditional clustering methods ($K$ -means and density-based spatial clustering of applications with noise), a geometry-based segmentation method, and state-of-the-art deep learning methods (PointNet and PointNet++). The results showed that: 1) at the instance level, the mean accuracy of classification and segmentation (F-score) were 1.00 and 0.96, respectively; 2) at the point level, the mean accuracy of classification and segmentation (F-score) were 0.91 and 0.89, respectively; 3) the VCNN method outperformed traditional clustering methods; and 4) the VCNN was on par with PointNet and PointNet++ in classification, and performed the best in segmentation. The proposed method demonstrated LiDAR’s ability to separate structural components for crop phenotyping using deep learning, which can be useful for other fields. [ABSTRACT FROM AUTHOR]

Battery-powered intelligent transportation systems (BPITS) are growing at an alarming rate, and a problem that cannot be ignored is that the safety of battery packs is currently the weak link in the security of BPITS. However, developing a fast and reliable diagnostic method is not easy due to the large number of battery cells and the difficulty to guarantee consistency among the individual cells. To better resolve this problem, a new fault diagnosis scheme based on voltage correlation coefficient (VCC) and independent component analysis (ICA) model is proposed in this paper. Where VCC is used to extract the fault signature of the battery pack independent of the cell inconsistency; and ICA is used to model the non-Gaussian distributed high-dimensional VCC signals in parallel and derive a statistical-based fault detection index. The kernel density estimation-based algorithm is used to calculate the theoretical threshold and to determine the optimal window width for VCC. In particular, a variable reconstruction-based fault isolation algorithm is proposed for accurate and reliable determination of the location of the faulty cell to prevent the smearing effect. Experimental results conducted on a real battery testbed under different conditions and configurations show that the new method provides significant improvements in both detection latency and positioning reliability in diagnosing internal short-circuit (ISC) faults with different failure grades. • A VCC-ICA-based ISC fault diagnosis method is designed for the battery pack of BPITS. • A KDE-based algorithm is used to set the threshold and window width for VCC. • A VCC reconstruction-based algorithm is applied for fault isolation and location. [ABSTRACT FROM AUTHOR]

Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem–leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem–leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem–leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture. [ABSTRACT FROM AUTHOR]

Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index. Combined with leaf functional traits, some of these StoManager1-measured guard cell and stomatal metrics explained 90% and 82% of tree biomass and intrinsic water use efficiency (iWUE) variances in hardwoods, making them substantial factors in leaf physiology and tree growth. StoManager1 demonstrated exceptional precision and recall (mAP@0.5 over 0.96), effectively capturing diverse stomatal properties across over 100 species. StoManager1 facilitates the automation of measuring leaf stomatal and guard cells, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance predictions of plant growth and resource usage worldwide. Easily accessible open-source code and standalone Windows executable applications are available on a GitHub repository (https://github.com/JiaxinWang123/StoManager1) and Zenodo (https://doi.org/10.5281/zenodo.7686022). StoManager1 uses deep learning and geometrical algorithms for leaf guard cell and stomatal metrics high-throughput measurement to explain plant physiological performance and growth. [ABSTRACT FROM AUTHOR]

Canopy height is an important forest structure parameter for understanding forest ecosystem and improving global carbon stock quantification accuracy. Light detection and ranging (LiDAR) can provide accurate canopy height measurements, but its application at large scales is limited. Using LiDAR-derived canopy height as ground truth to train the Random Forest (RF) algorithm and therefore predict canopy height from other remotely sensed datasets in areas without LiDAR coverage has been one of the most commonly used method in large-scale canopy height mapping. However, how variances in location, vegetation type, and spatial scale of study sites influence the RF modelling results is still a question that needs to be addressed. In this study, we selected 16 study sites (100 km2 each) with full airborne LiDAR coverage across the United States, and used the LiDAR-derived canopy height along with optical imagery, topographic data, and climate surfaces to evaluate the transferability of the RF-based canopy height prediction method. The results show a series of findings from general to complex. The RF model trained at a certain location or vegetation type cannot be transferred to other locations or vegetation types. However, by training the RF algorithm using samples from all sites with various vegetation types, a universal model can be achieved for predicting canopy height at different locations and different vegetation types with self-predicted R2 higher than 0.6 and RMSE lower than 6 m. Moreover, the influence of spatial scales on the RF prediction accuracy is noticeable when spatial extent of the study site is less than 50 km2 or the spatial resolution of the training pixel is finer than 500 m. The canopy height prediction accuracy increases with the spatial extent and the targeted spatial resolution. [ABSTRACT FROM AUTHOR]

Multiple source images acquired from diverse sensors mounted on unmanned aerial vehicles (UAVs) offer valuable complementary information for ground vegetation analysis. However, accurately aligning heterogeneous UAV images poses challenges due to differences in geometry, intensity, and noise resulting from varying imaging principles. This paper presents a two-stage registration method aimed at fusing visible RGB and multispectral images for cotton leaf lesion grading. The coarse alignment stage utilizes Scale Invariant Feature Transform (SIFT), while the refined alignment stage employs a novel correlation coefficient-based template matching. The proposed method first employs the EfficientDet network to detect infected cotton leaves with lesions in RGB images. Subsequently, lesion leaves in multiple spectral imagery (red, green, red edge, and near-infrared bands) are located using the perspective transformation matrix derived from SIFT and the coordinates of lesion leaves in RGB images. Refined registration between RGB and multispectral imagery is achieved through template matching with the new correlation coefficient. The registered reflectance data from the different spectral bands and RGB components are utilized to classify pixels in each infected leaf into lesion, healthy, and soil parts. The lesion grade is determined based on the ratio of lesion pixels to the total corresponding leaf area. Experimental results, compared with manual assessment, demonstrate a lesion leaves detection model with a mAP@0.5 of 91.01% and a leaf lesion grading accuracy of 92.01%. These results validate the suitability of the proposed method for UAV RGB and multispectral image registration, enabling automated cotton leaf lesion grading. [ABSTRACT FROM AUTHOR]

Navigation satellite signals have the advantages of all-day, all-weather, and global coverage, and the use of navigation signals for the detection of moving targets has significant application prospects. However, the GNSS signal is very weak, and the signal power is greatly attenuated after being scattered by the target. In order to detect the echo signal, a long integration time is required. However, the movement of the target will cause the echo signal to produce unpredictable range migration and a Doppler frequency shift, which will weaken the cumulative effect of long-term integration. This paper proposes a weak signal processing method with amplitude and phase self-correction for moving target detection in the GNSS-S radar. First, the phase consistency of the echoes of a single GNSS satellite is realized by the block expansion compression and phase differential correction method to improve the coherent accumulation gain; then, multi-star joint accumulation is carried out after the signal amplitudes of multiple satellites are corrected by the improved keystone method, so as to obtain a stable echo signal track. This method can effectively improve the integral gain of the scattering signal of the moving target and realize target detection. The simulation results and field tests show that this method can effectively improve the SNR of the GNSS-S signal and can realize the detection of small moving targets such as cars with GNSS-S radar. [ABSTRACT FROM AUTHOR]

The local climate zone (LCZ) scheme is of great value for urban heat island (UHI) effect studies by providing a standard classification framework to describe the local physical structure at a global scale. In recent years, with the rapid development of satellite imaging techniques, both multi-spectral (MS) and synthetic aperture radar (SAR) data have been widely used in LCZ classification tasks. However, the fusion of MS and SAR data still faces the challenges of the different imaging mechanisms and the feature heterogeneity. In this study, to fully exploit and utilize the features of SAR and MS data, a data-grouping method was firstly proposed to divide multi-source data into several band groups according to the spectral characteristics of different bands. Then, a novel network architecture, namely Multi-source data Fusion Network for Local Climate Zone (MsF-LCZ-Net), was introduced to achieve high-precision LCZ classification, which contains a multi-branch CNN for multi-modal feature extraction and fusion, followed by a classifier for LCZ prediction. In the proposed multi-branch structure, a split–fusion-aggregate strategy was adopted to capture multi-level information and enhance the feature representation. In addition, a self channel attention (SCA) block was introduced to establish long-range spatial and inter-channel dependencies, which made the network pay more attention to informative features. Experiments were conducted on the So2Sat LCZ42 dataset, and the results show the superiority of our proposed method when compared with state-of-the-art methods. Moreover, the LCZ maps of three main cities in China were generated and analyzed to demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Non-imaging radar can achieve the detection and localization of moving targets, but it faces difficulties in the classification of targets. Aiming for the application of moving target classification by low/medium resolution non-imaging radar, this paper proposes a novel feature extraction method, based on the statistical characteristics of echo amplitude with the L-band fully polarimetric radar. A feature plane, composed of the 3rd-order central moment (skewness) and 4th-order central moment (kurtosis) as the statistical characteristics of the echo envelope, is established. In addition, two types of moving targets, pedestrians and non-motorized vehicles, are able to be effectively distinguished according to whether the echo signals of different polarizations have overlapping areas in the feature plane. The L-band fully polarimetric radar has been developed and field experiments have been conducted. The experimental results verify that the kurtosis and skewness of the fully polarimetric echo amplitude of the pedestrians are very close, and there is an overlapping area in the feature plane, while the statistical characteristics of the fully polarimetric echo signal of non-motor vehicles are different, and there is no overlapping area in the feature plane. This proposed feature extraction method has the advantage of being simple and robust, compared to the traditional imaging approach. [ABSTRACT FROM AUTHOR]

• Proposing a framework for multi-plant phenotype extraction with minimal point clouds. • Non-parametric strategy enhances point cloud spatial representation capability. • Creating a labeled cotton point cloud dataset with multi-temporal and multi-species. Rapid and precise 3D organ segmentation is crucial for the automatic extraction of phenotypic traits, forming a fundamental prerequisite for intelligent plant breeding. The advancement of deep learning technology has replaced labor-intensive manual measurements and traditional computer vision methods, which are sensitive to parameters, in phenotypic trait extraction. However, current larger network structures not only require extensive point cloud data but also consume substantial computational resources, rendering them unsuitable for agricultural tasks with limited plant samples. Therefore, this study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction. The adopted simple-but-effective network architecture and innovative modern operations, including a high-dimensional feature mapping strategy for preprocessing input points, a local feature extraction module based on inverted residual bottleneck block, and a cost-free attention block for spatial feature fusion, effectively implement multi-scale hierarchies and adaptively reduce computational overheads. Experimental results from cotton stem-leaf segmentation demonstrated that PEPNet not only presented approximately 2 × faster inference speed (9.59 ms) and throughput (146.32 plants per second) but also achieved competitive segmentation performance compared to other six state-of-the-art deep learning networks, namely PointNet++, DGCNN, CurveNet, Point Cloud Transformer, PointMLP, and SPoTr, achieving 95.99 %, 94.66 %, 95.32 %, and 91.31 % in Precision, Recall, F1-score, and mIoU, respectively. In transferability experiments with tomato and soybean plants, PEPNet achieved almost all the best metrics and significantly outperformed the second-best model (CurveNet). Furthermore, ablation study verified the optimal trade-off between efficiency and accuracy in this network. Any modifications to the modules could potentially disrupt the optimal trade-off. This work could contribute to reducing computational resources and annotation costs for applying segmentation methods in high-throughput phenotyping tasks. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a compressed model composed of horizontal and vertical compression. [•] We employ OneR as horizontal compression, AP clustering as vertical compression. [•] We implement a Map-Reduce based scalable and parallel framework for compression. [•] We use KNN and SVM to build intrusion detector using the proposed compressed model. [•] Both KDD99 and CDMC2012 are evaluated to show the detection efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Regional landslide identification is important for the risk management of landslide hazards. The traditional methods of regional landslide identification were mainly conducted by a human being. In previous studies, automatic landslide recognition mainly focused on new landslides distinct from the environment induced by rainfall or earthquake, using the image classification method and semantic segmentation method of deep learning. However, there is a lack of research on the automatic recognition of old loess landslides, which are difficult to distinguish from the environment. Therefore, this study uses the object detection method of deep learning to identify old loess landslides with Google Earth images. At first, a database of loess historical landslide samples was established for deep learning based on Google Earth images. A total of 6111 landslides were interpreted in three landslide areas in Gansu Province, China. Second, three object detection algorithms including the one-stage algorithm RetinaNet and YOLO v3 and the two-stage algorithm Mask R-CNN, were chosen for automatic landslide identification. Mask R-CNN achieved the greatest accuracy, with an AP of 18.9% and F1-score of 55.31%. Among the three landslide areas, the order of identification accuracy from high to low was Site 1, Site 2, and Site 3, with the F1-scores of 62.05%, 61.04% and 50.88%, respectively, which were positively related to their recognition difficulty. The research results proved that the object detection method can be employed for the automatic identification of loess landslides based on Google Earth images. [ABSTRACT FROM AUTHOR]

• A well-labeled dataset for stem-leaf semantic and instance segmentation was constructed manually, which contains 5460 point clouds using a self-designed data sampling and augmentation method, i.e., 3D Edge-Preserving Sampling (3DEPS). • A point-based dual-function network for stem-leaf semantic and instance segmentation was proposed, which achieves average Precision of 92.49% in semantic segmentation and average Mean Precision of 83.30% in instance segmentation of three different species of crops. • The PlantNet outperforms state-of-the-art deep learning networks including PointNet, PointNet++, SGPN, and ASIS with an average improvement of 5.56% in Precision on semantic segmentation and an average improvement of 22.18% in Mean Precision on instance segmentation. The accurate plant organ segmentation is crucial and challenging to the quantification of plant architecture and selection of plant ideotype. The popularity of point cloud data and deep learning methods make plant organ segmentation a feasible and cutting-edge research. However, current plant organ segmentation methods are specially designed for only one species or variety, and they rarely perform semantic segmentation (stems and leaves) and instance segmentation (individual leaf) simultaneously. This study innovates a dual-function deep learning neural network (PlantNet) to realize semantic segmentation and instance segmentation of two dicotyledons and one monocotyledon from point clouds. The innovations of the PlantNet include a 3D Edge-Preserving Sampling (3DEPS) strategy for preprocessing input points, a Local Feature Extraction Operation (LFEO) module based on dynamic graph convolutions, and a semantic-instance Feature Fusion Module (FFM). The semantic segmentation results of tobacco, tomato, and sorghum in average Precision , Recall , F1-score , and IoU reached 92.49%, 92.04%, 92.13%, and 85.86%, respectively; and the instance segmentation results in the mean precision (mPrec) , the mean recall (mRec) , the mean coverage (mCov) , and the mean weighted coverage (mWCov) reached 83.30%, 74.08%, 78.62%, and 84.38%, respectively. The PlantNet outperformed state-of-the-art deep learning networks including PointNet, PointNet++, SGPN, and ASIS, which achieved an average improvement of 5.56%, 3.58%, 4.78%, and 6.74% in Precision, Recall, F1-score, IoU on semantic segmentation, and an average improvement of 22.18%, 16.37%, 14.13%, and 13.35% in mPrec , mRec , mCov , and mWCov on instance segmentation. In addition, the effectiveness of 3DEPS, sub-modules, and the new loss function were verified separately by the ablation analysis, in which the removal of any of them can result in a segmentation performance decline of up to 2.0% on average quantitative measures. This study may contribute to the development of plant phenotype extraction, ideotype selection, and intelligent agriculture. [ABSTRACT FROM AUTHOR]

The majority of investigations on mechanisms underlying yield difference between superior and conventional cultivars has focused on the grain-filling stage after anthesis. However, the physiological process before anthesis has been less explored. We aimed to quantify the contribution of canopy photosynthesis at pre-anthesis stage to grain yield formation and clarify the mechanism underlying the yield advantage of the superior cultivars. This study selected representative cultivars of indica and japonica rice, each type having a pair of cultivars with ideal plant architecture (IPA) and conventional architecture. Field experiments over four years (2019–2022) were conducted to compare the relevance of canopy photosynthesis to yield formation between the phases before and after anthesis. The two IPA cultivars, JYZK-6 and ZZY-1, produced higher grain yield than the two conventional cultivars, NJ-5055 and HHZ. JYZK-6 and ZZY-1 also had good distribution of light interception inside the canopy, with the middle and lower part receiving more light than NJ-5055 and HHZ. At leaf level, there was no significant difference in photosynthetic rate between the two types of cultivars. At canopy level, the IPA cultivars had stronger photosynthetic activities before anthesis, outperforming NJ-5055 by 35.56% in averaged canopy photosynthesis, and by 46.65% in averaged daily net photosynthetic accumulation in 2021–2022. By contrast, canopy photosynthesis after anthesis was not significantly different between the two types. The IPA cultivars stored heavier biomass in internodes and sheaths before anthesis and remobilized more reserves to the developing grains, with the remobilization rate being 16.42% (internodes) and 35.34% (sheaths) higher than NJ-5055 and HHZ. The higher canopy productivity before anthesis is one of the main reasons accounting for the superior IPA cultivars, suggesting the necessity of optimizing plant growth and development during the vegetative and reproductive phases. • Canopy photosynthesis was compared between IPA and conventional cultivars. • IPA cultivars had a good distribution of light interception inside the canopy. • IPA cultivars had stronger canopy photosynthetic activities before anthesis. • We suggest the necessity of optimizing canopy structure development before anthesis. [ABSTRACT FROM AUTHOR]

High throughput plant phenomics enables precise quantification of structural information for the complex crop canopy. Leaf to panicle ratio (LPR) in terms of light interception is a physiological trait we formerly developed to clarify the light distribution pattern within the canopy of japonica rice. Here, using the methodology of deep learning neural network (Transformer Feature Pyramid Network), we proposed a general method for LPR calculation for both japonica and indica rice, and tested it in the study on variation of canopy structure across nitrogen (N) fertilization modes. Field experiments over three years (2020–2022) with three nitrogen levels and two basal to topdressing ratios were conducted for two cultivars of each japonica and indica rice. Results showed contrasting dynamic variation of LPR between the two species, ascending for indica rice but descending for japonica rice along with the grain-filling progression. Indica rice had larger temporal variation in LPR than the japonica. N topdressing significantly increased the LPR of indica rice cultivars at same N level, whereas that of japonica was dependent on N level and genotype. Morphological measurement revealed that the differential response of LPR to N was associated with the height difference between the flag leaf and panicle, panicle curvature, leaf area index and panicle area index. Correlation analysis revealed that the relation between LPR and grain yield was significantly positive for indica rice but negative for japonica rice. Our findings suggest that LPR can effectively reflect the characteristics of canopy structure as affected by cultivars and fertilization modes, thus being a valuable physiological indicator for crop science. • LPR (leaf to panicle ratio) was used to compare the canopy structure between japonica and indica rice. • The two subspecies were contrasting in the dynamics of canopy structure. • LPR of indica rice was more sensitive to nitrogen topdressing. • LPR and yield was positively related for indica but negatively for japonica. [ABSTRACT FROM AUTHOR]

Vegetation maps are important sources of information for biodiversity conservation, ecological studies, vegetation management and restoration, and national strategic decision making. The current Vegetation Map of China (1:1000000) was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s. However, the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades, and it urgently needs to be updated to better represent the distribution of current vegetation types. Here, we describe the process of updating the Vegetation Map of China (1:1000000) generated in the 1980s using a "crowdsourcing-change detection-classification-expert knowledge" vegetation mapping strategy. A total of 203,024 field samples were collected, and 50 taxonomists were involved in the updating process. The resulting updated map has 12 vegetation type groups, 55 vegetation types/subtypes, and 866 vegetation formation/sub-formation types. The overall accuracy and kappa coefficient of the updated map are 64.8% and 0.52 at the vegetation type group level, 61% and 0.55 at the vegetation type/subtype level and 40% and 0.38 at the vegetation formation/sub-formation level. When compared to the original map, the updated map showed that 3.3 million km2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change. We expect this updated map to benefit the understanding and management of China's terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics. [ABSTRACT FROM AUTHOR]

The emerging near-surface light detection and ranging (LiDAR) platforms [e.g., terrestrial, backpack, mobile, and unmanned aerial vehicle (UAV)] have shown great potential for forest inventory. However, different LiDAR platforms have limitations either in data coverage or in capturing undercanopy information. The fusion of multiplatform LiDAR data is a potential solution to this problem. Because of the complexity and irregularity of forests and the inaccurate positioning information under forest canopies, current multiplatform data fusion still involves substantial manual efforts. In this article, we proposed an automatic multiplatform LiDAR data registration framework based on the assumption that each forest has a unique tree distribution pattern. Five steps are included in the proposed framework, i.e., individual tree segmentation, triangulated irregular network (TIN) generation, TIN matching, coarse registration, and fine registration. TIN matching, as the essential step to find the corresponding tree pairs from multiplatform LiDAR data, uses a voting strategy based on the similarity of triangles composed of individual tree locations. The proposed framework was validated by fusing backpack and UAV LiDAR data and fusing multiscan terrestrial LiDAR data in coniferous forests. The results showed that both registration experiments could reach a satisfying data registration accuracy (horizontal root-mean-square error (RMSE) < 30 cm and vertical RMSE < 20 cm). Moreover, the proposed framework was insensitive to individual tree segmentation errors, when the individual tree segmentation accuracy was higher than 80%. We believe that the proposed framework has the potential to increase the efficiency of accurately registering multiplatform LiDAR data in forest environments. [ABSTRACT FROM AUTHOR]

• A new deep-learning-based individual stoma tracking pipeline was proposed. • The circadian rhythm of stomata opening was first reported from video data. • Smaller stomata not only respond faster but also had longer closure time at night. Plant stomata are essential channels for gas exchange between plants and the environment. The infrared gas-exchange system has greatly accelerated the studies of stomatal conductance (g s). Nevertheless, due to the lack of in-situ monitoring techniques, the behavior of stomata themselves remains poorly understood, especially in nocturnal environmental conditions. Here, a deep-learning-based stoma tracking pipeline (StomataTracker) was first proposed to continuously monitor stoma traits from unprecedentedly long-term, continuous, and non-destructive video data. Compared to the semi-automatic method (ImageJ), the open-source StomataTracker could greatly improve the extraction efficiency from 207 s to 1.47 s of stomatal traits, including stomatal area, perimeter, length, and width. The R2 adjusted of the four stomatal traits ranged from 0.620 to 0.752. In addition, the rhythm of wheat stomata opening in a completely dark environment was first reported from long-term video data. The closed time of stoma at night was negatively correlated with stomatal traits, and the R ranged from −0.583 to −0.855. The heterogeneity of stomatal behavior also highlighted that smaller stomata have the rhythm pattern of longer closure time at night. Overall, our study provides a novel perspective for stomatal study, and it is conducive to accelerating the application of stomatal circadian rhythm in wheat breeding. [ABSTRACT FROM AUTHOR]

It has been found that the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is systematically higher than the actual land surface in vegetated areas. This study developed a new globally corrected SRTM DEM through reducing its systematic bias in vegetated areas. Over 150, 000 km2airborne light detection and ranging (LiDAR) data along with spaceborne LiDAR, global canopy height data, global canopy cover data and global land cover data were collected to correct the SRTM DEM. A linear regression based method was used to estimate the original SRTM DEM error and therefore correct the SRTM DEM data. The results show that the original SRTM DEM data is around 6 m higher than the actual land surfaces on average across all vegetation types. Our corrected SRTM DEM data can significantly reduce the significant bias to near zero, and can also reduce the root-mean-square error by 1 m. [ABSTRACT FROM PUBLISHER]

This paper presents the field test of our first demonstration cluster with five base stations (BTS) on code division multiple access (CDMA) wireless communication in China, while each BTS was installed with a high temperature superconductor (HTS) receiver. The HTS receivers have the same centre frequency of 830 MHz, a bandwidth of 13 MHz, and a noise figure (NF) of 0.9 dB. After the conventional receivers in the BTSs were replaced by the HTS receivers, a consequent drive test was carried out. A 2.35 dB decrease of the mean mobile transmit power was observed in the demonstration cluster. [ABSTRACT FROM AUTHOR]

This letter presents the design, fabrication, and measurements of a high-temperature superconducting (HTS) filter at 16 GHz with a 100-MHz bandwidth. The main aspects of achieving high performance narrowband filter at such high frequency are analyzed and addressed. Simple rectangular resonators and end-coupled structure are used in the design to achieve high quality factor (Q), weak coupling, and good parasitic response. The measurements of the seven-pole HTS filter, without any tuning, exhibit a high performance. The insertion loss is less than 1.1 dB, equivalent of a resonator Q about 6100. The return loss is better than −15 dB, and the out-of-band rejection is over 70 dB up to 29.3 GHz. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:1514-1516, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26816 [ABSTRACT FROM AUTHOR]

• Evaluated the performance of TLS data on deriving degraded grassland AGB. • The SMR model yields the highest accuracy for predicting AGB (R2 = 0.84). • Canopy cover and minimum height are the two best predictors for estimating AGB. • AGB prediction accuracy increases with the TLS point density. Aboveground biomass (AGB) is an important indicator for grassland ecosystem assessment, management and utilization. Remote sensing technologies have driven the development of grassland AGB estimation from labor-intensive to highly-efficient. However, optical image-based remote sensing methods are fraught with uncertainty issues due to the saturation effects. In this study, we evaluated the capability of the emerging terrestrial laser scanning (TLS) technique in estimating grassland AGB in the northern agro-pastoral ecotone of China. Seven variables (i.e., canopy cover, canopy volume, mean height, maximum height, minimum height, range of height, and standard deviation of height) were extracted from the TLS data of 30 plots across the northern agro-pastoral ecotone of China, and were used to build regression models with field measured AGB using four regression methods, which are simple regression (SR) model, stepwise multiple regression (SMR) model, random forest (RF) model and artificial neural network (ANN) model. The results demonstrate that TLS is a feasible technique for extracting grassland structural parameters. Mean grass height and canopy cover obtained from TLS data have good correspondence with field measurements (R2 > 0.7, p -values < 0.001). Among the four regression models, the SMR model yields the highest prediction accuracy (R2 = 0.84, RMSE = 48.89 g/m2), followed by the RF model (R2 = 0.78, RMSE = 68.72 g/m2), the SR model (R2 = 0.80, RMSE = 86.4 g/m2), and the ANN model (R2 = 0.73, RMSE = 101.40 g/m2). Minimum grass height and canopy coverage are the two most important variables influencing the prediction accuracy of the SMR model, and the prediction accuracy of the SMR model increases with the increase of point density. The results of this study can provide guidance for choosing the optimal model and data collection method for estimating degraded grassland AGB using TLS in agro-pastoral ecotone. [ABSTRACT FROM AUTHOR]

Background: Maize (Zea mays L.) is the third most consumed grain in the world and improving maize yield is of great importance of the world food security, especially under global climate change and more frequent severe droughts. Due to the limitation of phenotyping methods, most current studies only focused on the responses of phenotypes on certain key growth stages. Although light detection and ranging (lidar) technology showed great potential in acquiring three-dimensional (3D) vegetation information, it has been rarely used in monitoring maize phenotype dynamics at an individual plant level. Results: In this study, we used a terrestrial laser scanner to collect lidar data at six growth stages for 20 maize varieties under drought stress. Three drought-related phenotypes, i.e., plant height, plant area index (PAI) and projected leaf area (PLA), were calculated from the lidar point clouds at the individual plant level. The results showed that terrestrial lidar data can be used to estimate plant height, PAI and PLA at an accuracy of 96%, 70% and 92%, respectively. All three phenotypes showed a pattern of first increasing and then decreasing during the growth period. The high drought tolerance group tended to keep lower plant height and PAI without losing PLA during the tasseling stage. Moreover, the high drought tolerance group inclined to have lower plant area density in the upper canopy than the low drought tolerance group. Conclusion: The results demonstrate the feasibility of using terrestrial lidar to monitor 3D maize phenotypes under drought stress in the field and may provide new insights on identifying the key phenotypes and growth stages influenced by drought stress. [ABSTRACT FROM AUTHOR]