Plant Phenomics最新文献

{"title":"Multispectral Phenotyping and Genetic Analyses of Spring Appearance in Greening Plant, <i>Phedimus</i> spp.","authors":"Taeko Koji,&nbsp;Hiroyoshi Iwata,&nbsp;Motoyuki Ishimori,&nbsp;Hideki Takanashi,&nbsp;Yuji Yamasaki,&nbsp;Hisashi Tsujimoto","doi":"10.34133/plantphenomics.0063","DOIUrl":"https://doi.org/10.34133/plantphenomics.0063","url":null,"abstract":"<p><p>The change in appearance during the seasonal transitions in ornamental greening plants is an important characteristic. In particular, the early onset of green leaf color is a desirable trait for a cultivar. In this study, we established a method for phenotyping leaf color change by multispectral imaging and performed genetic analysis based on the phenotypes to clarify the potential of the approach in breeding greening plants. We performed multispectral phenotyping and quantitative trait locus (QTL) analysis of an F₁ population derived from 2 parental lines of <i>Phedimus takesimensis</i>, known to be a drought and heat-tolerant rooftop plant species. The imaging was conducted in April of 2019 and 2020 when dormancy breakage occurs and growth extension begins. Principal component analysis of 9 different wavelength values showed a high contribution from the first principal component (PC1), which captured variation in the visible light range. The high interannual correlation in PC1 and in the intensity of visible light indicated that the multispectral phenotyping captured genetic variation in the color of leaves. We also performed restriction site-associated DNA sequencing and obtained the first genetic linkage map of <i>Phedimus</i> spp. QTL analysis revealed 2 QTLs related to early dormancy breakage. Based on the genotypes of the markers underlying these 2 QTLs, the F₁ phenotypes with early (late) dormancy break, green (red or brown) leaves, and a high (low) degree of vegetative growth were classified. The results suggest the potential of multispectral phenotyping in the genetic dissection of seasonal leaf color changes in greening plants.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0063"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10292581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9734419","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":"Quantifying Contributions of Different Factors to Canopy Photosynthesis in 2 Maize Varieties: Development of a Novel 3D Canopy Modeling Pipeline.","authors":"Qingfeng Song,&nbsp;Fusang Liu,&nbsp;Hongyi Bu,&nbsp;Xin-Guang Zhu","doi":"10.34133/plantphenomics.0075","DOIUrl":"https://doi.org/10.34133/plantphenomics.0075","url":null,"abstract":"<p><p>Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate (<i>A_c</i>) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in <i>A_c</i> between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in <i>A_c</i> (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with <i>A_c</i> and plant architecture traits were non-linearly related to <i>A_c</i>. This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0075"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10371248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10050100","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 Tea Buds Counting Method Based on YOLOv5 and Kalman Filter Tracking Algorithm.","authors":"Yang Li,&nbsp;Rong Ma,&nbsp;Rentian Zhang,&nbsp;Yifan Cheng,&nbsp;Chunwang Dong","doi":"10.34133/plantphenomics.0030","DOIUrl":"https://doi.org/10.34133/plantphenomics.0030","url":null,"abstract":"<p><p>The tea yield estimation provides information support for the harvest time and amount and serves as a decision-making basis for farmer management and picking. However, the manual counting of tea buds is troublesome and inefficient. To improve the efficiency of tea yield estimation, this study presents a deep-learning-based approach for efficiently estimating tea yield by counting tea buds in the field using an enhanced YOLOv5 model with the Squeeze and Excitation Network. This method combines the Hungarian matching and Kalman filtering algorithms to achieve accurate and reliable tea bud counting. The effectiveness of the proposed model was demonstrated by its mean average precision of 91.88% on the test dataset, indicating that it is highly accurate at detecting tea buds. The model application to the tea bud counting trials reveals that the counting results from test videos are highly correlated with the manual counting results (<i>R</i> ² = 0.98), indicating that the counting method has high accuracy and effectiveness. In conclusion, the proposed method can realize tea bud detection and counting in natural light and provides data and technical support for rapid tea bud acquisition.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0030"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9616849","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":"Wearable Sensor: An Emerging Data Collection Tool for Plant Phenotyping.","authors":"Cheng Zhang,&nbsp;Jingjing Kong,&nbsp;Daosheng Wu,&nbsp;Zhiyong Guan,&nbsp;Baoqing Ding,&nbsp;Fadi Chen","doi":"10.34133/plantphenomics.0051","DOIUrl":"https://doi.org/10.34133/plantphenomics.0051","url":null,"abstract":"<p><p>The advancement of plant phenomics by using optical imaging-based phenotyping techniques has markedly improved breeding and crop management. However, there remains a challenge in increasing the spatial resolution and accuracy due to their noncontact measurement mode. Wearable sensors, an emerging data collection tool, present a promising solution to address these challenges. By using a contact measurement mode, wearable sensors enable in-situ monitoring of plant phenotypes and their surrounding environments. Although a few pioneering works have been reported in monitoring plant growth and microclimate, the utilization of wearable sensors in plant phenotyping has yet reach its full potential. This review aims to systematically examine the progress of wearable sensors in monitoring plant phenotypes and the environment from an interdisciplinary perspective, including materials science, signal communication, manufacturing technology, and plant physiology. Additionally, this review discusses the challenges and future directions of wearable sensors in the field of plant phenotyping.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0051"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10180408","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":"Phenotyping Key Fruit Quality Traits in Olive Using RGB Images and Back Propagation Neural Networks.","authors":"Giuseppe Montanaro,&nbsp;Angelo Petrozza,&nbsp;Laura Rustioni,&nbsp;Francesco Cellini,&nbsp;Vitale Nuzzo","doi":"10.34133/plantphenomics.0061","DOIUrl":"https://doi.org/10.34133/plantphenomics.0061","url":null,"abstract":"<p><p>To predict oil and phenol concentrations in olive fruit, the combination of back propagation neural networks (BPNNs) and contact-less plant phenotyping techniques was employed to retrieve RGB image-based digital proxies of oil and phenol concentrations. Fruits of cultivars (×3) differing in ripening time were sampled (~10-day interval, ×2 years), pictured and analyzed for phenol and oil concentrations. Prior to this, fruit samples were pictured and images were segmented to extract the red (R), green (G), and blue (B) mean pixel values that were rearranged in 35 RGB-based colorimetric indexes. Three BPNNs were designed using as input variables (a) the original 35 RGB indexes, (b) the scores of principal components after a principal component analysis (PCA) pre-processing of those indexes, and (c) a reduced number (28) of the RGB indexes achieved after a sparse PCA. The results show that the predictions reached the highest mean <i>R</i>² values ranging from 0.87 to 0.95 (oil) and from 0.81 to 0.90 (phenols) across the BPNNs. In addition to the <i>R</i>², other performance metrics were calculated (root mean squared error and mean absolute error) and combined into a general performance indicator (GPI). The resulting rank of the GPI suggests that a BPNN with a specific topology might be designed for cultivars grouped according to their ripening period. The present study documented that an RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the developing olive sector within a digital agriculture domain.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0061"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9708703","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":"The Importance of Using Realistic 3D Canopy Models to Calculate Light Interception in the Field.","authors":"Shunfu Xiao,&nbsp;Shuaipeng Fei,&nbsp;Qing Li,&nbsp;Bingyu Zhang,&nbsp;Haochong Chen,&nbsp;Demin Xu,&nbsp;Zhibo Cai,&nbsp;Kaiyi Bi,&nbsp;Yan Guo,&nbsp;Baoguo Li,&nbsp;Zhen Chen,&nbsp;Yuntao Ma","doi":"10.34133/plantphenomics.0082","DOIUrl":"https://doi.org/10.34133/plantphenomics.0082","url":null,"abstract":"<p><p>Quantifying canopy light interception provides insight into the effects of plant spacing, canopy structure, and leaf orientation on radiation distribution. This is essential for increasing crop yield and improving product quality. Canopy light interception can be quantified using 3-dimensional (3D) plant models and optical simulations. However, virtual 3D canopy models (VCMs) have often been used to quantify canopy light interception because realistic 3D canopy models (RCMs) are difficult to obtain in the field. This study aims to compare the differences in light interception between VCMs and RCM. A realistic 3D maize canopy model (RCM) was reconstructed over a large area of the field using an advanced unmanned aerial vehicle cross-circling oblique (CCO) route and the structure from motion-multi-view stereo method. Three types of VCMs (VCM-1, VCM-4, and VCM-8) were then created by replicating 1, 4, and 8 individual realistic plants constructed by CCO in the center of the corresponding RCM. The daily light interception per unit area (DLI), as computed for the 3 VCMs, exhibited marked deviation from the RCM, as evinced by the relative root mean square error (rRMSE) values of 20.22%, 17.38%, and 15.48%, respectively. Although this difference decreased as the number of plants used to replicate the virtual canopy increased, rRMSE of DLI for VCM-8 and RCM still reached 15.48%. It was also found that the difference in light interception between RCMs and VCMs was substantially smaller in the early stage (48 days after sowing [DAS]) than in the late stage (70 DAS). This study highlights the importance of using RCM when calculating light interception in the field, especially in the later growth stages of plants.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0082"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10437493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10051133","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":"To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status?","authors":"Alexis Carlier,&nbsp;Sebastien Dandrifosse,&nbsp;Benjamin Dumont,&nbsp;Benoît Mercatoris","doi":"10.34133/plantphenomics.0083","DOIUrl":"https://doi.org/10.34133/plantphenomics.0083","url":null,"abstract":"<p><p>The utilization of high-throughput in-field phenotyping systems presents new opportunities for evaluating crop stress. However, existing studies have primarily focused on individual stresses, overlooking the fact that crops in field conditions frequently encounter multiple stresses, which can display similar symptoms or interfere with the detection of other stress factors. Therefore, this study aimed to investigate the impact of wheat yellow rust on reflectance measurements and nitrogen status assessment. A multi-sensor mobile platform was utilized to capture RGB and multispectral images throughout a 2-year fertilization-fungicide trial. To identify disease-induced damage, the SegVeg approach, which combines a U-NET architecture and a pixel-wise classifier, was applied to RGB images, generating a mask capable of distinguishing between healthy and damaged areas of the leaves. The observed proportion of damage in the images demonstrated similar effectiveness to visual scoring methods in explaining grain yield. Furthermore, the study discovered that the disease not only affected reflectance through leaf damage but also influenced the reflectance of healthy areas by disrupting the overall nitrogen status of the plants. This emphasizes the importance of incorporating disease impact into reflectance-based decision support tools to account for its effects on spectral data. This effect was successfully mitigated by employing the NDRE vegetation index calculated exclusively from the healthy portions of the leaves or by incorporating the proportion of damage into the model. However, these findings also highlight the necessity for further research specifically addressing the challenges presented by multiple stresses in crop phenotyping.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0083"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10211318","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":"Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform.","authors":"Yinglun Li,&nbsp;Weiliang Wen,&nbsp;Jiangchuan Fan,&nbsp;Wenbo Gou,&nbsp;Shenghao Gu,&nbsp;Xianju Lu,&nbsp;Zetao Yu,&nbsp;Xiaodong Wang,&nbsp;Xinyu Guo","doi":"10.34133/plantphenomics.0043","DOIUrl":"https://doi.org/10.34133/plantphenomics.0043","url":null,"abstract":"<p><p>The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements (<i>R</i>² = 0.98), and the accuracy was higher than only using one source point cloud data (<i>R</i>² = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0043"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9569974","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":"Combining High-Resolution Imaging, Deep Learning, and Dynamic Modeling to Separate Disease and Senescence in Wheat Canopies.","authors":"Jonas Anderegg,&nbsp;Radek Zenkl,&nbsp;Achim Walter,&nbsp;Andreas Hund,&nbsp;Bruce A McDonald","doi":"10.34133/plantphenomics.0053","DOIUrl":"https://doi.org/10.34133/plantphenomics.0053","url":null,"abstract":"<p><p>Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling. Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions. Besides direct effects on green leaf area in terms of leaf damage, stressors often anticipate or accelerate physiological senescence, which may multiply their negative impact on grain filling. Here, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation. A vegetation segmentation model was trained using semisynthetic training data generated using image composition and generative adversarial neural networks, which greatly reduced the risk of annotation uncertainties and annotation effort. Application of the models to image time series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis. Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations (<i>r</i> ≈ 0.9). Contrasting patterns were observed for plots with different levels of foliar diseases, particularly septoria tritici blotch. Our results suggest that tracking the chlorotic and necrotic fractions separately may enable (a) a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and (b) investigation of interactions between biotic stress and physiological senescence. The high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0053"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10101012","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":"From Laboratory to Field: Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild.","authors":"Xinlu Wu,&nbsp;Xijian Fan,&nbsp;Peng Luo,&nbsp;Sruti Das Choudhury,&nbsp;Tardi Tjahjadi,&nbsp;Chunhua Hu","doi":"10.34133/plantphenomics.0038","DOIUrl":"https://doi.org/10.34133/plantphenomics.0038","url":null,"abstract":"<p><p>Plant disease recognition is of vital importance to monitor plant development and predicting crop production. However, due to data degradation caused by different conditions of image acquisition, e.g., laboratory vs. field environment, machine learning-based recognition models generated within a specific dataset (source domain) tend to lose their validity when generalized to a novel dataset (target domain). To this end, domain adaptation methods can be leveraged for the recognition by learning invariant representations across domains. In this paper, we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization, namely, Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification (MSUN). Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial training. Specifically, MSUN comprises multirepresentation, subdomain adaptation modules and auxiliary uncertainty regularization. The multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source domain. This effectively alleviates the problem of large interdomain discrepancy. Subdomain adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass variation. Finally, the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain transfer. MSUN was experimentally validated to achieve optimal results on the PlantDoc, Plant-Pathology, Corn-Leaf-Diseases, and Tomato-Leaf-Diseases datasets, with accuracies of 56.06%, 72.31%, 96.78%, and 50.58%, respectively, surpassing other state-of-the-art domain adaptation techniques considerably.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0038"},"PeriodicalIF":6.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10059679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9616851","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}