Plant Phenomics最新文献

{"title":"EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph.","authors":"Wenli Zhang, Yuxin Liu, Chao Zheng, Guoqiang Cui, 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":"5 ","pages":"0067"},"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":"5 ","pages":"0078"},"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":"5 ","pages":"0069"},"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":"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":"5 ","pages":"0071"},"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":"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":"5 ","pages":"0087"},"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":"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":"5 ","pages":"0074"},"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}

{"title":"Determination of <i>F_v</i> /<i>F_m</i> from Chlorophyll <i>a</i> Fluorescence without Dark Adaptation by an LSSVM Model.","authors":"Qian Xia,&nbsp;Hao Tang,&nbsp;Lijiang Fu,&nbsp;Jinglu Tan,&nbsp;Govindjee Govindjee,&nbsp;Ya Guo","doi":"10.34133/plantphenomics.0034","DOIUrl":"https://doi.org/10.34133/plantphenomics.0034","url":null,"abstract":"<p><p>Evaluation of photosynthetic quantum yield is important for analyzing the phenotype of plants. Chlorophyll <i>a</i> fluorescence (ChlF) has been widely used to estimate plant photosynthesis and its regulatory mechanisms. The ratio of variable to maximum fluorescence, <i>F_v</i> /<i>F_m</i> , obtained from a ChlF induction curve, is commonly used to reflect the maximum photochemical quantum yield of photosystem II (PSII), but it is measured after a sample is dark-adapted for a long time, which limits its practical use. In this research, a least-squares support vector machine (LSSVM) model was developed to explore whether <i>F_v</i> /<i>F_m</i> can be determined from ChlF induction curves measured without dark adaptation. A total of 7,231 samples of 8 different experiments, under diverse conditions, were used to train the LSSVM model. Model evaluation with different samples showed excellent performance in determining <i>F_v</i> /<i>F_m</i> from ChlF signals without dark adaptation. Computation time for each test sample was less than 4 ms. Further, the prediction performance of test dataset was found to be very desirable: a high correlation coefficient (0.762 to 0.974); a low root mean squared error (0.005 to 0.021); and a residual prediction deviation of 1.254 to 4.933. These results clearly demonstrate that <i>F_v</i> /<i>F_m</i> , the widely used ChlF induction feature, can be determined from measurements without dark adaptation of samples. This will not only save experiment time but also make <i>F_v</i> /<i>F_m</i> useful in real-time and field applications. This work provides a high-throughput method to determine the important photosynthetic feature through ChlF for phenotyping plants.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0034"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10065787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9246683","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":"PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis.","authors":"Xinyu Dong,&nbsp;Qi Wang,&nbsp;Qianding Huang,&nbsp;Qinglong Ge,&nbsp;Kejun Zhao,&nbsp;Xingcai Wu,&nbsp;Xue Wu,&nbsp;Liang Lei,&nbsp;Gefei Hao","doi":"10.34133/plantphenomics.0054","DOIUrl":"https://doi.org/10.34133/plantphenomics.0054","url":null,"abstract":"<p><p>Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant diseases is critical to agricultural production. Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved plant disease detection and diagnosis for precision agriculture. In the meantime, most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves. However, the commonly used pre-trained models are from the computer vision dataset, not the botany dataset, which barely provides the pre-trained models sufficient domain knowledge about plant disease. Furthermore, this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision. To address this issue, we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis. In addition, we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification, plant disease detection, plant disease segmentation, and other subtasks. The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time, thereby supporting the better diagnosis of plant diseases. In addition, our pre-trained models will be open-sourced at https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0054"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10194370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9556435","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":"BreedingEIS: An Efficient Evaluation Information System for Crop Breeding.","authors":"Kaijie Qi,&nbsp;Xiao Wu,&nbsp;Chao Gu,&nbsp;Zhihua Xie,&nbsp;Shutian Tao,&nbsp;Shaoling Zhang","doi":"10.34133/plantphenomics.0029","DOIUrl":"https://doi.org/10.34133/plantphenomics.0029","url":null,"abstract":"<p><p>Crop breeding programs generate large datasets. Thus, it is difficult to ensure the accuracy and integrity of all the collected data in the breeding process. To improve breeding efficiency, we established an open source and free breeding evaluation information system (BreedingEIS). The full system is composed of a web client and a mobile client. The web client is used to name the individual breeding offspring plants and analyze data. The mobile client is based on the technology of widely used smartphones and is suitable for Android and iOS systems. Its functions focus on field evaluation, including quick response code recognition, evaluation data entry, and real-time viewing. In addition, near-field communication technology and portable label machines are introduced to enable breeders to quickly locate each individual plant and accurately label any samples collected from it. Generally, BreedingEIS enables users to accurately and conveniently register phenotypic data and quickly lock target individual plants from large volumes of data. The system provides a low-cost and highly efficient solution for crop information evaluation and enables breeders to better collect, manage, and use breeding data for decision making, which is a valuable resource for crop breeding.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0029"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9146786","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 Strategy for the Acquisition and Analysis of Image-Based Phenome in Rice during the Whole Growth Period.","authors":"Zhixin Tang,&nbsp;Zhuo Chen,&nbsp;Yuan Gao,&nbsp;Ruxian Xue,&nbsp;Zedong Geng,&nbsp;Qingyun Bu,&nbsp;Yanyan Wang,&nbsp;Xiaoqian Chen,&nbsp;Yuqiang Jiang,&nbsp;Fan Chen,&nbsp;Wanneng Yang,&nbsp;Weijuan Hu","doi":"10.34133/plantphenomics.0058","DOIUrl":"https://doi.org/10.34133/plantphenomics.0058","url":null,"abstract":"<p><p>As one of the most widely grown crops in the world, rice is not only a staple food but also a source of calorie intake for more than half of the world's population, occupying an important position in China's agricultural production. Thus, determining the inner potential connections between the genetic mechanisms and phenotypes of rice using dynamic analyses with high-throughput, nondestructive, and accurate methods based on high-throughput crop phenotyping facilities associated with rice genetics and breeding research is of vital importance. In this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice. Up to 84.8% of the phenotypic variance of the rice yield could be explained by these i-traits. A total of 285 putative quantitative trait loci (QTLs) were detected for the i-traits, and principal components analysis was applied on the basis of the i-traits in the temporal and organ dimensions, in combination with a genome-wide association study that also isolated QTLs. Moreover, the differences among the different population structures and breeding regions of rice with regard to its phenotypic traits demonstrated good environmental adaptability, and the crop growth and development model also showed high inosculation in terms of the breeding-region latitude. In summary, the strategy developed here for the acquisition and analysis of image-based rice phenomes can provide a new approach and a different thinking direction for the extraction and analysis of crop phenotypes across the whole growth period and can thus be useful for future genetic improvements in rice.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0058"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9617623","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}