On the use of depth camera for 3D phenotyping of entire plants

In this article, we assess the potential of depth imaging systems for 3D measurements in the context of plant phenotyping. We propose an original algorithm to segment depth images of plant from a single top-view. Various applications of biological interest involving for illustration rosebush, yucca and apple tree are then presented to demonstrate the practical interest of such imaging systems. In addition, the depth camera used here is very low cost and low weight. The present results therefore open interesting perspectives in the direction of high-throughput phenotyping in controlled environment or in field conditions.     

Imaging technologies for plant high-throughput phenotyping: a review

Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants. With the rapid development of genomics, many plant phenotyping platforms have been developed to study complex traits related to the growth, yield, and adaptation to biotic or abiotic stress, but the ability to acquire high-throughput phenotypic data has become the bottleneck in the study of plant genomics. In recent years, researchers around the world have conducted extensive experiments and research on high-throughput, image-based phenotyping techniques, including visible light imaging, fluorescence imaging, thermal imaging, spectral imaging, stereo imaging, and tomographic imaging. This paper considers imaging technologies developed in recent years for high-throughput phenotyping, reviews applications of these technologies in detecting and measuring plant morphological, physiological, and pathological traits, and compares their advantages and limitations.     

Advances in crop phenotyping and multi-environment trials

Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques,while plant phenotyping has lagged far behind and it has become the rate-limiting factor in genetics, large-scale breeding and development of new cultivars. In this paper,we consider crop phenotyping technology under three categories. The first is high-throughput phenotyping techniques in controlled environments such as greenhouses or specifically designed platforms. The second is a phenotypic strengthening test in semi-controlled environments, especially for traits that are difficult to be tested in multi-environment trials(MET), such as lodging, drought and disease resistance. The third is MET in uncontrolled environments, in which crop plants are managed according to farmer's cultural practices. Research and application of these phenotyping techniques are reviewed and methods for MET improvement proposed.     

植株自旋转多视角重建技术在小麦植株三维表型获取中的应用评估

基于多视角重建技术的作物三维表型高通量获取系统成本低、获取效率高,引起越来越多的关注。植物自旋转式拍摄平台易于搭建,但植物旋转过程中产生的抖动对点云三维重建和表型解析精度有一定影响。为评估旋转式多视角成像在小麦植株三维表型解析中的适用性,基于植物旋转设计了便携式小麦植株三维表型高通量采集系统,选取穗期不同品种的小麦植株作为实验样本进行点云重建,基于Hausdorff距离评价了重建点云的精度误差;并基于人工测量数据,对所提取的表型指标精度进行评价。结果表明,植物旋转式重建的点云与相机旋转式重建的点云有较高的一致性,点云精度差距基本控制在0.4 cm以下;获取的叶长、叶宽和株高的均根方误差分别为0.79、0.13和0.53 cm,平均绝对百分比误差分别为3.26%、7.63%和0.74%,表明该方式适合穗期的小麦植株表型重建,具有较高的点云重建和表型提取精度,并为小麦植株表型评价提供了一种低成本的解决方案。     

Image-based root phenotyping for field-grown crops: An example under maize/soybean intercropping

Root architecture, which determines the water and nutrient uptake ability of crops, is highly plastic in response to soil environmental changes and different cultivation patterns. Root phenotyping for field-grown crops, especially topological trait extraction, is rarely performed. In this study, an image-based semi-automatic root phenotyping method for field-grown crops was developed. The method consisted of image acquisition, image denoising and segmentation, trait extraction and data analysis....     

基于多视角图像的植物三维重建研究进展

快速重建植株三维结构并以三维可视的方式分析研究农作物的形态结构和生长过程、进行表型测量是数字植物及作物育种研究的热点和难点。概述了植物三维重建的研究现状和主流植物三维重建技术,并对各种技术的方法原理和技术优势进行了分类和对比;重点介绍了基于多视角图像三维重建方法 SFM和MVS的研究进展、技术原理以及相关软硬件系统平台;分析了近几年基于多视角图像三维重建方法在植物三维重建中的应用进展,基于多视角图像三维重建方法具有数据获取成本低、获取点云精度高、植物三维重建质量高等技术优势,同时指出了该技术方法的不足之处和未来技术发展趋势,以期为植物三维重建技术的发展、植物表型组学的研究以及低成本高通量设备的研制提供有益参考。     

激光扫描共聚焦显微镜在植物研究中的应用

荧光成像技术、光学显微镜和计算机成像分析系统的完美结合催生了激光扫描共聚焦显微镜(LSCM)的出现,其优越的结构特点实现了将较厚样品能够逐点逐层扫描成清晰图像,有利于各种荧光信号的采集,直观进行三维重建和样品多重荧光信号采集进行的定量分析。这些优势功能极大地提高了从细胞、分子水平来探求生物时空表达基本功能的能力。本文详细阐述了近年来随着该项技术发展的日益多样化,激光扫描共聚焦显微镜技术在植物组织化学、植物细胞器和细胞骨架、植物发育方面的应用。     

一种低成本的农业现场视频/ 图像快速采集系统马翠

农业现场图像信息采集是农作物长势和病虫害分析的重要手段。结合嵌入式技术与B/S 架构,设计了1 个采用高清晰图像传感器罗技Pro9000摄像头的低成本农业现场视频/图像快速采集系统,并对系统的体系结构、通信子系统、采集、压缩与传输子系统进行了详细设计。系统采用大功率WiFi和快速ARM 平台S3C6410 处理器,基于V4L2 技术采集图像,采用JPEG 图像数据压缩技术能够大大地减少传输数据量,使得视频传输更加流畅。试验结果表明,本系统传输640伊480 大小的图像到远程服务器的图像传输成功率在300 m范围内可达到92%以上;在50 m内可达到视频20 帧/秒以上,在300 m的距离也能够达到6帧/秒以上的视频帧率,可以满足视频监控的需求。由于本方案基于ARM 平台设计,成本低,易于集成应用,非常适于大范围农业上开展应用。     

Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat

The root system architecture(RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource adaptive responses in wheat(Triticum aestivum L.). Here, a high-throughput germination paper-based plant phenotyping system was used to identify seedling traits in a wheat doubled haploid mapping population, Savannah×Rialto. Significant genotypic and nitrate-N treatment variation was found across th...     

表型组学研究进展及其在作物研究中的应用

表型组学是指在基因组水平上系统地研究某一生物或细胞在不同环境条件下所有表型的学科。随着植物表型获取技术和设备的不断完善,以及基因组学、蛋白组学、代谢组学、生物信息学和大数据计算技术的快速发展,高通量表型组学分析在种质资源鉴定、遗传图谱绘制、功能基因挖掘等方面发挥越来越大的作用。高通量表型组学研究正成为突破未来作物学研究和应用的关键领域,为作物遗传育种、栽培管理提供精准、高效的决策支持。因此,充分挖掘多组学信息,加快高通量表型研究对全面了解农作物基因型、表型和环境之间的关系,发现和揭示重要农艺性状调控基因和作为机制,促进作物功能基因组及作物遗传改良研究具有非常重要的意义。概述了表型组学的发展历程,并对当前表型组学主要研究平台和成像系统,及其在作物根系结构、冠层结构、生物和非生物胁迫研究中的应用进行了系统论述,旨在为农作物表型高通量获取和解析提供方法借鉴,加速表型组学研究方法在作物育种中的应用。     

基于三维数字化的玉米株型参数提取方法研究

【目的】玉米株型参数获取是玉米精确化育种和栽培研究的重要环节,研究解决玉米株型参数获取中存在的测量标准不一致、测量精度低、数据难以可视化、算法提取参数精度低等问题具有重要意义。【方法】本文利用三维数字化仪获取玉米植株骨架结构,提出玉米茎、叶、雄穗和雌穗器官三维数字化获取标准规程。通过将植株三维数字化数据旋转至与Z轴正方向平行并平移至坐标系原点进行数据标准化,进一步根据三维数字化数据位置关系,结合各株型参数的定义实现了株高、叶片着生高度、叶片最高点高度、叶长、叶宽、叶展、叶倾角及叶方位角等主要株型参数的提取,同时提出一种新的玉米植株方位平面计算方法,通过构建植株方位平面与各叶方位角角度差绝对值之和作为目标优化函数,进一步对该L1优化问题进行迭代求解得到植株方位平面,当叶数量是偶数时,方法可以给出精确的方位平面区间,在此基础上,引入dev值作为评价植株叶相对植株方位平面偏离度的指标。【结果】利用6个品种吐丝期玉米植株三维数字化数据和人工测量参数数据进行株型参数提取方法验证。结果表明,方法提取的叶长、叶倾角、方位角误差较小,RMSE分别为3.44 cm、3.41°和8.23°,叶长和叶倾角的MAPE分别为4.06%和4.72%,叶宽因叶片在叶脉垂直平面上的曲线形态不一致导致误差相对较大,RMSE和MAPE分别为0.80 cm和7.21%。与传统负方向能量均值法相比,所提出新的玉米植株方位平面计算方法给出了玉米植株方位平面更确切的定量化描述,对于玉米株型的定量评价具有一定价值。【结论】基于三维数字化的玉米株型参数提取方法为玉米株型参数的提取与分析提供了一种精确、便捷、可视的技术手段,对于玉米株型表型组学、玉米功能结构模型及玉米株型优化研究具有重要作用。     

基于最优基小波包的植物病害图像压缩算法

针对现代温室中由于植物病害图像数据量庞大而产生的远程传输负荷过大等问题,分析了植物病害图像存在的数据冗余、视觉冗余等特性,提出了基于最优基小波包的压缩算法。简要介绍了小波包的构造方法及其压缩的基本原理,重点阐述了最优基的构建及病害图像压缩算法实现方法,并与小波变换的压缩方法进行比较。仿真试验表明:提出的算法在保证图像质量的同时能够有效的提高图像压缩性能。     

Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum

Precision Agriculture - This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput...     

基于轮廓投影的盆栽水稻三维重建方法研究

近几年,基于图像的高通量水稻表型研究取得了极大进展,但从三维层面进行研究的工作则相对较少。一般而言,相对于二维图像,从三维模型中能提取更为全面的性状参数。三维模型重建是作物三维表型研究的基础,提出一种适用于盆栽水稻三维点云的重建方法。该方法在相机固定、样本旋转的拍摄模式下获取水稻多视角图像,根据相机标定参数以及水稻轮廓二值图,通过轮廓投影方法重建水稻三维可视外壳点云模型,并通过反投影方法进行点云着色。结果表明,该方法对于不同时期及不同品种的水稻样本均能取得较好重建效果。     

基于轮廓投影的盆栽水稻三维重建方法研究

近几年,基于图像的高通量水稻表型研究取得了极大进展,但从三维层面进行研究的工作则相对较少。一般而言,相对于二维图像,从三维模型中能提取更为全面的性状参数。三维模型重建是作物三维表型研究的基础,提出一种适用于盆栽水稻三维点云的重建方法。该方法在相机固定、样本旋转的拍摄模式下获取水稻多视角图像,根据相机标定参数以及水稻轮廓二值图,通过轮廓投影方法重建水稻三维可视外壳点云模型,并通过反投影方法进行点云着色。结果表明,该方法对于不同时期及不同品种的水稻样本均能取得较好重建效果。     

Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model

Effective and efficient segmentation of vegetation from digital plant images is an actively studied topic in crop phenotyping. Many of the formerly proposed methods showed good performance in the extraction under controlled light conditions but it is still hard to properly extract only vegetation from RGB images taken under natural light condition where the images can contain shadowed and lighted parts with specularly reflected parts of plants. In this paper, we propose a robust method to extract vegetation from the plant images taken under natural light conditions using wheat images. The method is based on a machine learning process, decision tree and image noise reduction filters. We adopted the CART algorithm to create a decision tree in the training process and examined its performance using test images, comparing it with the performances of other methods such as ExG, ExG-ExR and Modified ExG which are widely used recently. The results showed that the accuracy of the vegetation extraction by the proposed method was significantly better than that of the other methods particularly for the images which include strongly shadowed and specularly reflected parts. The proposed method also has an advantage that the same model can be applied to different images without requiring a threshold adjustment for each image.     

Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean

Leaf photosynthesis and rhizobial nitrogen fixation are the two metabolic processes of utmost importance to legume growth and development. As these processes are closely related to each other, measuring of leaf chlorophyll content can provide information on the nodulation and nitrogen fixation status of crop plants. In the present investigation, a number of soybean breeding lines consisting of near-isogenic families which are genetically segregating for the nodulation trait were utilized in field experiments carried out across three growing seasons at Vienna, Austria. For phenotyping leaf chlorophyll content, the Minolta SPAD spectrometer was applied in parallel to a simple leaf digital image analysis procedure based on a commercial digital still camera. The main objectives of the research included the comparison of SPAD metering and image analysis for determination of chlorophyll content, phenotyping of the soybean nodulation vs. non-nodulation characteristic with respect to leaf, agronomic and seed traits, and relating both chlorophyll and image analysis data to seed quality characteristics. Nodulating and non-nodulating soybean lines significantly differed in chlorophyll content from the V5 (five leaves fully developed) soybean developmental stage onwards. Apart from chlorophyll content, leaf size, plant height, number of pods per plant, 1000-seed weight, and seed protein and oil content were also affected by nodulation type. The chlorophyll content of soybean leaves as determined by SPAD metering was significantly correlated (r = −0.937) to the green color value (RGB color model) of leaf image analysis at the R3 (beginning of pod growth) soybean developmental stage. Both chlorophyll content and leaf image analysis parameters were correlated to 1000-seed weight, seed protein and seed oil content. Thus, it appears that these leaf parameters related to photosynthesis and nitrogen fixation could be utilized to determine the nitrogen status of a soybean crop and subsequently in forecasting seed quality parameters of the harvest product.     

田间稻飞虱图像远程实时采集系统的研制

研制了一种田间稻飞虱图像远程实时采集系统。该系统由图像采集装置、自动控制系统和远程传输系统组成。利用稻飞虱的趋光性,将其引诱至图像采集装置的白色幕布上,可编程逻辑控制器(PLC)为自动控制系统的核心,发出脉冲控制相机拍摄,同时控制交流伺服电机运行,驱动拍摄装置,实现对幕布的自动扫描拍摄稻飞虱图像。远程传输系统选用4G技术和基于TCP协议的客户端/服务器模式传输方法,将所拍摄昆虫图像自动传输至监测中心处。验证试验结果表明,该系统可直接获得768×576像素的田间稻飞虱图像。通过二维离散傅里叶变换验证,采集系统所采集到的稻飞虱图像与其他昆虫图像存在明显特征差异,说明该系统可满足稻飞虱图像采集要求。     

Sweet sorghum and Miscanthus:Two potential dedicated bioenergy crops in China

Among the potential non-food energy crops,the sugar-rich C_4 grass sweet sorghum and the biomass-rich Miscanthus are increasingly considered as two leading candidates.Here,we outline the biological traits of these energy crops for largescale production in China.We also review recent progress on understanding of plant cell wall composition and wall polymer features of both plant species from large populations that affect both biomass enzymatic digestibility and ethanol conversion rates under various pretreatment conditions.We finally propose genetic approaches to enhance biomass production,enzymatic digestibility and sugar-ethanol conversion efficiency of the energy crops.     

Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images

This study aimed to assess the spectral information potential of images captured with an unmanned aerial vehicle, in the context of crop–weed discrimination. A model is proposed in which the entire image acquisition chain is simulated in order to compute the digital values of image pixels according to several parameters (light, plant characteristics, optical filters, sensors…) to reproduce in-field acquisition conditions. The spectral mixings in the pixels are modeled, based on an image with a 60 mm spatial resolution, to estimate the impact of the resolution on the ability to discriminate small plants. The classification potential (i.e. the ability to separate two classes) in soil and vegetation and in monocotyledon and dicotyledon classes is studied using simulations for different vegetation rates (defined as the proportion of vegetation covering the surface projected in the considered pixel). The classification is unsupervised and based on the Mahalanobis distance computation. The results of soil-vegetation discrimination show that pixels with low vegetation rates can be classified as vegetation: pixels with vegetation rate greater than 0.5 had a probability to be correctly classified between 80 and 100%. Classification between monocotyledonous and dicotyledonous plants requires pixels with a high vegetation rate: to obtain a probability to be correctly classified better than 80%, vegetation rates in the pixels have to be over 0.9. To compare the results with data from real images, the same classification was tested on multispectral images of a weed infested field. The comparison confirmed the ability of the model to assess vegetation–soil and crop–weed discrimination potential for specific sensors (such as the multiSPEC 4C sensor, AIRINOV, Paris, France), where the acquisition chain parameters can be tested.