Rate-distortion tradeoff to optimize high-throughput phenotyping systems. Application to X-ray images of seeds

In the context of high-throughput plant phenotyping, measurements are carried out on large populations of plants and produce large amounts of data to be analyzed and stored. The need for automated phenotyping in plant biology opens new fields of application for image acquisition and compression algorithms. In this report, we focus on X-ray imaging for high-throughput analysis of seeds. A practical tradeoff between the tolerated distortion on images and image acquisition rates is demonstrated for measurement, visual inspection or pattern recognition. In these contexts, using the same methodology, we quantify the highest acquisition and compression rates achievable while preserving all the useful biological information with standard lossy compression formats. Using a study case, we quantitatively demonstrate the interest of considering the final biological task as a priori knowledge to optimize the design of phenotyping systems.     

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.     

作物根系表型鉴定评价方法的现状与展望

根系是作物固定植株并吸收土壤水分和养分的主要器官,其表型特征直接影响作物的生产力和适应性。优化根系表型被认为是实现第二次“绿色革命”的重要途径之一。然而,根系的隐匿性、复杂性和可塑性极大地制约着根系表型鉴定效率,导致根系优化进程远远滞后于地上部器官。随着光谱成像、机器学习和三维重建等新技术的快速发展,根系表型鉴定方法逐渐由传统取样观测向原位、无损、自动化检测转变,评价依据由二维形态指标向立体构型参数拓展,促进了根系表型鉴定效率大幅提升,根系表型数据快速增长。与此同时,海量数据也带来了信息冗余及利用率低等问题,对根系表型研究提出了规范化和共享化的时代新要求。本文概述了现行主要根系表型鉴定方法的原理和技术要点,从精准度、通量和成本等方面对不同方法进行系统比较,并从使用许可、运行平台和分析方式等方面对常用根系表型量化软件进行归纳总结;进一步提出今后重点研究方向,即开发高效的田间根系表型鉴定方法,建立根系可塑性鉴定评价技术体系,加强根系解剖结构的鉴定和利用,强化分子检测技术在根系表型鉴定中的应用,推进根系表型鉴定技术规范化和数据信息共享化,以期为合理选用和改进作物根系表型鉴定评价方法提供参考,促进作物根系改良。     

High-throughput phenotyping identifies plant growth differences under well-watered and drought treatments

The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of highthroughput phenotyping(HTP) over traditional phenotyping techniques. In this study, two wheat accessions were grown in a controlled-environment with a moderate drought imposed from stem elongation to post-anthesis. Red-green-blue(RGB) imaging was performed on 17 of the 22 d following the start of drought imposition. Destructive measurements from all plants were performed at the conclusion of the experiment. The effect of line was significant for shoot dry matter, spike dry matter, root dry matter, and tiller number, while the water treatment was significant on shoot dry matter and root dry matter. The temporal, non-destructive nature of HTP allowed the drought treatment to be significantly differentiated from the well-watered treatment after 6 d in a line from Argentina and 9 d in a line from Chile. This difference of 3 d indicated an increased degree of drought tolerance in the line from Chile. Furthermore, HTP from the final day of imaging accurately predicted reference plant height(r=1), shoot dry matter(r=0.95) and tiller number(r=0.91). This experiment illustrates the potential of HTP and its use in modeling plant growth and development.     

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。     

A 3D white referencing method for soybean leaves based on fusion of hyperspectral images and 3D point clouds

Precision Agriculture - In recent years, plant phenotyping technologies have been widely applied to evaluate complex plant traits such as morphology, physiology, ecology, biochemistry, tolerance,...     

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.     

A minirhizotron imaging system to identify roots expressing the green fluorescent protein

The limited flexibility available in the configuration of commercial minirhizotron imaging systems makes it difficult to adapt these systems to new applications. It is also too expensive to introduce modifications, which are often very temporary to these systems at the end of the development process.In order to identify the roots of a single species in mixed plant stands, we developed a new minirhizotron imaging system that makes it possible to observe roots expressing green fluorescent protein (GFP). This system is based on affordable and easily obtainable components such as webcams. Here, we report a protocol to identify suitable webcams for constructing a minirhizotron imaging system and demonstrate the application of this protocol to build a minirhizotron imaging system that can identify the roots of a transformed maize plant expressing GFP.     

Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress

In this study the suitability of thermal imaging for phenotyping was investigated as part of a breeding experiment carried out by the International Maize and Wheat Improvement Centre (CIMMYT) at Tlaltizapán experimental station in Mexico. Different subtropical maize genotypes with two replications were screened with respect to their tolerance to water stress. Thermal images of the canopy of 92 different maize genotypes were acquired on two different days in the time interval between anthesis and blister stages (grain filling 1), whereby each picture contained five plots of different genotypes and canopy temperatures calculated for each plot. Significantly, lower canopy temperatures were found in well-watered genotypes compared with water-stressed genotypes. Furthermore significant differences (p < 0.001) between genotypes under water stress were detected using thermal images. A close correlation (p < 0.01–0.001) between canopy temperature or modified Crop water stress index with NDVI and SPAD values was obtained. It may be concluded that genotypes better adapted to drought conditions exhibited lower temperatures.Thermography is a potentially promising method to accelerate the screening process and thereby enhance phenotyping for drought adaptation in maize.     

Precision phenotyping of contrasting potato(Solanum tuberosum L.) varieties in a novel aeroponics system for improving nitrogen use efficiency: In search of key traits and genes

With increasing population, degrading soil health, limited arable land area, and high cost of nitrogen(N) fertilizers, improving nitrogen use efficiency(NUE) of potato is an inevitable approach to save the environment and achieve sufficient tuber yields with less N fertilizer supply. Recently, we have developed an aeroponics system to study NUE in potato using genomics, physiology, and breeding approaches. This study aims on precision phenotyping of plants of two distinct potato varieties(Kufri Gaurav, N efficient; Kufri Jyoti, N inefficient) in the novel aeroponics system. Plants were grown in aeroponics under controlled conditions with low N(0.75 mmol L~(-1) NO_3~-) and high N(7.5 mmol L~(–1) NO_3~-) levels. Plant biomass, root traits, total chlorophyll content, and plant N were increased with increasing N supply, whereas higher NUE parameters namely NUE, agronomic NUE(Ag NUE), N uptake efficiency(NUp E), harvest index(HI), and N harvest index(NHI) were observed at low N. An NUE efficient cv. Kufri Gaurav showed higher tuber dry weight, fresh tuber yield, tuber number per plant, early start of tuber harvesting, root traits, stolon traits, NUE parameters, and higher amino acid(aspartic acid and asparagine) content at low N supply. Higher expression of nitrate reductase(NR), nitrite reductase(NIR), and asparagine synthetase(AS) genes was observed in the leaf tissues of Kufri Gaurav at high N. Thus, aeroponics-based precision phenotyping enables identification of NUE efficient genotypes based on key traits and genes involved in improving NUE in potato. Further, this study suggests that the potential of aeroponics can be utilized to investigate N biology in potato under different N regimes.     

A 15N-Labeling Study of the Capture of Deep Soil Nitrate from Different Plant Systems

The objective of this study was to determine the efficiency of different plant systems in capturing deep soil nitrate (NO₃⁻) to reduce NO₃⁻ leaching in a field plot experiment using ¹⁵N labelling. The study was conducted on a calcareous alluvial soil on the North China Plains and the plant systems evaluated included alfalfa (Medicago sativa), American black poplar (Populus nigra) and cocksfoot (Dactylis). ¹⁵N-labelled N fertilizer was injected to 90 cm depth to determine the recovery of ¹⁵N by the plants. With conventional water and nutrient management, the total recovery of ¹⁵N-labeled NO₃⁻-N was 23.4% by alfalfa after two consecutive growth years. The recovery was significantly higher than those by American black poplar (12.3%) and cocksfoot (11.4%). The highest proportion of soil residual ¹⁵N from the labeled fertilizer N (%Ndff) was detected around 90 cm soil depth at the time of the 1st year harvest and at 110-130 cm soil depth at time of the 2nd year harvest. Soil %Ndff in 0-80 cm depth was significantly higher in the alfalfa treatment than those in all the other treatments. The soil %Ndff below 100 cm depth was much lower in the alfalfa than those in all the other treatments. These results indicated that ¹⁵N leaching losses in the alfalfa treatment were significantly lower than by those in the black poplar and cocksfoot treatments, due to the higher root density located in nitrate labeling zone of soil profile. In conclusion, alfalfa may be used as a plant to capture deep soil NO₃⁻ left from previous crops to reduce NO₃⁻ leaching in high intensity crop cultivation systems of North China Plain.     

L-系统的基本概念和示例

L－系统（L-system）是表达植物形态结构和生长规则的重要方法,因而是设计虚拟植物,模拟植物生长发育过程和农作物育种的重要工具,本文力求深入浅出而又不失严谨地介绍L－系统的基本概念,分类和典型示例,希望通过本文能引起广大农业科研工作者对L－系统的兴趣。     

作物胁迫早期症状监测的新图像技术

传统的作物胁迫早期症状诊断主要运用生化和分子方法对样品进行破坏性检测实现。计算机图像技术能在作物出现明显症状和不可逆转伤害前及时检测出作物的胁迫情况。尤其是在通过检测作物叶片散发光图像中与胁迫相关的变化量方面,热红外图像、荧光图像、反射多光谱和高光谱图像等新技术显示出较大的潜力。介绍和讨论了这些新图像技术在作物胁迫监测中的运用情况,并展望运用多种图像技术融合是作物综合胁迫早期监测诊断的发展趋势。     

Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points

Crop canopy spatial parameters are indicative of plant phenological growth stage and physiological condition, and their estimation is therefore of great interest for modeling and precision agriculture practices. Rapid increases in computing power have made stereovision models an attractive alternative to common single-image-based 2D methods, by allowing detailed estimation of the plant’s growth parameters regardless of imaging conditions. Models that have been proposed thus far are still limited in their application because of sensitivity to outdoor illumination conditions and the inherent difficulty in modeling complex plant shapes using only radiometric information. Assuming that not all of the plant-related pixels are essential for growth estimation, this study proposes a 3D reconstruction model that focuses on selected salient features on the plant surface, which are sufficient for obtaining growth characteristics. In addition, by introducing a hue-invariant model, the proposed algorithm shows robustness to diverse outdoor illumination conditions. The algorithm was tested under greenhouse and field conditions on corn, cotton, sunflower, tomato and black nightshade plants, from young seedlings to fully developed plant growth stages, and accurately estimated height (error ~4.5 %) and leaf cover area (error ~5 %). Furthermore, a strong correlation (r² ~0.92) was found between the plant’s estimated volume and measured biomass, yielding an accurate biomass estimator in the validation tests (error ~4.5 %). This estimation ability remained stable while applying the model on plants with varying densities (overlapping leaves) and imaging setups where the standard 2D based analyses failed, thus showing the 3D modeling contribution to robust growth estimation models.     

不同栽培深度对张良姜生长及产量的影响

通过研究不同栽培深度对张良姜出苗、生长、产量及品质的影响,以期为生产中科学栽培提供理论依据。试验在露地条件下进行,通过深度试验(5 cm、10 cm、20 cm、高垄)研究了张良姜在不同栽培深度、不同时期在株高、茎粗、叶片数、子姜级数、单株重等方面的差异。结果表明,栽培深度10 cm最利于张良姜的出苗;栽培深度20 cm利于张良姜前期地上部生长;面对降水较多的异常天气,高垄栽培张良姜品质最优、产量最高。     

扁穗牛鞭草无性繁殖根系形成发育多样性的研究

研究了15份扁穗牛鞭草资源无性繁殖单株根系的形成时间及发育过程。结果表明:①根形成时间存在着差异,初生根和次生根形成时间的变异系数分别为32.36%、20.90%;②根系入土深度在苗期、成苗期、成株期存在着差异,变异系数分别为70.39%、35.36%、19.81%;③根系分布范围在成苗期、成株期存在着差异,变异系数分别为58.03%、37.56%;④成株期根系重量有差异,变异系数为58.03%;⑤成苗期根系深度与分布范围存在极显著正相关(R=0.76530),苗期根数与成苗期根系深度呈极显著负相关(R=-0.68871),与成株期根系分布范围呈显著负相关(R=-0.58941),成苗根系深度与成苗根系分布范围呈极显著正相关(R=0.74745),成苗根系分布范围与成株根系分布范围呈极显著正相关(R=0.82279)。     

Registration of multispectral 3D points for plant inspection

Machine vision technologies have shown advantages for efficient and accurate plant inspection in precision agriculture. Regarding the balance between accuracy of inspection and compactness for infield applications, multispectral imaging systems would be more suitable than RGB colour cameras or hyperspectral imaging systems. Multispectral image registration (MIR) is a key issue for multispectral imaging systems, however, this task is challenging. First of all, in many cases, two images needing registration do not have a one-to-one linear mapping in 2D space and therefore they cannot be aligned in 2D images. Furthermore, the general MIR algorithms are limited to images with uniform intensity and are incapable of registering images with rich features. This study developed a machine vision system (MVS) and a MIR method which replaces 2D-2D image registration by 3D-3D point cloud registration. The system can register 3D point clouds of ultraviolet (UV), blue, green, red and near-infrared (NIR) spectra in 3D space. It was found that the point clouds of general plants created by images of different spectral bands have a complementary property, and therefore a combined point cloud, called multispectral 3D point cloud, is denser than any cloud created by a single spectral band. Intensity information of each spectral band is available in a multispectral 3D point cloud and therefore image fusion and 3D morphological analysis can be conducted in the cloud. The MVS could be used as a sensor of a robotic system to fulfil on-the-go infield plant inspection tasks.     

数字植物研究进展:植物形态结构三维数字化

数字植物围绕农林植物生命、生产和生态系统的多维信息高效感知和认知的理论、技术和方法,通过多学科交叉合作,研究农林植物-环境3D数字化、高通量信息获取、情景感知、信息融合、结构和功能模拟、数字化设计和精准管理决策等数字农业的关键性、基础性以及共性理论和技术问题。植物形态结构的三维数字化是数字植物研究的重要组成部分,近年来很多学者从植物组织、器官、植株和群体等不同尺度,或者从植物根系和地上部等不同视角,围绕植物形态结构的参数测量、几何结构解析、三维模型构建、结构与功能建模,以及三维植物模型真实感展现等需要,开展了更深入的研究。在组织尺度方面,随着MRI、CT、显微成像等技术产品的不断成熟,使得利用这些先进测量仪器获取和测量植物组织内部结构数据成为可能,并被越来越多的研究者采用,成为进行植物内部形态结构测量和分析的有效手段。在植物根系的三维数字化方面,由于植物的根普遍生长在土壤里,观察和测量十分困难。虽然近年来XCT、MRI等穿透射线成像技术已越来越多地用于根系的形态结构探测,但这类技术往往仅能获取范围较小的根系局部数据,且价格昂贵。因此植物根系形态结构的准确、无损(原位)、快速测量仍然是一个挑战。在群体尺度方面,基于实测数据的三维重建逐渐成为植物群体三维重建的主要途径,研究者正试图从激光三维扫描仪获取的植物群体三维点云中提取群体的形态参数并实现群体的三维重构。而在三维植物模型的真实感绘制方面,如何准确地测量各种植物器官的光学特性并建立相应的数学模型是当前的研究重点,虽然已有不少研究者提出了相应的解决方案,但这些方法在便捷性和普适性方面仍然难以令人满意,有待更多深入研究。笔者最后结合相关领域的技术进展对数字植物的进一步研究进行了展望。     

植物生理学课程考试改革探讨

结合植物生理学课程的特点,分析了安徽农业大学现行课程考试的现状以及存在的弊端,提出了多元化考试方式、开放式监考模式以及科学评分体系等改革措施,有助于学生学习兴趣的提高和创新思维的培养,从而为促进创新型人才培养和高校考试改革提供参考.