Fast Evaluation of Unhealthy and Healthy Neonates Using Hyperspectral Features on 700-850 Nm Wavelengths,ROI Extraction,and 3D-CNN

ObjectivesHyperspectral imaging (HSI) has great potential in detecting the health conditions of neonates as it provides diagnostic information about the tissue by avoiding tissue biopsy. HSI gives more features than thermal imaging, which can obtain images in a single wavelength, as it can obtain images in a large number of wavelengths. The data obtained with hyperspectral sensors are 3-dimensional data called hypercube including first two-dimensional spatial information and third-dimensional spectral information.Material and methodsIn this study, hyperspectral data were obtained from 19 different neonates in the Neonatal Intensive Care Unit (NICU) of Selcuk University, Medical Faculty. There are 16 hypercubes from 16 unhealthy neonates, 16 hypercubes from 3 healthy neonates in a period of three months, and 32 hypercubes in total are available. For the training of 3D-CNN model, data augmentation methods, such as rotation, height shifting, width shifting, and shearing were applied to hyperspectral data. A number of 32 hypercubes taken from neonates in NICU were augmented to 160 hypercubes. Spectral signatures were examined and 51 bands in the range of 700-850 nm with distinctive features were used for the classification. The spectral dimension was reduced by applying Principal Component Analysis (PCA) to all hypercubes. In addition, it is aimed to obtain both spectral and spatial features with the 3D-CNN. For increasing the classification efficiency, ROI extraction was made and four datasets were created in different spatial dimensions. These datasets contain 160, 640, 1440, and 5760 hypercubes, respectively.ResultsThe best result was achieved by using 5760 hypercubes of 25x25x51. As a result of the classification of the hypercubes, accuracy 98.00%, sensitivity 97.22%, and specificity 98.78% were obtained. It was determined how many PCs used to achieve the best result. Further, the proposed 3D-CNN model is compared to 2D-CNN model to evaluate the performance of the study.ConclusionIt was aimed to evaluate the health status of neonates fastly by using HSI and 3D-CNN for the first time. The obtained results are an indication that HSI and 3D-CNN are very effective for the evaluation of unhealthy and healthy neonates.     

无人机高光谱影像与冠层树种多样性监测

冠层树种多样性是自然森林生态系统功能和服务的重要基础。及时掌握冠层多样性的现状及变化趋势, 是探讨诸多重要生态学问题的前提, 更是制定合理生物多样性保护策略的基础。但受制于传统的多样性信息采集方法, 区域尺度的高精度冠层多样性监测发展较为缓慢; 许多在气候变化和人类干扰下的生物多样性分布信息得不到及时更新。近年来基于无人机的冠层高光谱影像收集与分析技术的发展, 使得冠层多样性监测迎来了新的发展契机。本文从森林冠层高光谱影像出发, 介绍了与多样性监测相关的无人机航拍和基于深度学习的图像处理技术, 并结合已有文献, 探讨了无人机高光谱应用于森林冠层树种多样性监测的研究现状、可行性、优势及缺陷等。我们认为冠层高光谱影像为多样性监测提供了不可或缺且丰富的原始信息; 而无人机与高光谱相机的结合, 使得区域化高频率(如每周)、高精度(如分米乃至厘米级)的冠层多样性信息自动化收集成为可能。然而高光谱影像数据量大、数据维度高与数据结构非线性的特点为影像处理带来了挑战, 而深度学习技术的飞跃, 使得从冠层高光谱影像中提取个体及物种信息达到了极高精度。恰当地使用这些技术将大大提升冠层树种多样性的自动化监测水平, 由此也将帮助我们在当前剧变环境下及时掌握森林冠层多样性的现状与变化, 为生物多样性研究与保护提供可靠的数据支撑。     

Optimized 1D-CNN model for medicinal Psyllium Husk crop mapping with temporal optical satellite data

Deep Learning models are preferred for complex image analysis-based solutions to application-oriented problems. However, the architecture of such models largely influences the results which includes several hyperparameters that need to be tuned. This study aims at developing an optimized 1D-CNN model for medicinal Psyllium Husk crop mapping using open source temporal optical Sentinel-2A/2B satellite data. In this study, a sequential 1D-CNN model architecture was developed by optimizing hyperparameters which includes convolution layers, number of neurons, activation function, and batch size. Psyllium Husk crop fields were mapped in the Jalore district of Rajasthan using Sentinel 2A/ 2B (10 m) optical data. For spectral dimensionality reduction of the data, Modified Soil Adjusted Vegetation Index (MSAVI2) was used to maintain the data dimensionality since temporal data was utilized. The dataset was subsequently refined to include the target crop's specific phenological stages that distinguish it from other closely resembling species. The information corresponding to these specific crop stages was fed to the 1D-CNN model to carry out the classification. A range of training sample sizes were explored to determine the optimal number of training data points. As the output from the model, fractional images are obtained consisting of values proportional to the probability of a pixel lying in the target class. Accuracy assessment was carried out using fuzzy error matrix (FERM) by generating fractional output images from temporal optical PlanetScope data (3m) which was used as a reference. The best overall accuracy among the test cases came out to be 89.85% using conventional MSAVI2 with 1000 training samples.     

Assessing the application of a geographic presence-only model for land suitability mapping

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the MaxEnt model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results.     

基于无人机多源遥感数据的亚热带森林树种分类

树种多样性是生态学研究的重要内容,树木的种类和空间分布信息可有效服务于可持续森林管理。但在复杂林分条件下,获取高精度分类结果的难度大。而无人机遥感可获取局域超精细数据,为树种分类精度的提高提供了可能。基于可见光、高光谱、激光雷达等多源无人机遥感数据,探究其在亚热带林分条件下的树种分类潜力。研究发现:(1)随机森林分类器总体精度和各树种的F1分数最高,适合亚热带多树种的分类制图,其区分13种类别(8乔木,4草本)的总体精度为95.63%,Kappa系数为0.948;(2)多源数据的使用可以显著提高分类精度,全特征模型精度最高,且高光谱和激光雷达数据显著影响全特征模型分类精度,可见光纹理数据作用较小;(3)分类特征重要性从大到小排序为结构信息,植被指数,纹理信息,最小噪声变换分量。     

基于地面观测光谱数据的冬小麦冠层叶片氮含量反演模型

冬小麦冠层叶片氮含量是反映其产量与品质的重要指标,构建高普适性、高精准性冬小麦冠层叶片氮含量高光谱反演模型对提高其监测效率具有重要意义。以不同地点、品种、年份、施氮水平、生育期的大田试验数据为基础,基于两波段光谱植被指数NDRE和550 nm光谱反射率组合构建一个三波段植被指数NEW-NDRE,并与11个传统冬小麦冠层叶片氮素光谱指数进行比较。结果表明: NEW-NDRE及传统植被指数中NDRE、NDDA、RI-1dB与冬小麦冠层叶片氮含量的相关性较好;其中,灌浆初期NEW-NDRE与冬小麦冠层叶片氮含量相关性最好,决定系数R²为0.9,均方根误差(RMSE)为0.4;经独立数据检验,以NEW-NDRE为变量建立的冬小麦冠层叶片氮含量反演模型的平均相对误差(RE)为9.3%,明显低于以NDRE、NDDA、RI-1dB为变量的模型RE。总体上,新构建的NEW-NDRE对冬小麦冠层叶片氮含量的模拟能力显著优于传统指数,减弱了试验条件的限制性,可为精准施肥提供新的技术支撑。     

Detection and Discrimination of Tea Plant Stresses Based on Hyperspectral Imaging Technique at a Canopy Level

Tea plant stresses threaten the quality of tea seriously. The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation. In recent years, hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases, pests and some other stresses at the leaf level. However, the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale. In this study, based on the canopy-level hyperspectral imaging data, the methods for identifying and differentiating the three commonly occurred tea stresses (i.e., the tea leafhopper, anthrax and sun burn) were studied. To account for the complexity of the canopy scenario, a stepwise detecting strategy was proposed that includes the process of background removal, identification of damaged areas and discrimination of stresses. Firstly, combining the successive projection algorithm (SPA) spectral analysis and K-means cluster analysis, the background and overexposed non-plant regions were removed from the image. Then, a rigorous sensitivity analysis and optimization were performed on various forms of spectral features, which yielded optimal features for detecting damaged areas (i.e., YSV, Area, GI, CARI and NBNDVI) and optimal features for stresses discrimination (i.e., MCARI, CI, LCI, RARS, TCI and VOG). Based on this information, the models for identifying damaged areas and those models for discriminating different stresses were established using K-nearest neighbor (KNN), Random Forest (RF) and Fisher discriminant analysis. The identification model achieved an accuracy over 95%, and the discrimination model achieved an accuracy over 93% for all stresses. The results suggested the feasibility of stress detection and differentiation using canopy-level hyperspectral imaging techniques, and indicated the potential for its extension over large areas.     

A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications

The globe's population is increasing day by day, which causes the severe problem of organic food for everyone. Farmers are becoming progressively conscious of the need to control numerous essential factors such as crop health, water or fertilizer use, and harmful diseases in the field. However, it is challenging to monitor agricultural activities. Therefore, precision agriculture is an important decision support system for food production and decision-making. Several methods and approaches have been used to support precision agricultural practices. The present study performs a systematic literature review on hyperspectral imaging technology and the most advanced deep learning and machine learning algorithm used in agriculture applications to extract and synthesize the significant datasets and algorithms. We reviewed legal studies carefully, highlighted hyperspectral datasets, focused on the most methods used for hyperspectral applications in agricultural sectors, and gained insight into the critical problems and challenges in the hyperspectral data processing. According to our study, it has been found that the Hyperion hyperspectral, Landsat-8, and Sentinel 2 multispectral datasets were mainly used for agricultural applications. The most applied machine learning method was support vector machine and random forest. In addition, the deep learning-based Convolutional Neural Networks (CNN) model is mainly used for crop classification due to its high performance with hyperspectral datasets. The present review will be helpful to the new researchers working in the field of hyperspectral remote sensing for agricultural applications with a machine and deep learning methods.     

Support vector machine-based open crop model (SBOCM): Case of rice production in China

Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.     

基于多源遥感数据的大豆叶面积指数估测精度对比

近年来遥感技术的革新促使遥感源越来越丰富.为分析多源遥感数据的叶面积指数(LAI)估测精度,本文以大豆为研究对象,利用比值植被指数(RVI)、归一化植被指数(NDVI)、土壤调整植被指数(SAVI)、差值植被指数(DVI)、三角植被指数(TVI)5种植被指数,结合地面实测LAI构建经验回归模型,比较3类遥感数据(地面高光谱数据、无人机多光谱影像以及高分一号WFV影像)对大豆LAI的估测能力,并从传感器几何位置和光谱响应特性以及像元空间分辨率三方面分析讨论了3类遥感数据的LAI反演差异.结果表明： 地面高光谱数据模型和无人机多光谱数据模型都可以准确预测大豆LAI(在α=0.01显著水平下,R²均>0.69,RMSE均<0.40);地面高光谱RVI对数模型的LAI预测能力优于无人机多光谱NDVI线性模型,但两者差异不大(E_A相差0.3%,R²相差0.04,RMSE相差0.006);高分一号WFV数据模型对研究区内大豆LAI的预测效果不理想(R²<0.30,RMSE>0.70).针对星、机、地三类遥感信息源,地面高光谱数据在反演LAI方面较传统多光谱数据有优势但不突出;16 m空间分辨率的高分一号WFV影像无法满足田块尺度作物长势监测的需求;在保证获得高精度大豆LAI预测值和高工作效率的前提条件下,基于无人机遥感的农情信息获取技术不失为一种最佳试验方案.在当今可用遥感信息源越来越多的情况下,农业无人机遥感信息可成为指导田块精细尺度作物管理的重要依据,为精准农业研究提供更科学准确的信息.     

Rapid Identification of Rice Samples Using an Electronic Nose

Four rice samples of long grain type were tested using an electronic nose (Cyranose-320).Samples of 5 g of each variety ofrice were placed individually in vials and were analyzed with the electronic nose unit consisting of 32 polymer sensors.TheCyranose-320 was able to differentiate between varieties of rice.The chemical composition of the rice odors for differentiatingrice samples needs to be investigated.The optimum parameter settings should be considered during the Cyranose-320 trainingprocess especially for multiple samples,which are helpful for obtaining an accurate training model to improve identificationcapability.Further,it is necessary to investigate the E-nose sensor selection for obtaining better classification accuracy.A re-duced number of sensors could potentially shorten the data processing time,and could be used to establish an application pro-cedure and reduce the cost for a specific electronic nose.Further research is needed for developing analytical procedures thatadapt the Cyranose-320 as a tool for testing rice quality.     

Hyperspectral remote sensing of plant pigments

The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.     

一种估测小麦冠层氮含量的新高光谱指数

提出了一种估测小麦冠层氮含量的新高光谱指数--微分归一化氮指数(FD-NDNI)。以FieldSpec Pro FR地物光谱仪采集拔节后至孕穗前小麦的冠层光谱190份,随机抽取142份作为训练集,其余48份作为预测集。将光谱以小波阈值去噪法去噪后,利用其525、570 与730 nm处的一阶导数值,采用差值、比值以及归一化的方法构建了12种光谱指数以实现小麦冠层氮含量的估测,并与mNDVI705、mSR以及NDVI705等22种常用指数进行了比较分析。发现指数FD-NDNI对小麦冠层氮含量的估测结果最佳,其估测模型(指数形式)校正集决定系数(C-R²)与预测集决定系数(P-R²)分别达0.818与0.811,优于mNDVI705等常用指数。进一步分析表明,在各指数中,FD-NDNI对叶面积系数最不敏感,可最有效地避免冠层郁闭度等因素对氮含量估测的影响。为优化结果,采用最小二乘支持向量回归算法(LS-SVR)对模型进行了改进,当模型惩罚系数C与RBF核函数参数g取得最优解6.4与1.6时,其C-R²与P-R²分别提高至0.846与0.838,具有比指数模型更高的精度。结果表明:FD-NDNI是小麦冠层氮含量估测的优选指数,LS-SVR为建模的优选算法。     

灌溉水稻生长发育和潜力产量的模拟模型

本文提出的HDRICE模型是灌溉水稻生长的生理生态模型,它由相互衍接的水稻形态发育、干物质积累和叶面积发育三模块组成。形态发育模块用以模拟逐日温度和日长对水稻发育的影响,其参数可反映水稻品种的基本营养性、感温性和感光性;干物质积累模块用以模拟冠层CO_2同化、作物的维持呼吸和生长呼吸及干物质分配等过程;叶面积发育模块用以模拟叶面积指数的动态。本文还讨论了模型的输入参数和模型检验。模型可应用于模拟水稻的生长发育,预测水稻品种潜在产量及为取得潜在产量所必需的群体数量指标。     

估测水稻叶层氮浓度的新型蓝光氮指数

基于不同氮素水平与品种类型的多个田间试验,综合分析了水稻冠层高光谱植被指数与叶层氮浓度的定量关系.结果表明：对氮反应最敏感的波段为红光665～675 nm、蓝光490～500 nm和红边区域波段680～760 nm.400～2500 nm波段范围内两波段植被指数与水稻叶层氮浓度相关性最好的是550～600 nm与500～550 nm,属绿光波段组合,决定系数（R²）最高的是比值指数SR(533,565).以3个蓝光波段构建的光谱参数R₄₃₄/(R₄₉₆+R₄₀₁)（蓝光氮指数）与水稻叶层氮浓度呈极显著的直线相关关系,与SR(533,565)相比,该参数显著提高了对叶层氮浓度的预测性.独立资料检验结果显示,R₄₃₄/(R₄₉₆+R₄₀₁)对水稻叶层氮浓度具有较好的预测性,检验根均方差（RMSE）和相对误差（RE）值分别为9.67%和8%,是一种适合于水稻叶层氮浓度估测的良好高光谱植被指数.     

Dynamic simulation of leaf area index for the soybean canopy based on 3D reconstruction

Leaf area index (LAI) of the soybean canopy was an important indicator to reflect the growth and development of the soybean plant. However, the traditional LAI measurement in the field was expensive, time-consuming, and challenging to achieve high accuracy. Thus, in this study, a calculation method of LAI for the soybean canopy based on 3D reconstruction was proposed, and the dynamic simulation model of canopy LAI was established. First, northeast soybean varieties of Kangxianchong8 and Dongnong252 were taken as the research objects, and a multi-source image synchronous acquisition platform for soybean canopy based on Kinect 2.0 was constructed to obtain the canopy image data from the V3 to R7 growth periods. Second, the 3D structure of the soybean canopy was reconstructed by conditional filtering and statistical filtering. Third, a soybean LAI estimation method was established by canopy analysis. The determination coefficient R² between the calculated value and the standard value of LAI was greater than 0.99. Finally, a dynamic simulation model of soybean LAI was established based on the Richards model and the genetic parameters of varieties. The results showed that the accuracy of the dynamic simulation model reached above 0.99, which realized the critical technology of rapid detection and dynamic simulation of LAI for the soybean canopy, and provided quantitative dynamic prediction and technical support for scientific regulation of ecology and morphology for the soybean canopy.     

农作物遗传多样性农家保护的现状及前景

农作物地方品种的有效保护是农业生物多样性的可持续利用的基础。由于现代农业的集约化生产方式使大量农作物地方品种被少数高产改良品种所取代,造成农作物基因库的严重“基因流失”（genetic erosion)。农家保护是在农业生态系统中进行的动态保护,被保护的生物多亲性可在其生境中继续进化而产生新的遗传变异,在而是农业生物多样性就地保护的重要途径。然而,尽管人们对作物品种资源农家保护的兴趣不断增长,也有大量有关农家的保护的研究和案例分析,但目前为止还没有比较成功的农家保护实例报道。因此,对农家保护的机制及科学问题进行深入的研究,并寻求一条新的途径来充分发挥农家保护应有的作用,显得格外重要。利用生物多样性布局的水稻混合间栽的生产模式,不仅解决了病害控制的问题,而且也保护了水稻地方品种的多样性。这种混合间栽的生物多样性布局和生产方式可能成为农保护的一条新途径。     

Benefits of hyperspectral remote sensing for tracking plant invasions

Aim We aim to report what hyperspectral remote sensing can offer for invasion ecologists and review recent progress made in plant invasion research using hyperspectral remote sensing. Location United States. Methods We review the utility of hyperspectral remote sensing for detecting, mapping and predicting the spatial spread of invasive species. We cover a range of topics including the trade‐off between spatial and spectral resolutions and classification accuracy, the benefits of using time series to incorporate phenology in mapping species distribution, the potential of biochemical and physiological properties in hyperspectral spectral reflectance for tracking ecosystem changes caused by invasions, and the capacity of hyperspectral data as a valuable input for quantitative models developed for assessing the future spread of invasive species. Results Hyperspectral remote sensing holds great promise for invasion research. Spectral information provided by hyperspectral sensors can detect invaders at the species level across a range of community and ecosystem types. Furthermore, hyperspectral data can be used to assess habitat suitability and model the future spread of invasive species, thus providing timely information for invasion risk analysis. Main conclusions Our review suggests that hyperspectral remote sensing can effectively provide a baseline of invasive species distributions for future monitoring and control efforts. Furthermore, information on the spatial distribution of invasive species can help land managers to make long‐term constructive conservation plans for protecting and maintaining natural ecosystems.     

利用红边面积形状参数估测水稻叶层氮浓度

研究红边面积参数与叶层氮素状况的定量关系, 有助于水稻(Oryza sativa)生长信息的实时无损获取及精确追氮管理。该研究基于多年不同施氮水平和不同水稻品种的冠层高光谱数据, 系统分析了水稻的红边区域光谱、面积形状特征及其与叶层氮浓度的定量关系。结果表明, 水稻冠层红边区域微分光谱随不同氮素水平变化出现“三峰”现象, 峰值分别出现在700、720和730 nm附近, 且3个波段的峰值高低发生交替变化; 同时, 以3个峰值波段为中心与x坐标轴组成的微分光谱面积和形状相应发生变化。发现基于两两峰值波段划分所得红边子面积所构成的比值(双峰对称度)、归一化差值(归一化对称度)参数与叶层氮浓度具有密切的定量关系, 可作为估测水稻叶层氮浓度的红边面积形状参数。经曲线拟合和模型检验的结果显示, 双峰对称度DPS (A_675-700, A_675-755), 即由675~700 nm区域面积与675~755 nm区域面积的比值, 和DPS (A_730-755,A_675-700) (由730~755 nm区域面积和675~700 nm区域面积的比值)对水稻叶层氮浓度的估测效果最好, 可用于不同水稻品种和生长条件下的叶层氮浓度估测。     

Classification of Rice Seed Storage Proteins using Neural Networks

Bioinformatics techniques are increasingly employed in annotation of information from sequencing of model organisms. Development of rice varieties with improved protein quality requires detailed annotation and classification of seed storage proteins in rice germplasm. Two neural network tools were employed for classification of seed storage proteins into four classes- Albumins, Globulins, Gluteins and Prolamins using biochemical, structural properties and conserved sequence patterns. Protein classification results obtained from two software tools were cross compared and superior protein family classification accuracy of 95.3 percent is achieved with Alyuda Neurointelligence computational tool. This is the first report on classification of rice seed proteins using sequenced rice genome information.