Plant Methods最新文献

{"title":"A high-throughput approach for quantifying turgor loss point in grapevine.","authors":"Adam R Martin, Guangrui Li, Boya Cui, Rachel O Mariani, Kale Vicario, Kimberley A Cathline, Allison Findlay, Gavin Robertson","doi":"10.1186/s13007-024-01304-1","DOIUrl":"10.1186/s13007-024-01304-1","url":null,"abstract":"<p><p>Quantifying drought tolerance in crops is critical for agriculture management under environmental change, and drought response traits in grape vine have long been the focus of viticultural research. Turgor loss point (π_tlp) is gaining attention as an indicator of drought tolerance in plants, though estimating π_tlp often requires the construction and analysis of pressure-volume (P-V) curves which are very time consuming. While P-V curves remain a valuable tool for assessing π_tlp and related traits, there is considerable interest in developing high-throughput methods for rapidly estimating π_tlp, especially in the context of crop screening. We tested the ability of a dewpoint hygrometer to quantify variation in π_tlp across and within 12 clones of grape vine (Vitis vinifera subsp. vinifera) and one wild relative (Vitis riparia), and compared these results to those derived from P-V curves. At the leaf-level, methodology explained only 4-5% of the variation in π_tlp while clone/species identity accounted for 39% of the variation, indicating that both methods are sensitive to detecting intraspecific π_tlp variation in grape vine. Also at the leaf level, π_tlp measured using a dewpoint hygrometer approximated π_tlp values (r² = 0.254) and conserved π_tlp rankings from P-V curves (Spearman's ρ = 0.459). While the leaf-level datasets differed statistically from one another (paired t-test p = 0.01), average difference in π_tlp for a given pair of leaves was small (0.1 ± 0.2 MPa (s.d.)). At the species/clone level, estimates of π_tlp measured by the two methods were also statistically correlated (r² = 0.304), did not deviate statistically from a 1:1 relationship, and conserved π_tlp rankings across clones (Spearman's ρ = 0.692). The dewpoint hygrometer (taking ∼ 10-15 min on average per measurement) captures fine-scale intraspecific variation in π_tlp, with results that approximate those from P-V curves (taking 2-3 h on average per measurement). The dewpoint hygrometer represents a viable method for rapidly estimating intraspecific variation in π_tlp, and potentially greatly increasing replication when estimating this drought tolerance trait in grape vine and other crops.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"180"},"PeriodicalIF":4.7,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11587569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710909","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":"Microcontroller-based water control system for evaluating crop water use characteristics.","authors":"Daisuke Sugiura, Shiro Mitsuya, Hirokazu Takahashi, Ryo Yamamoto, Yoshiyuki Miyazawa","doi":"10.1186/s13007-024-01305-0","DOIUrl":"10.1186/s13007-024-01305-0","url":null,"abstract":"<p><strong>Background: </strong>Climate change and the growing demand for agricultural water threaten global food security. Understanding water use characteristics of major crops from leaf to field scale is critical, particularly for identifying crop varieties with enhanced water-use efficiency (WUE) and stress tolerance. Traditional methods to assess WUE are either by gas exchange measurements at the leaf level or labor-intensive manual pot weighing at the whole-plant level, both of which have limited throughput.</p><p><strong>Results: </strong>Here, we developed a microcontroller-based low-cost system that integrates pot weighing, automated water supply, and real-time monitoring of plant water consumption via Wi-Fi. We validated the system using major crops (rice soybean, maize) under diverse stress conditions (salt, waterlogging, drought). Salt-tolerant rice maintained higher water consumption and growth under salinity than salt-sensitive rice. Waterlogged soybean exhibited reduced water use and growth. Long-term experiments revealed significant WUE differences between rice varieties and morphological adaptations represented by altered shoot-to-root ratios under constant drought conditions in maize.</p><p><strong>Conclusions: </strong>We demonstrate that the system can be used for varietal differences between major crops in their response to drought, waterlogging, and salinity stress. This system enables high-throughput, long-term evaluation of water use characteristics, facilitating the selection and development of water-saving and stress-tolerant crop varieties.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"179"},"PeriodicalIF":4.7,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11585949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142709524","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":"AI-powered detection and quantification of post-harvest physiological deterioration (PPD) in cassava using YOLO foundation models and K-means clustering.","authors":"Daniela Gómez Ayalde, Juan Camilo Giraldo Londoño, Audberto Quiroga Mosquera, Jorge Luis Luna Melendez, Winnie Gimode, Thierry Tran, Xiaofei Zhang, Michael Gomez Selvaraj","doi":"10.1186/s13007-024-01309-w","DOIUrl":"10.1186/s13007-024-01309-w","url":null,"abstract":"<p><strong>Background: </strong>Post-harvest physiological deterioration (PPD) poses a significant challenge to the cassava industry, leading to substantial economic losses. This study aims to address this issue by developing a comprehensive framework in collaboration with cassava breeders.</p><p><strong>Results: </strong>Advanced deep learning (DL) techniques such as Segment Anything Model (SAM) and YOLO foundation models (YOLOv7, YOLOv8, YOLOv9, and YOLO-NAS), were used to accurately categorize PPD severity from RGB images captured by cameras or cell phones. YOLOv8 achieved the highest overall mean Average Precision (mAP) of 80.4%, demonstrating superior performance in detecting and classifying different PPD levels across all three models. Although YOLO-NAS had some instability during training, it demonstrated stronger performance in detecting the PPD_0 class, with a mAP of 91.3%. YOLOv7 exhibited the lowest performance across all classes, with an overall mAP of 75.5%. Despite challenges with similar color intensities in the image data, the combination of SAM, image processing techniques such as RGB color filtering, and machine learning (ML) algorithms was effective in removing yellow and gray color sections, significantly reducing the Mean Absolute Error (MAE) in PPD estimation from 20.01 to 15.50. Moreover, Artificial Intelligence (AI)-based algorithms allow for efficient analysis of large datasets, enabling rapid screening of cassava roots for PPD symptoms. This approach is much faster and more streamlined compared to the labor-intensive and time-consuming manual visual scoring methods.</p><p><strong>Conclusion: </strong>These results highlight the significant advancements in PPD detection and quantification in cassava samples using cutting-edge AI techniques. The integration of YOLO foundation models, alongside SAM and image processing methods, has demonstrated promising precision even in scenarios where experts struggle to differentiate closely related classes. This AI-powered model not only effectively streamlines the PPD assessment in the pre-breeding pipeline but also enhances the overall effectiveness of cassava breeding programs, facilitating the selection of PPD-resistant varieties through controlled screening. By improving the precision of PPD assessments, this research contributes to the broader goal of enhancing cassava productivity, quality, and resilience, ultimately supporting global food security efforts.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"178"},"PeriodicalIF":4.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11585225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695642","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 innovative natural speed breeding technique for accelerated chickpea (Cicer arietinum L.) generation turnover.","authors":"S Gurumurthy, Apoorva Ashu, S Kruthika, Amol P Solanke, T Basavaraja, Khela Ram Soren, Jagadish Rane, Himanshu Pathak, P V Vara Prasad","doi":"10.1186/s13007-024-01299-9","DOIUrl":"10.1186/s13007-024-01299-9","url":null,"abstract":"<p><strong>Background: </strong>The slow breeding cycle presents a significant challenge in legume research and breeding. While current speed breeding (SB) methods promise faster plant turnover, they encounter space limitations and high costs. Enclosed environments risk pest and disease outbreaks, and supplying water and electricity remains challenging in many developing nations. Here, we propose an innovative natural speed breeding (nSB) approach to achieve two generation cycles per rabi season under natural open field conditions in chickpea. This cost-effective, environmentally friendly method offers a location-specific alternative to prevalent SB techniques.</p><p><strong>Results: </strong>Two field experiments were conducted. First, 11-day-old fresh immature green (FIG) seeds exhibited an 80% germination rate, reducing the duration of the breeding cycle by 14%. In second, abiotic stresses such as atmospheric, nutrient, soil, and water stresses reduced the duration of the breeding cycle by 40%, 18%, 15%, and 18%, respectively. Despite the shortened generation time, we consistently obtained a minimum of 4-6 pods plant^-1, ensuring continuity in the subsequent breeding cycle without compromising the nSB process.</p><p><strong>Conclusion: </strong>Our investigation revealed that the combination of this location advantage (40%) with the sowing of FIG seeds (14%) enables Baramati to achieve progress from F2 to F5 in 1.5 years, with two generation cycles per rabi (cool) season. Using the nSB method can save 3 years, marking a notable reduction from the conventional six-year timeline. Moreover, incorporating the additional abiotic stresses mentioned above will further reduce the generation advancement time. Therefore, nSB accelerates generation turnover and reduces varietal improvement time at a low cost.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"177"},"PeriodicalIF":4.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11583528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693259","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":"Strategy for early selection for grain yield in soybean using BLUPIS.","authors":"Andreia Schuster, Felipe Lopes da Silva, João Amaro Ferreira Vieira Netto, Emanuel Ferrari do Nascimento, Paulo Eduardo Teodoro, Leonardo Lopes Bhering","doi":"10.1186/s13007-024-01298-w","DOIUrl":"10.1186/s13007-024-01298-w","url":null,"abstract":"<p><p>In soybean breeding programs, a great deal of time is devoted to the use of methods that perform selection of individual plants during the initial generations. Our hypothesis is that BLUPIS (simulated individual BLUP) can be efficient when applied in the initial stages of soybean breeding programs. This study aimed to explore the potential of BLUPIS in the early generations of a soybean breeding program, as well as to assess the viability of the strategy of dividing the useful area of experimental plots for estimating genotypic effects and plant selection. The experiment involved 84 segregating populations and 15 soybean parents in the F₂ and F₃ generations. Yield data was collected from the 2019/2020 and 2020/2021 cropping seasons. In the F₂ generation, different data exploration methods were applied to determine the most suitable adaptation to be used in the F₃ generation. The individual BLUP (BLUPI) was compared with BLUPIS using information from different replications and/or equal to the information used in BLUPI. The selection conducted by BLUPIS and BLUPI showed high concordance regarding the selected plants. In the F₃ generation, segregating populations were selected based on positive genotypic effects, and individual plants within these populations were further selected according to the number of plants determined by BLUPIS. The division of the plot area was an efficient strategy for selecting segregating populations and individual plants within superior populations in the F₃ generation, resulting in genetic gains of approximately 1.56 g per plant. When combined with the strategy of advancing generations in the off-season, the BLUPIS approach reduces the time required to achieve a high level of homozygosity. Therefore, BLUPIS proved to be a powerful statistical tool for early selection based on grain yield in soybeans.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"176"},"PeriodicalIF":4.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11580668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142682294","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":"Automated image registration of RGB, hyperspectral and chlorophyll fluorescence imaging data.","authors":"Hans Lukas Bethge, Inga Weisheit, Mauritz Sandro Dortmund, Timm Landes, Miroslav Zabic, Marcus Linde, Thomas Debener, Dag Heinemann","doi":"10.1186/s13007-024-01296-y","DOIUrl":"10.1186/s13007-024-01296-y","url":null,"abstract":"<p><strong>Background: </strong>The early and specific detection of abiotic and biotic stresses, particularly their combinations, is a major challenge for maintaining and increasing plant productivity in sustainable agriculture under changing environmental conditions. Optical imaging techniques enable cost-efficient and non-destructive quantification of plant stress states. Monomodal detection of certain stressors is usually based on non-specific/indirect features and therefore is commonly limited in their cross-specificity to other stressors. The fusion of multi-domain sensor systems can provide more potentially discriminative features for machine learning models and potentially provide synergistic information to increase cross-specificity in plant disease detection when image data are fused at the pixel level.</p><p><strong>Results: </strong>In this study, we demonstrate successful multi-modal image registration of RGB, hyperspectral (HSI) and chlorophyll fluorescence (ChlF) kinetics data at the pixel level for high-throughput phenotyping of A. thaliana grown in Multi-well plates and an assay with detached leaf discs of Rosa × hybrida inoculated with the black spot disease-inducing fungus Diplocarpon rosae. Here, we showcase the effects of (i) selection of reference image selection, (ii) different registrations methods and (iii) frame selection on the performance of image registration via affine transform. In addition, we developed a combined approach for registration methods through NCC-based selection for each file, resulting in a robust and accurate approach that sacrifices computational time. Since image data encompass multiple objects, the initial coarse image registration using a global transformation matrix exhibited heterogeneity across different image regions. By employing an additional fine registration on the object-separated image data, we achieved a high overlap ratio. Specifically, for the A. thaliana test set, the overlap ratios (OR_Convex) were 98.0 ± 2.3% for RGB-to-ChlF and 96.6 ± 4.2% for HSI-to-ChlF. For the Rosa × hybrida test set, the values were 98.9 ± 0.5% for RGB-to-ChlF and 98.3 ± 1.3% for HSI-to-ChlF.</p><p><strong>Conclusion: </strong>The presented multi-modal imaging pipeline enables high-throughput, high-dimensional phenotyping of different plant species with respect to various biotic or abiotic stressors. This paves the way for in-depth studies investigating the correlative relationships of the multi-domain data or the performance enhancement of machine learning models via multi modal image fusion.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"175"},"PeriodicalIF":4.7,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11572093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647844","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":"Establishment of callus induction and plantlet regeneration systems of Peucedanum Praeruptorum dunn based on the tissue culture method.","authors":"Haoyu Pan, Ranran Liao, Yingyu Zhang, Muhammad Arif, Yuxin Zhang, Shuai Zhang, Yuanyuan Wang, Pengcheng Zhao, Zaigui Wang, Bangxing Han, Cheng Song","doi":"10.1186/s13007-024-01300-5","DOIUrl":"10.1186/s13007-024-01300-5","url":null,"abstract":"<p><strong>Background: </strong>Peucedanum praeruptorum Dunn has typical stacked umbels and medicinal value; however, the lack of an effective tissue culture system for P. praeruptorum has limited the large-scale propagation of its seedlings.</p><p><strong>Results: </strong>We systematically established an in vitro regeneration system for P. praeruptorum using young leaves and stems as explants. Tissue culture plantlets were successfully obtained within 123 and 90 d of somatic embryogenesis and organogenesis, respectively. Combined plant growth regulators (PGRs) were optimized to promote efficient plant regeneration at each stage of the culture process. Specifically, embryogenic callus induction was superior in Murashige and Skoog (MS) medium supplemented with 0.5 mg/L 6-benzyladenine (BA) and 2.0 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D). For somatic embryonic development, the highest differentiation rates were achieved using BA, 2,4-D, and 6-furfuryl aminopurine (6-KT). Induction of organogenesis resulted in the highest differentiation rates and proliferation coefficients of buds in MS medium supplemented with BA and α-naphthaleneacetic acid (NAA). Moreover, regeneration of P. praeruptorum seedlings was achieved by adjusting the BA and indole-3-butyric acid (IBA) concentrations in 1/2 MS medium.</p><p><strong>Conclusion: </strong>Our results provide a technical system for the rapid propagation of P. praeruptorum, which can facilitate germplasm improvement, resource conservation, and further genetic transformation of Peucedanum species.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"174"},"PeriodicalIF":4.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644385","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":"Early detection of verticillium wilt in eggplant leaves by fusing five image channels: a deep learning approach.","authors":"Youwei Zhang, Dongfang Zhang, Yunfei Zhang, Fengqing Cheng, Xuming Zhao, Min Wang, Xiaofei Fan","doi":"10.1186/s13007-024-01291-3","DOIUrl":"10.1186/s13007-024-01291-3","url":null,"abstract":"<p><strong>Background: </strong>As one of the world's most important vegetable crops, eggplant production is often severely affected by verticillium wilt, leading to significant declines in yield and quality. Traditional multispectral disease-imaging equipment is expensive and complicated to operate. Low-cost multispectral devices cannot capture images and cover less information. The traditional approach to early disease diagnosis involves using multispectral disease-imaging equipment in conjunction with machine learning technology. However, this approach has significant limitations in early disease diagnosis, including challenges such as high costs, complex operation, and low model performance.</p><p><strong>Results: </strong>The aim of this study was to combine low-cost multispectral cameras with deep learning technology to detect early stage Verticillium wilt in eggplant effectively. Using the Manual FS-3200T-10GE-NNC multispectral camera to perform multispectral imaging of the leaves of eggplant seedlings at the early infection stage, information fusion was performed on the collected multispectral images, and a five-channel image information fusion model was established. Image information fusion technology was combined with deep learning technology, among which the VGG16-triplet attention model performed the best, achieving a precision of 86.73% on the test set. Model validation on 48- and 72-hour data reached a precision of 75% and 82%, respectively, achieving an early diagnosis of Verticillium wilt. This highlighted the potential of multispectral cameras for early disease detection.</p><p><strong>Conclusions: </strong>In this study, we successfully developed a method for the non-destructive detection of the early stages of eggplant wilt disease by combining multispectral imaging technology with deep learning algorithms. While ensuring high accuracy, this method significantly reduces the cost of experimental equipment. The application of this method can reduce the cost of agricultural equipment and provide a scientific basis for agricultural production practices, helping to reduce losses caused by diseases.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"173"},"PeriodicalIF":4.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142625129","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":"BerryPortraits: Phenotyping Of Ripening Traits cranberry (Vaccinium macrocarpon Ait.) with YOLOv8.","authors":"Jenyne Loarca, Tyr Wiesner-Hanks, Hector Lopez-Moreno, Andrew F Maule, Michael Liou, Maria Alejandra Torres-Meraz, Luis Diaz-Garcia, Jennifer Johnson-Cicalese, Jeffrey Neyhart, James Polashock, Gina M Sideli, Christopher F Strock, Craig T Beil, Moira J Sheehan, Massimo Iorizzo, Amaya Atucha, Juan Zalapa","doi":"10.1186/s13007-024-01285-1","DOIUrl":"10.1186/s13007-024-01285-1","url":null,"abstract":"<p><p>BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community-developed, actively-maintained Python libraries such as OpenCV, BerryPortraits software was trained on 512 postharvest images (taken under controlled lighting conditions) of phenotypically diverse cranberry populations (Vaccinium macrocarpon Ait.) from the two largest public cranberry breeding programs in the U.S. The implementation of CIELAB, an intuitive and perceptually uniform color space, enables differentiation between berry color and berry brightness, which are confounded in classic RGB color channel measurements. Furthermore, computer vision enables precise and quantifiable color phenotyping, thus facilitating inclusion of researchers and data analysts with color vision deficiency. BerryPortraits is a phenotyping tool for researchers in plant breeding, plant genetics, horticulture, food science, plant physiology, plant pathology, and related fields. BerryPortraits has strong potential applications for other specialty crops such as blueberry, lingonberry, caneberry, grape, and more. As an open source phenotyping tool based on widely-used python libraries, BerryPortraits allows anyone to use, fork, modify, optimize, and embed this software into other tools or pipelines.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"172"},"PeriodicalIF":4.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11562335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142625007","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":"Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification.","authors":"Corine Faehn, Grzegorz Konert, Markku Keinänen, Katja Karppinen, Kirsten Krause","doi":"10.1186/s13007-024-01297-x","DOIUrl":"10.1186/s13007-024-01297-x","url":null,"abstract":"<p><strong>Background: </strong>Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines.</p><p><strong>Methods: </strong>This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification.</p><p><strong>Results: </strong>Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88-91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions.</p><p><strong>Conclusions: </strong>Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"171"},"PeriodicalIF":4.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11555864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142623491","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}