Plant Phenomics最新文献

{"title":"Quantitative Assessment of Ultraviolet-Induced Erythema and Tanning Responses in the Han Chinese Population.","authors":"Yanyun Ma, Yimei Tan, Yue Hu, Weilin Pu, Jinhua Xu, Li Jin, Jiucun Wang","doi":"10.1007/s43657-023-00105-1","DOIUrl":"10.1007/s43657-023-00105-1","url":null,"abstract":"<p><p>Ultraviolet radiation (UVR) can induce erythema and tanning responses with strong diversity within and between populations, but there were no precise method for evaluating the variation in these responses. In this study, we assessed the time course of ultraviolet (UV)-induced responses based on the erythema index (EI) and melanin index (MI) over 14 consecutive days in a pilot cohort study (N = 31). From safety evaluations, we found that no skin blisters occurred at a UV dosage of 45 mJ/cm², but there were significant skin reactions. Regardless of UV dosage, the measurements and variances of EI peaked on day 1 after UV irradiation, and those of MI peaked on day 7. Dose-response curves, including erythema dose-response (EDR) and melanin dose-response (MDR), could measure UV-induced phenotypes sensitively but more laboriously. As an alternative, we directly represented the UV-induced erythema and tanning responses using the erythema increment (ΔE) and melanin increment (ΔM). We found that ΔE and ΔM at 45 mJ/cm² significantly correlated with erythema dose-response (EDR) (<i>R</i> ² > 0.9) and melanin dose-response (MDR) (<i>R</i> ² > 0.9), respectively. Therefore, ΔE and ΔM on day 1 and day 7 after UV irradiation at a dosage of 45 mJ/cm² might be ideal alternative measures for assessing individual erythema and tanning responses. Then, a second cohort (N = 664) was recruited to validate the UV-induced phenotypes, and, as expected, the results of the two cohorts were in agreement. Therefore, we developed a simplified and precise method to quantify the UV-induced erythema response and tanning ability for the Han Chinese population.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s43657-023-00105-1.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11169260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81022117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review.","authors":"Jason John Walsh, Eleni Mangina, Sonia Negrão","doi":"10.34133/plantphenomics.0153","DOIUrl":"10.34133/plantphenomics.0153","url":null,"abstract":"<p><p>Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10905704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140022478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protocol for Brain Magnetic Resonance Imaging and Extraction of Imaging-Derived Phenotypes from the China Phenobank Project.","authors":"Chengyan Wang, Zhang Shi, Yan Li, Xueqin Xia, Xutong Kuang, Shufang Qian, Le Xue, Lizhen Lan, Yudan Wu, Na Zhang, Ji Tao, Xumei Hu, Wenzhao Cao, Naying He, Yike Guo, Weibo Chen, Jun Zhang, Jingchun Luo, He Wang, Mei Tian","doi":"10.1007/s43657-022-00083-w","DOIUrl":"10.1007/s43657-022-00083-w","url":null,"abstract":"<p><p>Imaging-derived phenotypes (IDPs) have been increasingly used in population-based cohort studies in recent years. As widely reported, magnetic resonance imaging (MRI) is an important imaging modality for assessing the anatomical structure and function of the brain with high resolution and excellent soft-tissue contrast. The purpose of this article was to describe the imaging protocol of the brain MRI in the China Phenobank Project (CHPP). Each participant underwent a 30-min brain MRI scan as part of a 2-h whole-body imaging protocol in CHPP. The brain imaging sequences included <i>T</i>₁-magnetization that prepared rapid gradient echo, <i>T</i>₂ fluid-attenuated inversion-recovery, magnetic resonance angiography, diffusion MRI, and resting-state functional MRI. The detailed descriptions of image acquisition, interpretation, and post-processing were provided in this article. The measured IDPs included volumes of brain subregions, cerebral vessel geometrical parameters, microstructural tracts, and function connectivity metrics.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10781909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87416270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Instance Segmentation and Berry Counting of Table Grape before Thinning Based on AS-SwinT.","authors":"Wensheng Du,&nbsp;Ping Liu","doi":"10.34133/plantphenomics.0085","DOIUrl":"https://doi.org/10.34133/plantphenomics.0085","url":null,"abstract":"<p><p>Berry thinning is one of the most important tasks in the management of high-quality table grapes. Farmers often thin the berries per cluster to a standard number by counting. With an aging population, it is hard to find adequate skilled farmers to work during thinning season. It is urgent to design an intelligent berry-thinning machine to avoid exhaustive repetitive labor. A machine vision system that can determine the number of berries removed and locate the berries removed is a challenge for the thinning machine. A method for instance segmentation of berries and berry counting in a single bunch is proposed based on AS-SwinT. In AS-SwinT, Swin Transformer is performed as the backbone to extract the rich characteristics of grape berries. An adaptive feature fusion is introduced to the neck network to sufficiently preserve the underlying features and enhance the detection of small berries. The size of berries in the dataset is statistically analyzed to optimize the anchor scale, and Soft-NMS is used to filter the candidate frames to reduce the missed detection of densely shaded berries. Finally, the proposed method could achieve 65.7 <i>AP^box</i>, 95.0 <math><mi>A</mi><msubsup><mi>P</mi><mn>0.5</mn><mi>box</mi></msubsup></math>, 57 <math><mi>A</mi><msubsup><mi>P</mi><mi>s</mi><mi>box</mi></msubsup></math>, 62.8 <i>AP</i>^<i>mask</i>, 94.3 <math><mi>A</mi><msubsup><mi>P</mi><mn>0.5</mn><mtext>mask</mtext></msubsup></math>, 48 <math><mi>A</mi><msubsup><mi>P</mi><mi>s</mi><mtext>mask</mtext></msubsup></math>, which is markedly superior to Mask R-CNN, Mask Scoring R-CNN, and Cascade Mask R-CNN. Linear regressions between predicted numbers and actual numbers are also developed to verify the precision of the proposed model. <i>RMSE</i> and <i>R</i>² values are 7.13 and 0.95, respectively, which are substantially higher than other models, showing the advantage of the AS-SwinT model in berry counting estimation.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10465307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10193141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph.","authors":"Wenli Zhang,&nbsp;Yuxin Liu,&nbsp;Chao Zheng,&nbsp;Guoqiang Cui,&nbsp;Wei Guo","doi":"10.34133/plantphenomics.0067","DOIUrl":"https://doi.org/10.34133/plantphenomics.0067","url":null,"abstract":"Although deep learning-based fruit detection techniques are becoming popular, they require a large number of labeled datasets to support model training. Moreover, the manual labeling process is time-consuming and labor-intensive. We previously implemented a generative adversarial network-based method to reduce labeling costs. However, it does not consider fitness among more species. Methods of selecting the most suitable source domain dataset based on the fruit datasets of the target domain remain to be investigated. Moreover, current automatic labeling technology still requires manual labeling of the source domain dataset and cannot completely eliminate manual processes. Therefore, an improved EasyDAM_V3 model was proposed in this study as an automatic labeling method for additional classes of fruit. This study proposes both an optimal source domain establishment method based on a multidimensional spatial feature model to select the most suitable source domain, and a high-volume dataset construction method based on transparent background fruit image translation by constructing a knowledge graph of orchard scene hierarchy component synthesis rules. The EasyDAM_V3 model can automatically obtain fruit label information from the dataset, thereby eliminating manual labeling. To test the proposed method, pear was used as the selected optimal source domain, followed by orange, apple, and tomato as the target domain datasets. The results showed that the average precision of annotation reached 90.94%, 89.78%, and 90.84% for the target datasets, respectively. The EasyDAM_V3 model can obtain the optimal source domain in automatic labeling tasks, thus eliminating the manual labeling process and reducing associated costs and labor.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10374194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9909522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interaction of Genotype, Environment, and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat.","authors":"Bo Yao,&nbsp;Xiaolong Wang,&nbsp;Yancheng Wang,&nbsp;Tianyang Ye,&nbsp;Enli Wang,&nbsp;Qiang Cao,&nbsp;Xia Yao,&nbsp;Yan Zhu,&nbsp;Weixing Cao,&nbsp;Xiaojun Liu,&nbsp;Liang Tang","doi":"10.34133/plantphenomics.0078","DOIUrl":"https://doi.org/10.34133/plantphenomics.0078","url":null,"abstract":"<p><p>The organ-specific critical nitrogen (N_c) dilution curves are widely thought to represent a new approach for crop nitrogen (N) nutrition diagnosis, N management, and crop modeling. The N_c dilution curve can be described by a power function (N_c = A₁·W^-A2), while parameters A₁ and A₂ control the starting point and slope. This study aimed to investigate the uncertainty and drivers of organ-specific curves under different conditions. By using hierarchical Bayesian theory, parameters A₁ and A₂ of the organ-specific N_c dilution curves for wheat were derived and evaluated under 14 different genotype × environment × management (G × E × M) N fertilizer experiments. Our results show that parameters A₁ and A₂ are highly correlated. Although the variation of parameter A₁ was less than that of A₂, the values of both parameters can change significantly in response to G × E × M. Nitrogen nutrition index (NNI) calculated using organ-specific N_c is in general consistent with NNI estimated with overall shoot N_c, indicating that a simple organ-specific N_c dilution curve may be used for wheat N diagnosis to assist N management. However, the significant differences in organ-specific N_c dilution curves across G × E × M conditions imply potential errors in N_c and crop N demand estimated using a general N_c dilution curve in crop models, highlighting a clear need for improvement in N_c calculations in such models. Our results provide new insights into how to improve modeling of crop nitrogen-biomass relations and N management practices under G × E × M.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10396079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9993410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FCOS-LSC: A Novel Model for Green Fruit Detection in a Complex Orchard Environment.","authors":"Ruina Zhao,&nbsp;Yujie Guan,&nbsp;Yuqi Lu,&nbsp;Ze Ji,&nbsp;Xiang Yin,&nbsp;Weikuan Jia","doi":"10.34133/plantphenomics.0069","DOIUrl":"https://doi.org/10.34133/plantphenomics.0069","url":null,"abstract":"<p><p>To better address the difficulties in designing green fruit recognition techniques in machine vision systems, a new fruit detection model is proposed. This model is an optimization of the FCOS (full convolution one-stage object detection) algorithm, incorporating LSC (level scales, spaces, channels) attention blocks in the network structure, and named FCOS-LSC. The method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions, lighting conditions, and capture angles. Specifically, the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature information. The feature pyramid network (FPN) is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected way. Next, the attention mechanisms are added to each of the 3 dimensions of scale, space (including the height and width of the feature map), and channel of the generated multiscale feature map to improve the feature perception capability of the network. Finally, the classification and regression subnetworks of the model are applied to predict the fruit category and bounding box. In the classification branch, a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit detection. The proposed FCOS-LSC model has 38.65M parameters, 38.72G floating point operations, and mean average precision of 63.0% and 75.2% for detecting green apples and green persimmons, respectively. In summary, FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition using intelligent agricultural equipment. Correspondingly, FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10355323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9851154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SPSI: A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery.","authors":"Yapeng Wu,&nbsp;Wenhui Wang,&nbsp;Yangyang Gu,&nbsp;Hengbiao Zheng,&nbsp;Xia Yao,&nbsp;Yan Zhu,&nbsp;Weixing Cao,&nbsp;Tao Cheng","doi":"10.34133/plantphenomics.0087","DOIUrl":"https://doi.org/10.34133/plantphenomics.0087","url":null,"abstract":"<p><p>Rapid and accurate estimation of panicle number per unit ground area (PNPA) in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield. The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored. This study proposed a spectral-textural PNPA sensitive index (SPSI) from unmanned aerial vehicle (UAV) multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading. The effect of background materials on PNPA estimated by textural indices (TIs) was examined, and the composite index SPSI was constructed by integrating the optimal spectral index (SI) and TI. Subsequently, the performance of SPSI was evaluated in comparison with other indices (SI and TIs). The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI_[HOM], TI_[ENT], and TI_[SEM] among all indices from 8 types of textural features. SPSI, which was calculated by the formula DATT_{[850,730,675]} + NDTI_COR[850,730], exhibited the highest overall accuracies for any date in any dataset in comparison with DATT_{[850,730,675]}, TI_NDRE[MEA], and NDTI_COR[850,730]. For the unified models assembling 2 experimental datasets, the <i>R</i>_V² values of SPSI increased by 0.11 to 0.23, and both RMSE and RRMSE decreased by 16.43% to 38.79% as compared to the suboptimal index on each date. These findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10187856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping.","authors":"Jinnuo Zhang,&nbsp;Xuping Feng,&nbsp;Jian Jin,&nbsp;Hui Fang","doi":"10.34133/plantphenomics.0071","DOIUrl":"https://doi.org/10.34133/plantphenomics.0071","url":null,"abstract":"Currently, the presence of genetically modified (GM) organisms in agro-food markets is strictly regulated by enacted legislation worldwide. It is essential to ensure the traceability of these transgenic products for food safety, consumer choice, environmental monitoring, market integrity, and scientific research. However, detecting the existence of GM organisms involves a combination of complex, time-consuming, and labor-intensive techniques requiring high-level professional skills. In this paper, a concise and rapid pipeline method to identify transgenic rice seeds was proposed on the basis of spectral imaging technologies and the deep learning approach. The composition of metabolome across 3 rice seed lines containing the cry1Ab/cry1Ac gene was compared and studied, substantiating the intrinsic variability induced by these GM traits. Results showed that near-infrared and terahertz spectra from different genotypes could reveal the regularity of GM metabolic variation. The established cascade deep learning model divided GM discrimination into 2 phases including variety classification and GM status identification. It could be found that terahertz absorption spectra contained more valuable features and achieved the highest accuracy of 97.04% for variety classification and 99.71% for GM status identification. Moreover, a modified guided backpropagation algorithm was proposed to select the task-specific characteristic wavelengths for further reducing the redundancy of the original spectra. The experimental validation of the cascade discriminant method in conjunction with spectroscopy confirmed its viability, simplicity, and effectiveness as a valuable tool for the detection of GM rice seeds. This approach also demonstrated its great potential in distilling crucial features for expedited transgenic risk assessment.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10380542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9963324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Combined Genomics and Phenomics Approach is Needed to Boost Breeding in Sugarcane.","authors":"Ting Luo,&nbsp;Xiaoyan Liu,&nbsp;Prakash Lakshmanan","doi":"10.34133/plantphenomics.0074","DOIUrl":"https://doi.org/10.34133/plantphenomics.0074","url":null,"abstract":"Sugarcane is a major food and bioenergy crop globally. It produces ~80% of sugar consumed worldwide, with Brazil and India together accounting for 61% of world sugarcane production in 2021 [1]. Globally, sugarcane is the 5th largest crop by production value and acreage, and it is also the second largest bioenergy crop [1,2]. Modern sugarcane is an interspecific hybrid (Saccharum species hybrid) of wild progenitor species Saccharum officinarum (2n = 80; x = 10) and Saccharum spontaneum (2n = 40 to 130; x = 8) [3]. This genetically complex polyploid crop with varied chromosome numbers (100 to 130) has one of the largest genomes (~10 kb) among plants, making sugarcane breeding considerably slow and challenging. Sugarcane breeding involves visual clonal selection combined with manual screening for cane stalk weight and cane sugar content through a 10to 12-year-long multistage selection scheme with disease screening incorporated toward the end of the selection program. Globally, the rate of sugarcane yield improvement realized at commercial crop production level through breeding in recent decades remains considerably lower than that of other major crops, and in some breeding programs, genetic gain appears to have plateaued [1]. Long breeding cycle, practical difficulties for extensive phenotyping of breeding populations, low narrow-sense heritability of economically important traits, large complex polyploid genome with high heterozygosity, and genotype–environment– management interaction effects have been attributed to low rate of genetic gain. More specifically, the high biomass of sugarcane plants makes accurate detailed phenotyping logistically very challenging, which compromises selection accuracy. This is particularly so in the early stages of selection confounded by large interplot competition effects caused by small singleor 2-row plots [4]. Thus, accurate, cost-effective, and high-throughput phenotyping offers an excellent opportunity for more precise estimation of true yield potential of sugarcane clones in breeding trials, a major bottleneck for fast-tracking sugarcane improvement [5]. Recognizing the persisting slow yield improvement from sugarcane breeding and the accelerated genetic gains realized through molecular marker-assisted selection (MAS) in various other crops [6,7], some of the leading sugarcane industries invested substantial resources for sugarcane genome sequencing and MAS in the past 3 decades [8]. Over this period, sugarcane DNA marker systems have gradually evolved from the initial hybridizationbased [9] to the current DNA-sequence-derived singlenucleotide polymorphism (SNP) markers, facilitated by high-throughput nextgeneration sequencing technologies [8]. The rapid advancements in DNA sequencing and marker technologies led to the creation of genotyping systems for wholegenome profiling, such as genomic selection (GS), which further strengthened marker discovery and marker-trait association studies. GS is a robust genotyp","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10201157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}