Plant Methods最新文献

{"title":"A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAV.","authors":"Qiangjia Wu, Meixiang Chen, Hao Shi, Tongchuan Yi, Gang Xu, Weijia Wang, Ruirui Zhang","doi":"10.1186/s13007-025-01385-6","DOIUrl":"10.1186/s13007-025-01385-6","url":null,"abstract":"<p><p>Pine wood nematode (PWN), a major international quarantined forest pest, has resulted in significant loss to the pine forest resources, posing a serious threat to global forest ecosystems. Quick and accurate identification of trees infected by PWN can lead to earlier intervention in their spread, thereby significantly reducing losses. However, there is a scarcity of algorithm that are both swift and precise. To achieve more rapid and precise segmentation of trees affected by PWN, we proposed a novel lightweight model termed Refined and Deformable Carafe Attention Net (RCANet). The RCANet excels in both accuracy and real-time performance. It has achieved segmentation accuracy that surpasses mainstream segmentation networks, including DeepLabv3 + , Segformer, PSPNet, HrNet, and UNet. The number of parameters in RCANet is only 5.373 million, the segmentation speed reached 83.14 fps. Compared to the baseline model UNet, the IoU of the affected trees class is improved by 5.6%, and the segmentation speed is accelerated by about 90%. A straightforward yet highly effective lightweight structure was proposed, termed Refined VGG. Additionally, we validate the efficacy of several network modules for this task. RCANet addressed the challenges of low accuracy and inadequate real-time capabilities in the identification of PWN-affected pine trees within intricate forest landscapes. which is expected to be deployed on UAVs in the future for real-time recognition to accelerate the identification and localization of affected trees. This work could facilitate the management of PWN.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"68"},"PeriodicalIF":4.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid quantification of whole seed fatty acid amount, composition, and shape phenotypes from diverse oilseed species with large differences in seed size.","authors":"Matthew G Garneau, Prasad Parchuri, Nora Zander, Philip D Bates","doi":"10.1186/s13007-025-01388-3","DOIUrl":"10.1186/s13007-025-01388-3","url":null,"abstract":"<p><strong>Background: </strong>Seed oils are widely used in the food, biofuel, and industrial feedstock industries, with their utility and value determined by total oil content and fatty acid composition. Current high throughput seed oil analysis methods either lack accuracy in total fatty acid profiling or require extensive labor for lipid extraction prior to derivatization to fatty acid methyl esters (FAME) and quantification by gas chromatography (GC). Alternatively, direct whole seed FAME production methods have been developed for the very small seeds in the model species Arabidopsis thaliana but these have generally not been adapted to larger seeds of most oilseed crops.</p><p><strong>Results: </strong>High-throughput direct whole seed FAME production methods were optimized for seeds up to 5 mg each utilizing acid-catalyzed esterification. For the oilseed species Camelina sativa, Thlaspi avernse (pennycress), Cuphea viscosissima, and Brassica napus (var. Canola), the total seed fatty acid content and composition from direct seed esterification to FAME matched that of lipid extract derivatization demonstrating the accuracy of the methods. In combination with seed phenotyping using GridFree, this approach enabled the development of a rapid pipeline for simultaneous seed weight, count, size/shape phenotyping, and oil analysis. For the larger and tougher seeds produced by Limnanthes alba (Meadowfoam) and Cannabis sativa L. (hemp) the whole seed acid-based method proved insufficient, and prior laborious homogenization of seeds was required. Therefore, a rapid one-tube bead homogenization and base catalyzed-esterification method was developed. Base-derived fatty acid esterification cannot derivatize free fatty acids leading to slightly lower total seed fatty acid than acid-catalyzed methods, however the seed oil content and fatty acid composition that is valuable for screening large numbers of samples in research populations was accurately measured.</p><p><strong>Conclusions: </strong>New rapid whole seed fatty acid esterification and phenotyping protocols were developed to accurately assess oilseed lipid content. These methods are particularly valuable in oilseed research, breeding, and engineering applications where efficient analysis of large numbers of samples and accurate oil fatty acid profiling is essential. While having been developed for current and emerging oilseed crops, these methods also provide a foundation from which protocols might be established for new and emerging crop species.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"67"},"PeriodicalIF":4.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12096552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144128310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maui: modular analytics of UAS imagery for specialty crop research.","authors":"Kathleen Kanaley, Maylin J Murdock, Tian Qiu, Ertai Liu, Schuyler E Seyram, Dominik Starzmann, Lawrence B Smart, Kaitlin M Gold, Yu Jiang","doi":"10.1186/s13007-025-01376-7","DOIUrl":"10.1186/s13007-025-01376-7","url":null,"abstract":"<p><strong>Background: </strong>Imaging sensors (e.g., multispectral cameras) mounted on unmanned aerial systems (UAS) have emerged as a powerful tool for deriving insights about agricultural fields, from plant morphology phenotyping to plant disease monitoring. Advances in computer vision-based image analysis have enabled researchers to rapidly and accurately isolate crop spectra in UAS images. Specialty crops often employ unique production styles, such as trellising or inter-cropping. This presents a barrier to using existing image processing methodologies developed for broad-acre, row cropped systems (i.e. corn, wheat, soybean). Here, we present MAUI, a customizable image processing workflow built for specialty crops. Using a pathology research vineyard and hemp breeding trial as test cases, MAUI streamlines the generation of multispectral orthomosaic time-series, the segmentation of crops at the unit of research interest, and the extraction of crop spectra for downstream analysis.</p><p><strong>Results: </strong>We successfully used MAUI to collect and analyze UAS data at two field sites over two growing seasons. Of the five canopy segmentation methods we tested, a supervised deep convolutional neural network (DeepLabv3) and a vision foundation model (SAM) produced the most accurate crop masks for the vineyard and hemp images, with mean intersection over union (mIoU) values of 0.85 and 0.95, respectively. Segmentation accuracy decreased when we applied each method to the other dataset, highlighting the importance of modular, flexible segmentation workflows for UAS imaging analysis in specialty crops.</p><p><strong>Conclusion: </strong>We present a modular framework to efficiently extract spectral data for specialty crops from UAS imagery. We highlight two kinds of segmentation applied to trellised and row cropping systems to demonstrate the modularity and versatility of the proposed methodology. MAUI improved spectral discrimination between individual plants and treatment groups for hemp and grapevine, respectively. With the containerized deployment package and open-source codebase, MAUI can be widely adopted by specialty crop researchers to facilitate the integration of UAS imagery analysis into routine research.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"65"},"PeriodicalIF":4.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12090642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping of cotton bolls and branches with high-granularity through point cloud segmentation.","authors":"Lizhi Jiang, Javier Rodriguez-Sanchez, John L Snider, Peng W Chee, Longsheng Fu, Changying Li","doi":"10.1186/s13007-025-01375-8","DOIUrl":"10.1186/s13007-025-01375-8","url":null,"abstract":"<p><p>High resolution three-dimensional (3D) point clouds enable the mapping of cotton boll spatial distribution, aiding breeders in better understanding the correlation between boll positions on branches and overall yield and fiber quality. This study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants. The data processing workflow includes two independent approaches to map the vertical and horizontal distribution of cotton bolls. The vertical distribution was mapped by segmenting bolls using PointNet++ and identifying individual instances through Euclidean clustering. For horizontal distribution, TreeQSM segmented the plant into the main stem and individual branches. PointNet++ and Euclidean clustering were then used to achieve cotton boll instance segmentation. The horizontal distribution was determined by calculating the Euclidean distance of each cotton boll relative to the main stem. Additionally, branch types were classified using point cloud meshing completion and the Dijkstra shortest path algorithm. The results highlight that the accuracy and mean intersection over union (mIoU) of the 2-class segmentation based on PointNet++ reached 0.954 and 0.896 on the whole plant dataset, and 0.968 and 0.897 on the branch dataset, respectively. The coefficient of determination (R²) for the boll counting was 0.99 with a root mean squared error (RMSE) of 5.4. For the first time, this study accomplished high-granularity spatial mapping of cotton bolls and branches, but directly predicting fiber quality from 3D point clouds remains a challenge. This method provides a promising tool for 3D cotton plant mapping of different genotypes, which potentially could accelerate plant physiological studies and breeding programs.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"66"},"PeriodicalIF":4.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12090505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coconut tree modeling based on abiotic factors and modified cosserat rod theory.","authors":"Sakthiprasad Kuttankulangara Manoharan, Rajesh Kannan Megalingam","doi":"10.1186/s13007-025-01379-4","DOIUrl":"10.1186/s13007-025-01379-4","url":null,"abstract":"<p><p>The biomechanics of growing trees, particularly coconut trees, are intricate due to various abiotic factors such as sunlight, wind, gravitropism, and cultivation practices. Existing structural growth models fail to capture the unique characteristics of coconut trees, which lack branches and have large crown leaves. This research introduces a novel coconut tree modeling approach, integrating abiotic factors and modified Cosserat rod theory. Factors like sunlight availability, wind speed, cultivation practices, and gravitropism influence coconut tree growth rates. The model encompasses both primary and secondary growth processes. Primary growth is influenced by gravitropism, sunlight availability, and wind effects, while secondary growth is determined by variations in trunk diameter. Additionally, the model incorporates the diameter at breast height to accommodate cultivation practice variations. Comparisons between the proposed model, classical rod theory, and biomechanics growth models reveal that the proposed model aligns more closely with real-time data on spatial and temporal growth characteristics. This research marks the first attempt to model coconut tree growth considering abiotic factors comprehensively. In summary, this study presents a pioneering coconut tree growth model that integrates abiotic factors and modified Cosserat rod theory. By considering unique features of coconut trees and environmental influences, the model offers more accurate predictions compared to existing approaches, enhancing our understanding of coconut tree biomechanics and growth patterns. Coconut tree modeling has diverse applications in precision agriculture, automated harvesting, tree health monitoring, climate change analysis, urban planning, and the biomass industry, helping optimize yield, resource management, and sustainability. It also plays a crucial role in genetic research, disaster preparedness, and risk assessment, enabling advancements in robotics, environmental conservation, and industrial applications for improved productivity and resilience.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"64"},"PeriodicalIF":4.7,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12085861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RP-DETR: end-to-end rice pests detection using a transformer.","authors":"Jinsheng Wang, Tao Wang, Qin Xu, Lu Gao, Guosong Gu, Liangquan Jia, Chong Yao","doi":"10.1186/s13007-025-01381-w","DOIUrl":"10.1186/s13007-025-01381-w","url":null,"abstract":"<p><p>Pest infestations in rice crops greatly affect yield and quality, making early detection essential. As most rice pests affect leaves and rhizomes, visual inspection of rice for pests is becoming increasingly important. In precision agriculture, fast and accurate automatic pest identification is essential. To tackle this issue, multiple models utilizing computer vision and deep learning have been applied. Owing to its high efficiency, deep learning is now the favored approach for detecting plant pests. In this regard, the paper introduces an effective rice pest detection framework utilizing the Transformer architecture, designed to capture long-range features. The paper enhances the original model by adding the self-developed RepPConv-block to reduce the problem of information redundancy in feature extraction in the model backbone and to a certain extent reduce the model parameters. The original model's CCFM structure is enhanced by integrating the Gold-YOLO neck, improving its ability to fuse multi-scale features. Additionally, the MPDIoU-based loss function enhances the model's detection performance. Using the self-constructed high-quality rice pest dataset, the model achieves higher identification accuracy while reducing the number of parameters. The proposed RP18-DETR and RP34-DETR models reduce parameters by 16.5% and 25.8%, respectively, compared to the original RT18-DETR and RT34-DETR models. With a threshold of 0.5, the average accuracy calculated is 1.2% higher for RP18-DETR than for RT18-DETR.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"63"},"PeriodicalIF":4.7,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based identification of resistance genes associated with sunflower broomrape.","authors":"Yingxue Che, Congzi Zhang, Jixiang Xing, Qilemuge Xi, Ying Shao, Lingmin Zhao, Shuchun Guo, Yongchun Zuo","doi":"10.1186/s13007-025-01383-8","DOIUrl":"10.1186/s13007-025-01383-8","url":null,"abstract":"<p><strong>Background: </strong>Sunflowers (Helianthus annuus L.), a vital oil crop, are facing a severe challenge from broomrape (Orobanche cumana), a parasitic plant that seriously jeopardizes the growth and development of sunflowers, limits global production and leads to substantial economic losses, which urges the development of resistant sunflower varieties.</p><p><strong>Results: </strong>This study aims to identify resistance genes from a comprehensive transcriptomic profile of 103 sunflower varieties based on gene expression data and then constructs predictive models with the key resistant genes. The least absolute shrinkage and selection operator (LASSO) regression and random forest feature importance ranking method were used to identify resistance genes. These genes were considered as biomarkers in constructing machine learning models with Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Logistic Regression (LR), and Gaussian Naive Bayes (GaussianNB). The SVM model constructed with the 24 key genes selected by the LASSO method demonstrated high classification accuracy (0.9514) and a robust AUC value (0.9865), effectively distinguishing between resistant and susceptible varieties based on gene expression data. Furthermore, we discovered a correlation between key genes and differential metabolites, particularly jasmonic acid (JA).</p><p><strong>Conclusion: </strong>Our study highlights a novel perspective on screening sunflower varieties for broomrape resistance, which is anticipated to guide future biological research and breeding strategies.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"62"},"PeriodicalIF":4.7,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SSR_VibraProfiler: a Python package for accurate classification of varieties using SSRs with intra-variety specificity and inter-variety polymorphism.","authors":"Chenhao Jiang, Chuan Dong, Zhenzhen Wu, Chenyi Shi, Qiannan Ye, Xiaopei Wu, Siyi Ma, Yuming Wen, Guoping Yu, Jiasheng Wu, Chengjun Zhang","doi":"10.1186/s13007-025-01380-x","DOIUrl":"10.1186/s13007-025-01380-x","url":null,"abstract":"<p><strong>Background: </strong>Simple sequence repeats (SSRs) are widely used as molecular markers; however, traditional development of SSR molecular markers heavily relies on experimental methods. The advancement of modern sequencing technology has provided the possibility of directly extracting SSR characteristics from sequencing data and using them for variety identification.</p><p><strong>Results: </strong>We have developed a computational framework for variety identification, treating the presence or absence of each SSR in sequencing data as a numerical characteristic while ignoring specific loci, flanking sequences, and occurrence counts. Therefore, subsequent variety identification does not rely on experimental validation but is directly performed based on the numerical characteristic matrix. Using a formula, we measure the variance of these numerical characteristics both within and among varieties, and select SSRs that exhibit intra-variety specificity and inter-variety polymorphism, forming a 0,1 matrix. We use t-SNE (t-distributed Stochastic Neighbor Embedding) to project the matrix onto a two-dimensional plane, followed by K-means clustering of the individuals. The classification performance of the matrix is preliminarily assessed by comparing the cluster labels with the true labels, providing an initial evaluation of its effectiveness in variety detection. Ultimately, we construct a recognition model based on the SSRs matrix and apply it for variety identification. The process has been encapsulated into the package SSR_VibraProfiler, which can serve as a tool for constructing an SSR variety DNA fingerprint database. We tested this package on a Rhododendron dataset that included 40 individuals from 8 varieties. The accuracy achieved through t-SNE dimensionality reduction and K-means clustering was 100%. Furthermore, we used the leave-one-out method to validate the accuracy of our method in predicting variety, and confirmed the reliability of our method in detecting varieties. The package is freely available at https://github.com/Olcat35412/SSR_VibraProfiler .</p><p><strong>Conclusion: </strong>We introduced SSR_VibraProfiler, a Python package for distinguishing and predicting individual varieties without a reference genome by extracting SSR numerical characteristics from next-generation sequencing data. This tool will contribute to the development, identification, and protection of new varieties.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"61"},"PeriodicalIF":4.7,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plant recognition of maize seedling stage in UAV remote sensing images based on H-RT-DETR.","authors":"Yunlong Wu, Shouqi Yuan, Lingdi Tang","doi":"10.1186/s13007-025-01382-9","DOIUrl":"https://doi.org/10.1186/s13007-025-01382-9","url":null,"abstract":"<p><p>The real-time monitoring and counting of maize seed germination at seedling stage is of great significance for seed quality detection, field management and yield estimation. Traditional manual monitoring and counting is very time-consuming, cumbersome and error-prone. In order to quickly and accurately identify and count maize seedlings in a complex field environment, this study proposes an end-to-end maize seedling plant detection model H-RT-DETR (Hierarchical-Real-Time DEtection TRansformer) based on hierarchical feature extraction and RT-DETR (Real-Time DEtection TRansformer). H-RT-DETR uses Hierarchical Feature Representation and Efficient Self-Attention as the backbone network for feature extraction, thereby improving the network's ability to extract features of maize seedling stage in UAV remote sensing images. Through experiments on the UAV remote sensing data set of maize seedling stage, the mean Average Precision mAP0.5-0.95, mAP0.5 and mAP0.75 of the improved H-RT-DETR model reached 51.2%, 94.7% and 48.1%, respectively, and the Average Recall (AR) reached 68.5%. In order to verify the efficiency of the proposed method, H-RT-DETR is compared with the widely used and advanced target recognition methods. The results show that the detection accuracy of H-RT-DETR is better than that of the comparison methods. In terms of detection speed, the H-RT-DETR model does not require Non-Maximum Suppression (NMS) post-processing operations, the Frames Per Second (FPS) on the test dataset reaches 84f/s, which is 19,12,11 and 21 higher than that of YOLOv5, YOLOv7, YOLOv8 and YOLOX, respectively, under the same hardware environment. This model can provide technical support for real-time detection of maize seedlings under UAV remote sensing images in terms of both detection accuracy and speed (see https://github.com/wylSUGAR/H-RT-DETR for model implementation and results).</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"60"},"PeriodicalIF":4.7,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12079949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144079512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Two-fold red excess (TREx): a simple and novel digital color index that enables non-invasive real-time monitoring of green-leaved as well as anthocyanin-rich crops.","authors":"Avinash Agarwal, Filipe de Jesus Colwell, Viviana Andrea Correa Galvis, Tom R Hill, Neil Boonham, Ankush Prashar","doi":"10.1186/s13007-025-01370-z","DOIUrl":"https://doi.org/10.1186/s13007-025-01370-z","url":null,"abstract":"","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"59"},"PeriodicalIF":4.7,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063452/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143994070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}