Genetic analyses of embryo homology and ontogeny in the model grass Zea mays subsp. mays.

The homology of the single cotyledon of grasses and the ontogeny of the scutellum and coleoptile as the initial, highly modified structures of the grass embryo are investigated using leaf developmental genetics and targeted transcript analyses in the model grass Zea mays subsp. mays. Transcripts of leaf developmental genes are identified in both the initiating scutellum and the coleoptile, while mutations disrupting mediolateral leaf development also disrupt scutellum and coleoptile morphology, suggesting that these grass-specific organs are modified leaves. Higher-order mutations in WUSCHEL-LIKE HOMEOBOX3 (WOX3) genes, involved in mediolateral patterning of plant lateral organs, inform a model for the fusion of coleoptilar margins during maize embryo development. Genetic, RNA-targeting, and morphological evidence supports models for cotyledon evolution where the scutellum and coleoptile, respectively, comprise the distal and proximal domains of the highly modified, single grass cotyledon.

The pore-rhizosheath shapes maize root architecture by enhancing root distribution in macropores.

Pores and old root-channels are preferentially used by roots to allow them to penetrate hard soils. However, there are few studies that have accounted for the effects of pore-rhizosheath on root growth. In this study, we developed an approach by adding the synthetic root exudates using a porous stainless tube with 0.1-mm micropores through a peristaltic pump to reproduce the rhizosheath around the artificial pore, and investigated the effects of pores with and without rhizosheaths on maize root growth in a dense soil. The results indicated that the artificial rhizosheath was about 2.69 mm wide in the region surrounding the pores. The rhizosheath had a higher content of organic carbon, total nitrogen, and abundance of Actinobacteria than that of the bulk soil. Compared with the artificial macropores, the artificial root-pores with a rhizosheath increased the opportunities for root utilisation of the pores space, promoting steeper and deeper root growth. It is concluded that the pore-rhizosheath has a significant impact on root architecture by enhancing root distribution in macropores.

Genetic analysis and QTL mapping for pericarp thickness in maize (Zea mays L.).

Proper pericarp thickness protects the maize kernel against pests and diseases, moreover, thinner pericarp improves the eating quality in fresh corn. In this study, we aimed to investigate the dynamic changes in maize pericarp during kernel development and identified the major quantitative trait loci (QTLs) for maize pericarp thickness. It was observed that maize pericarp thickness first increased and then decreased. During the growth and formation stages, the pericarp thickness gradually increased and reached the maximum, after which it gradually decreased and reached the minimum during maturity. To identify the QTLs for pericarp thickness, a BC4F4 population was constructed using maize inbred lines B73 (recurrent parent with thick pericarp) and Baimaya (donor parent with thin pericarp). In addition, a high-density genetic map was constructed using maize 10 K SNP microarray. A total of 17 QTLs related to pericarp thickness were identified in combination with the phenotypic data. The results revealed that the heritability of the thickness of upper germinal side of pericarp (UG) was 0.63. The major QTL controlling UG was qPT1-1, which was located on chromosome 1 (212,215,145-212,948,882). The heritability of the thickness of upper abgerminal side of pericarp (UA) was 0.70. The major QTL controlling UA was qPT2-1, which was located on chromosome 2 (2,550,197-14,732,993). In addition, a combination of functional annotation, DNA sequencing analysis and quantitative real-time PCR (qPCR) screened two candidate genes, Zm00001d001964 and Zm00001d002283, that could potentially control maize pericarp thickness. This study provides valuable insights into the improvement of maize pericarp thickness during breeding.

Breeding maize of ideal plant architecture for high-density planting tolerance through modulating shade avoidance response and beyond.

Maize is a major staple crop widely used as food, animal feed, and raw materials in industrial production. High-density planting is a major factor contributing to the continuous increase of maize yield. However, high planting density usually triggers a shade avoidance response and causes increased plant height and ear height, resulting in lodging and yield loss. Reduced plant height and ear height, more erect leaf angle, reduced tassel branch number, earlier flowering, and strong root system architecture are five key morphological traits required for maize adaption to high-density planting. In this review, we summarize recent advances in deciphering the genetic and molecular mechanisms of maize involved in response to high-density planting. We also discuss some strategies for breeding advanced maize cultivars with superior performance under high-density planting conditions.

High hopes for short corn.

Plants bred or engineered to be short can stand up better to windstorms. They could also boost yields and benefit the environment.

The 2020 derecho revealed limited overlap between maize genes associated with root lodging and root system architecture.

Roots anchor plants in soil, and the failure of anchorage (i.e. root lodging) is a major cause of crop yield loss. Anchorage is often assumed to be driven by root system architecture (RSA). We made use of a natural experiment to measure the overlap between the genetic regulation of RSA and anchorage. After one of the most devastating derechos ever recorded in August 2020, we phenotyped root lodging in a maize (Zea mays) diversity panel consisting of 369 genotypes grown in 6 environments affected by the derecho. Genome-wide and transcriptome-wide association studies identified 118 candidate genes associated with root lodging. Thirty-four percent (40/118) of these were homologs of genes from Arabidopsis (Arabidopsis thaliana) that affect traits such as root morphology and lignin content, expected to affect root lodging. Finally, gene ontology enrichment analysis of the candidate genes and their predicted interaction partners at the transcriptional and translational levels revealed the complex regulatory networks of physiological and biochemical pathways underlying root lodging in maize. Limited overlap between genes associated with lodging resistance and RSA in this diversity panel suggests that anchorage depends in part on factors other than the gross characteristics of RSA.

Identification of quantitative trait loci for related traits of stalk lodging resistance using genome-wide association studies in maize (Zea mays L.).

BACKGROUND: Stalk lodging is one of the main factors affecting maize (Zea mays L.) yield and limiting mechanized harvesting. Developing maize varieties with high stalk lodging resistance requires exploring the genetic basis of lodging resistance-associated agronomic traits. Stalk strength is an important indicator to evaluate maize lodging and can be evaluated by measuring stalk rind penetrometer resistance (RPR) and stalk buckling strength (SBS). Along with morphological traits of the stalk for the third internodes length (TIL), fourth internode length (FIL), third internode diameter (TID), and the fourth internode diameter (FID) traits are associated with stalk lodging resistance. RESULTS: In this study, a natural population containing 248 diverse maize inbred lines genotyped with 83,057 single nucleotide polymorphism (SNP) markers was used for genome-wide association study (GWAS) for six stalk lodging resistance-related traits. The heritability of all traits ranged from 0.59 to 0.72 in the association mapping panel. A total of 85 significant SNPs were identified for the association mapping panel using best linear unbiased prediction (BLUP) values of all traits. Additionally, five candidate genes were associated with stalk strength traits, which were either directly or indirectly associated with cell wall components. CONCLUSIONS: These findings contribute to our understanding of the genetic basis of maize stalk lodging and provide valuable theoretical guidance for lodging resistance in maize breeding in the future.

Maize plant architecture trait QTL mapping and candidate gene identification based on multiple environments and double populations.

BACKGROUND: The plant architecture traits of maize determine the yield. Plant height, ear position, leaf angle above the primary ear and internode length above the primary ear together determine the canopy structure and photosynthetic efficiency of maize and at the same time affect lodging and disease resistance. A flat and tall plant architecture confers an obvious advantage in the yield of a single plant but is not conducive to dense planting and results in high rates of lodging; thus, it has been gradually eliminated in production. Although using plants that are too compact, short and density tolerant can increase the yield per unit area to a certain extent, the photosynthetic efficiency of such plants is low, ultimately limiting yield increases. Genetic mapping is an effective method for the improvement of plant architecture to identify candidate genes for regulating plant architecture traits. RESULTS: To find the best balance between the yield per plant and the yield per unit area of maize, in this study, the F2:3 pedigree population and a RIL population with the same male parent were used to identify QTL for plant height (PH), ear height (EH), leaf angle and internode length above the primary ear (LAE and ILE) in Changchun and Gongzhuling for 5 consecutive years (2016-2020). A total of 11, 13, 23 and 13 QTL were identified for PH, EH, LAE, and ILE, respectively. A pleiotropic consistent QTL for PH overlapped with that for EH on chromosome 3, with a phenotypic variation explanation rate from 6.809% to 21.96%. In addition, there were major consistent QTL for LAE and ILE, and the maximum phenotypic contribution rates were 24.226% and 30.748%, respectively. Three candidate genes were mined from the three consistent QTL regions and were involved in the gibberellin-activated signal pathway, brassinolide signal transduction pathway and auxin-activated signal pathway, respectively. Analysis of the expression levels of the three genes showed that they were actively expressed during the jointing stage of vigorous maize growth. CONCLUSIONS: In this study, three consistent major QTL related to plant type traits were identified and three candidate genes were screened. These results lay a foundation for the cloning of related functional genes and marker-assisted breeding of related functional genes.

GIF1 controls ear inflorescence architecture and floral development by regulating key genes in hormone biosynthesis and meristem determinacy in maize.

BACKGROUND: Inflorescence architecture and floral development in flowering plants are determined by genetic control of meristem identity, determinacy, and maintenance. The ear inflorescence meristem in maize (Zea mays) initiates short branch meristems called spikelet pair meristems, thus unlike the tassel inflorescence, the ears lack long branches. Maize growth-regulating factor (GRF)-interacting factor1 (GIF1) regulates branching and size of meristems in the tassel inflorescence by binding to Unbranched3. However, the regulatory pathway of gif1 in ear meristems is relatively unknown. RESULT: In this study, we found that loss-of-function gif1 mutants had highly branched ears, and these extra branches repeatedly produce more branches and florets with unfused carpels and an indeterminate floral apex. In addition, GIF1 interacted in vivo with nine GRFs, subunits of the SWI/SNF chromatin-remodeling complex, and hormone biosynthesis-related proteins. Furthermore, key meristem-determinacy gene RAMOSA2 (RA2) and CLAVATA signaling-related gene CLV3/ENDOSPERM SURROUNDING REGION (ESR) 4a (CLE4a) were directly bound and regulated by GIF1 in the ear inflorescence. CONCLUSIONS: Our findings suggest that GIF1 working together with GRFs recruits SWI/SNF chromatin-remodeling ATPases to influence DNA accessibility in the regions that contain genes involved in hormone biosynthesis, meristem identity and determinacy, thus driving the fate of axillary meristems and floral organ primordia in the ear-inflorescence of maize.

Maize stalk stiffness and strength are primarily determined by morphological factors.

The maize (Zea mays) stem is a biological structure that must balance both biotic and structural load bearing duties. These competing requirements are particularly relevant in the design of new bioenergy crops. Although increased stem digestibility is typically associated with a lower structural strength and higher propensity for lodging, with the right balance between structural and biological activities it may be possible to design crops that are high-yielding and have digestible biomass. This study investigates the hypothesis that geometric factors are much more influential in determining structural strength than tissue properties. To study these influences, both physical and in silico experiments were used. First, maize stems were tested in three-point bending. Specimen-specific finite element models were created based on x-ray computed tomography scans. Models were validated by comparison with experimental data. Sensitivity analyses were used to assess the influence of structural parameters such as geometric and material properties. As hypothesized, geometry was found to have a much stronger influence on structural stability than material properties. This information reinforces the notion that deficiencies in tissue strength could be offset by manipulation of stalk morphology, thus allowing the creation of stalks which are both resilient and digestible.

Responses and sensitivities of maize phenology to climate change from 1971 to 2020 in Henan Province, China.

Climate change affects many aspects of the physiological and biochemical processes of growing maize and ultimately its yield. A comprehensive climate suitability model is proposed that quantifies the effects of temperature, precipitation, solar radiation, and wind in different phenological stages of maize. It is calibrated using weather and yield data from China's Henan Province. The comprehensive suitability model showed the capability of correctly hindcasting observed temporal and spatial changes in maize phenology in response to climatic factors. The predicted yield based on the suitability model can well match the recorded field yield very well from 1971-2020. The results of correlation showed that the yields are more closely related to multi-weather factors, temperature and precipitation than to solar radiation and wind. The sensitivity analysis illustrates that temperature and precipitation are the dominant weather factors affecting yield changes based on a direct differentiation method. The comprehensive suitability model can provide a scientific support and analysis tool for predicting grain production considering climate changes.

Morphological Characterization and Transcriptome Analysis of New Dwarf and Narrow-Leaf (dnl2) Mutant in Maize.

Lodging is the primary factor limiting high yield under a high plant density. However, an optimal plant height and leaf shape can effectively decrease the lodging risk. Here we studied an ethyl methanesulfonate (EMS)-induced dwarf and a narrow-leaf mutant, dnl2. Gene mapping indicated that the mutant was controlled by a gene located on chromosome nine. Phenotypic and cytological observations revealed that dnl2 showed inhibited cell growth, altered vascular bundle patterning, and disrupted secondary cell wall structure when compared with the wild-type, which could be the direct cause of the dwarf and narrow-leaf phenotype. The phytohormone levels, especially auxin and gibberellin, were significantly decreased in dnl2 compared to the wild-type plants. Transcriptome profiling of the internodes of the dnl2 mutant and wild-type revealed a large number of differentially expressed genes enriched in the cell wall biosynthesis, remodeling, and hormone biosynthesis and signaling pathways. Therefore, we suggest that crosstalk between hormones (the altered vascular bundle and secondary cell wall structure) may contribute to the dwarf and narrow-leaf phenotype by influencing cell growth. These results provide a foundation for DNL2 gene cloning and further elucidation of the molecular mechanism of the regulation of plant height and leaf shape in maize.

Tissue-specific transcriptomics reveal functional differences in floral development.

Flowers are produced by floral meristems, groups of stem cells that give rise to floral organs. In grasses, including the major cereal crops, flowers (florets) are contained in spikelets, which contain one to many florets, depending on the species. Importantly, not all grass florets are developmentally equivalent, and one or more florets are often sterile or abort in each spikelet. Members of the Andropogoneae tribe, including maize (Zea mays), produce spikelets with two florets; the upper and lower florets are usually dimorphic, and the lower floret is greatly reduced compared to the upper floret. In maize ears, early development appears identical in both florets but the lower floret ultimately aborts. To gain insight into the functional differences between florets with different fates, we used laser capture microdissection coupled with RNA-sequencing to globally examine gene expression in upper and lower floral meristems in maize. Differentially expressed genes were involved in hormone regulation, cell wall, sugar, and energy homeostasis. Furthermore, cell wall modifications and sugar accumulation differed between the upper and lower florets. Finally, we identified a boundary domain between upper and lower florets, which we hypothesize is important for floral meristem activity. We propose a model in which growth is suppressed in the lower floret by limiting sugar availability and upregulating genes involved in growth repression. This growth repression module may also regulate floret fertility in other grasses and potentially be modulated to engineer more productive cereal crops.

Design and analysis of a performance monitoring system for a seed metering device based on pulse width recognition.

To realize real-time and accurate performance monitoring of large- and medium-sized seed metering devices, a performance monitoring system was designed for seed metering devices based on LED visible photoelectric sensing technology and a pulse width recognition algorithm. Through an analysis of the of sensing component pointing characteristics and seed motion characteristics, the layout of the sensing components and critical photoelectric sensing system components was optimized. Single-grain seed metering devices were employed as monitoring objects, and the pulse width thresholds for Ekangmian-10 cotton seeds and Zhengdan-958 corn seeds were determined through pulse width threshold calibration experiments employed at different seed metering plate rotational speeds. According to the seeding quantity monitoring experiments, when the seed metering plate rotational speed ranged from 28.31~35.71 rev/min, the accuracy reached 98.41% for Ekangmian-10 cotton seeds. When the seed metering plate rotational speed ranged from 13.78~19.39 rev/min, the seeding quantity monitoring accuracy reached 98.19% for Zhengdan-958 corn seeds. Performance monitoring experiments revealed that the qualified seeding quantity monitoring accuracy of cotton precision seed metering devices, missed seeding quantity monitoring accuracy, and reseeding quantity monitoring accuracy could reach 98.75%, 94.06%, and 91.30%, respectively, within a seeding speed range of 8~9 km/h. This system meets the requirements of real-time performance monitoring of large- and medium-sized precision seed metering devices, which helps to improve the operational performance of seeding machines.

Association mapping for general combining ability with yield, plant height and ear height using F1 population in maize.

General combining ability (GCA) is an important index for inbred lines breeding of maize. To identify the genetic loci of GCA and associated agronomic traits, an association analysis with 195 SSRs was made in phenotypic traits of 240 F1 derived from 120 elite inbred lines containing current breeding resources of maize crossed with 2 testers (Zheng58 and Chang7-2) in two places in 2018. All of the 20 association loci detected for grain yield (GY), plant height (PH), ear height (EH) and GCA for the three traits in two places could explain a phenotypic variation range of 7.31%-9.29%. Among the 20 association loci, 9 (7.31%-9.04%) were associated with GY, 4 (7.22%-8.91%) were related to GCA of GY, 1 (7.56%) was associated with PH, and 3 (7.53%-8.96%) were related to EH. In addition, 3 loci (9.14%-9.29%) were associated with GCA of PH whereas no locus was identified for GCA of EH. In the comparison of the association loci detected in Baoding and Handan, interestingly, one locus (7.69% and 8.11%) was identified in both environments and one locus (7.52% and 7.82%) was identified for yield and GCA of yield. Therefore, the identification of GY-, PH-, EH- and GCA-related association loci could not only provide references for high yield breeding of maize, but also help us comprehend the relationships among GY, agricultural traits and GCA.

A stomata classification and detection system in microscope images of maize cultivars.

Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.

DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays).

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.

Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms.

Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maize (Zea mays). LAI estimation models use multi-source data from unmanned aerial vehicles (UAVs), but using multimodal data to estimate maize LAI, and the effect of tassels and soil background, remain understudied. Our research aims to (1) determine how multimodal data contribute to LAI and propose a framework for estimating LAI based on remote-sensing data, (2) evaluate the robustness and adaptability of an LAI estimation model that uses multimodal data fusion and deep neural networks (DNNs) in single- and whole growth stages, and (3) explore how soil background and maize tasseling affect LAI estimation. To construct multimodal datasets, our UAV collected red-green-blue, multispectral, and thermal infrared images. We then developed partial least square regression (PLSR), support vector regression, and random forest regression models to estimate LAI. We also developed a deep learning model with three hidden layers. This multimodal data structure accurately estimated maize LAI. The DNN model provided the best estimate (coefficient of determination [R2] = 0.89, relative root mean square error [rRMSE] = 12.92%) for a single growth period, and the PLSR model provided the best estimate (R2 = 0.70, rRMSE = 12.78%) for a whole growth period. Tassels reduced the accuracy of LAI estimation, but the soil background provided additional image feature information, improving accuracy. These results indicate that multimodal data fusion using low-cost UAVs and DNNs can accurately and reliably estimate LAI for crops, which is valuable for high-throughput phenotyping and high-spatial precision farmland management.

Evaluation of corn hybrids for silage grown in different locations / Avaliação de híbridos de milho para silagem cultivados em diferentes locais

The aim of this study was to evaluate the yield, morphometric and chemical characteristics, and the digestibility of various corn hybrids cultivated in different locations for silage production. Four corn hybrids were grown: P2866H, P3456H, P30R50VYH and P4285YHR, in four locations, defined as farm A; B; C and D. The hybrid P4285YHR presented the highest plant and ear insertion heights (2.72m, 1.52m respectively), with a minimum registered height of 2.63 m and 1.42 m, for plant and ear insertion height, and for that reason it presented the highest yield of fresh biomass (78,089kg ha-1). The TND values showed trend very close to that observed in the DISMS of the whole-plant, with hybrid P3456H presenting the highest estimated value (68.74%), and the hybrid P4285YHR the lowest among them (65.25%). In general, lower fibrous carbohydrates and lignin content, higher fibrous carbohydrates content, together with satisfactory grains participation in the plant structure led to a greater dry matter digestibility. Hybrids with lower plant heights do not necessarily have lower dry biomass yields, but hybrids of greater height and with high stem and leaf participation tend to have a higher aFDN content and less dry matter digestibility.(AU)

O objetivo deste estudo foi avaliar o desempenho produtivo e as características morfométricas e químicas, além da digestibilidade de diferentes híbridos de milho cultivados em diferentes locais para produção de silagem. Foram cultivados quatro híbridos de milho: P2866H, P3456H, P30R50VYH e P4285YHR, em quatro locais, definidos como propriedades A; B; C e D. O híbrido P4285YHR apresentou as maiores alturas de planta e de inserção de espiga (2,72m, 1,52m, respectivamente), com altura mínima registrada, nos diferentes locais, de 2,63m e 1,42m, para altura de planta e inserção de espiga, e, por isso, apresentou o maior rendimento de biomassa fresca (78.089kg ha-1). Os valores calculados de NDT mostraram tendência muito próxima àquela observada na DISMS da planta inteira, tendo o híbrido P3456H apresentado o maior valor estimado (68,74%), e o híbrido P4285YHR o menor entre eles (65,25%). Em geral, menores concentrações de carboidratos fibrosos e lignina, maior teor de carboidratos fibrosos, juntamente com satisfatória participação de grãos na estrutura da planta, conduzem a uma maior digestibilidade da matéria seca. Híbridos com menores alturas de planta não necessariamente apresentam menores rendimentos de biomassa seca, mas híbridos de maior altura e com alta participação de colmo e folhas tendem a apresentar maior teor de aFDN e menor digestibilidade da matéria seca.(AU)

Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis.

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.