Plant Methods最新文献

{"title":"An improved chilli pepper flower detection approach based on YOLOv8.","authors":"Zhi-Yong Wang, Cui-Ping Zhang","doi":"10.1186/s13007-025-01390-9","DOIUrl":"10.1186/s13007-025-01390-9","url":null,"abstract":"<p><p>Artificial pollination can considerably improve pollination success and boost chilli pepper fruit set and quality when grown in enclosed environments (e.g., greenhouses). Artificial pollination, on the other hand, raises production costs while also necessitating specific operating abilities. The precise and efficient identification of pepper blossoms is a critical step in the development of robotic pollinators or pollination drones. In this paper, we propose a pepper flower detection method based on YOLOv8 that incorporates multi-scale, attention, and conditional information. To begin, the CBAM structure that incorporates edge information is integrated into Backbone to expand the feature extraction receptive field and facilitate the learning of long-distance dependency. The BERT model is then used to encode conditional information, which is integrated into the backbone via the ELAN layer to assist the training and inference processes. Finally, an improved MPDIoU is applied to increase detection accuracy while increasing flexibility. The experimental results show that the modification enhances the network depth and reduces the number of parameters from 4M to 2.85M, while improving the mean average accuracy (mAP) by 3.1% over the baseline approach. The study's findings can help in crop object detection. The chilli pepper flower dataset: https://drive.google.com/file/d/1cKNie_iAzx-K4iPLQRVdyiOKV1d9zHrF/view?usp=drive_link The source code is available in https://drive.google.com/drive/folders/1ubNnKu7PWYAdUXvbs4Z2OBAVcSAQ3WLd?usp=drive_link .</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"71"},"PeriodicalIF":4.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161292","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":"An advanced deep learning method for pepper diseases and pests detection.","authors":"Xuewei Wang, Jun Liu, Qian Chen","doi":"10.1186/s13007-025-01387-4","DOIUrl":"10.1186/s13007-025-01387-4","url":null,"abstract":"<p><p>Despite the significant progress in deep learning-based object detection, existing models struggle to perform optimally in complex agricultural environments. To address these challenges, this study introduces YOLO-Pepper, an enhanced model designed specifically for greenhouse pepper disease and pest detection, overcoming three key obstacles: small target recognition, multi-scale feature extraction under occlusion, and real-time processing demands. Built upon YOLOv10n, YOLO-Pepper incorporates four major innovations: (1) an Adaptive Multi-Scale Feature Extraction (AMSFE) module that improves feature capture through multi-branch convolutions; (2) a Dynamic Feature Pyramid Network (DFPN) enabling context-aware feature fusion; (3) a specialized Small Detection Head (SDH) tailored for minute targets; and (4) an Inner-CIoU loss function that enhances localization accuracy by 18% compared to standard CIoU. Evaluated on a diverse dataset of 8046 annotated images, YOLO-Pepper achieves state-of-the-art performance, with 94.26% mAP@0.5 at 115.26 FPS, marking an 11.88 percentage point improvement over YOLOv10n (82.38% mAP@0.5) while maintaining a lightweight structure (2.51 M parameters, 5.15 MB model size) optimized for edge deployment. Comparative experiments highlight YOLO-Pepper's superiority over nine benchmark models, particularly in detecting small and occluded targets. By addressing computational inefficiencies and refining small object detection capabilities, YOLO-Pepper provides robust technical support for intelligent agricultural monitoring systems, making it a highly effective tool for early disease detection and integrated pest management in commercial greenhouse operations.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"70"},"PeriodicalIF":4.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144151444","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":"Improved estimation of forage nitrogen in alpine grassland by integrating Sentinel-2 and SIF data.","authors":"Yongkang Zhang, Jinlong Gao, Dongmei Zhang, Tiangang Liang, Zhiwei Wang, Xuanfan Zhang, Zhanping Ma, Jinhuan Yang","doi":"10.1186/s13007-025-01389-2","DOIUrl":"10.1186/s13007-025-01389-2","url":null,"abstract":"<p><p>Nitrogen is an essential element for the growth and reproduction of vegetation in alpine grasslands and plays a vital role in determining the nutrient-carrying capacity of plants and maintaining the balance of forage nutrition supply and demand. In recent years, the widespread application of high-resolution multispectral satellites (i.e., Sentinel-2) equipped with multiple red-edge bands has proven an effective approach for estimating forage nitrogen content in alpine grassland habitats. In addition, solar-induced chlorophyll fluorescence (SIF), as a direct probe of vegetation photosynthesis, has become an effective indicator for estimating key growth parameters of green vegetation in recent years. However, it currently unknown whether integrating SIF and Sentinel-2 satellite data can further enhance the mapping accuracy of forage nitrogen content in alpine grassland. In this study, we integrates SIF products from TanSat and Orbiting Carbon Observatory-2 (OCO-2) satellites, Sentinel-2 Multi-Spectral Instrument (MSI) data with derived vegetation indices, and field observations across phenological stages (green-up stage, vigorous growth stage, and senescence stage) in northeastern Tibetan Plateau alpine grasslands to develop support vector machine (SVM), gaussian process regression (GPR), and artificial neural network (ANN) models for regional-scale forage nitrogen estimation. The results indicated that both the Sentinel-2 (V-R² of 0.68-0.71, CVRMSE of 17.73-18.65%) and SIF data (V-R² of 0.59-0.73, CVRMSE of 17.05-21.40%) individually yielded relatively accurate estimates of the forage nitrogen. The integrated model constructed using both spectral data types explained 69-74% of the variation in forage nitrogen content, with a CVRMSE ranging from 16.89 to 17.85%, which indicates that the synergy between Sentinel-2 and SIF data can slightly enhance the model's estimation capability of forage nitrogen content. Thus, integrating Sentinel-2 and SIF data presents a potential solution for precisely measuring spatial distribution of forage nitrogen in alpine grassland at the regional scale. The proposed method provides a feasible framework for the spatiotemporal prediction of the key forage growth parameters of forage and offers a theoretical basis for determining the rational utilization of grassland resources and studying the nutritional balance between grassland and livestock.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"69"},"PeriodicalIF":4.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132468","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":"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}