Plant Phenomics最新文献

{"title":"Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet.","authors":"Jianqing Zhao,&nbsp;Yucheng Cai,&nbsp;Suwan Wang,&nbsp;Jiawei Yan,&nbsp;Xiaolei Qiu,&nbsp;Xia Yao,&nbsp;Yongchao Tian,&nbsp;Yan Zhu,&nbsp;Weixing Cao,&nbsp;Xiaohu Zhang","doi":"10.34133/plantphenomics.0109","DOIUrl":"https://doi.org/10.34133/plantphenomics.0109","url":null,"abstract":"<p><p>Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10618025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71426239","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":"Visualizing Macrophage Phenotypes and Polarization in Diseases: From Biomarkers to Molecular Probes","authors":"Dan Ni, Heqing Zhou, Pengwei Wang, Fulin Xu, Cong Li","doi":"10.1007/s43657-023-00129-7","DOIUrl":"https://doi.org/10.1007/s43657-023-00129-7","url":null,"abstract":"","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135268206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Gut Microbiome Researches Over the Last Decade: Current Challenges and Future Directions","authors":"Hao Wu, Sofia Forslund, Zeneng Wang, Guoping Zhao","doi":"10.1007/s43657-023-00131-z","DOIUrl":"https://doi.org/10.1007/s43657-023-00131-z","url":null,"abstract":"","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135366249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>ExtSpecR</i>: An R Package and Tool for Extracting Tree Spectra from UAV-Based Remote Sensing.","authors":"Zhuo Liu,&nbsp;Mahmoud Al-Sarayreh,&nbsp;Cong Xu,&nbsp;Federico Tomasetto,&nbsp;Yanjie Li","doi":"10.34133/plantphenomics.0103","DOIUrl":"10.34133/plantphenomics.0103","url":null,"abstract":"<p><p>The development of unmanned aerial vehicle (UAV) remote sensing has been increasingly applied in forestry for high-throughput and rapid acquisition of tree phenomics traits for various research areas. However, the detection of individual trees and the extraction of their spectral data remain a challenge, often requiring manual annotation. Although several software-based solutions have been developed, they are far from being widely adopted. This paper presents <i>ExtSpecR</i>, an open-source tool for spectral extraction of a single tree in forestry with an easy-to-use interactive web application. <i>ExtSpecR</i> reduces the time required for single tree detection and annotation and simplifies the entire process of spectral and spatial feature extraction from UAV-based imagery. In addition, <i>ExtSpecR</i> provides several functionalities with interactive dashboards that allow users to maximize the quality of information extracted from UAV data. <i>ExtSpecR</i> can promote the practical use of UAV remote sensing data among forest ecology and tree breeding researchers and help them to further understand the relationships between tree growth and its physiological traits.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41237991","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":"Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.","authors":"Zixuan Teng,&nbsp;Jiawei Chen,&nbsp;Jian Wang,&nbsp;Shuixiu Wu,&nbsp;Riqing Chen,&nbsp;Yaohai Lin,&nbsp;Liyan Shen,&nbsp;Robert Jackson,&nbsp;Ji Zhou,&nbsp;Changcai Yang","doi":"10.34133/plantphenomics.0105","DOIUrl":"10.34133/plantphenomics.0105","url":null,"abstract":"<p><p>Rice (<i>Oryza sativa</i>) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM²) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM² trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM² trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM² trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM² trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41237992","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":"Making the genotypic variation visible: hyperspectral phenotyping in Scots pine seedlings.","authors":"Jan Stejskal, Jaroslav Čepl, Eva Neuwirthová, Olusegun Olaitan Akinyemi, Jiří Chuchlík, Daniel Provazník, Markku Keinänen, Petya Campbell, Jana Albrechtová, Milan Lstibůrek, Zuzana Lhotáková","doi":"10.34133/plantphenomics.0111","DOIUrl":"https://doi.org/10.34133/plantphenomics.0111","url":null,"abstract":"Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant’s physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings’ hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136185373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crop/Plant Modeling Supports Plant Breeding: I. Optimization of Environmental Factors in Accelerating Crop Growth and Development for Speed Breeding.","authors":"Yi Yu,&nbsp;Qin Cheng,&nbsp;Fei Wang,&nbsp;Yulei Zhu,&nbsp;Xiaoguang Shang,&nbsp;Ashley Jones,&nbsp;Haohua He,&nbsp;Youhong Song","doi":"10.34133/plantphenomics.0099","DOIUrl":"https://doi.org/10.34133/plantphenomics.0099","url":null,"abstract":"<p><p>The environmental conditions in customered speed breeding practice are, to some extent, empirical and, thus, can be further optimized. Crop and plant models have been developed as powerful tools in predicting growth and development under various environments for extensive crop species. To improve speed breeding, crop models can be used to predict the phenotypes resulted from genotype by environment by management at the population level, while plant models can be used to examine 3-dimensional plant architectural development by microenvironments at the organ level. By justifying the simulations via numerous virtual trials using models in testing genotype × environment × management, an optimized combination of environmental factors in achieving desired plant phenotypes can be quickly determined. Artificial intelligence in assisting for optimization is also discussed. We admit that the appropriate modifications on modeling algorithms or adding new modules may be necessary in optimizing speed breeding for specific uses. Overall, this review demonstrates that crop and plant models are promising tools in providing the optimized combinations of environment factors in advancing crop growth and development for speed breeding.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41210110","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":"Rotating Stomata Measurement Based on Anchor-Free Object Detection and Stomata Conductance Calculation.","authors":"Fan Zhang,&nbsp;Bo Wang,&nbsp;Fuhao Lu,&nbsp;Xinhong Zhang","doi":"10.34133/plantphenomics.0106","DOIUrl":"10.34133/plantphenomics.0106","url":null,"abstract":"<p><p>Stomata play an essential role in regulating water and carbon dioxide levels in plant leaves, which is important for photosynthesis. Previous deep learning-based plant stomata detection methods are based on horizontal detection. The detection anchor boxes of deep learning model are horizontal, while the angle of stomata is randomized, so it is not possible to calculate stomata traits directly from the detection anchor boxes. Additional processing of image (e.g., rotating image) is required before detecting stomata and calculating stomata traits. This paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time. Simultaneously, the stomata conductance loss function is introduced in the DeepRSD model training, which improves the efficiency of stomata detection and conductance calculation. The experimental results demonstrate that the DeepRSD model reaches 94.3% recognition accuracy for stomata of maize leaf. The proposed method can help researchers conduct large-scale studies on stomata morphology, structure, and stomata conductance models.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41210111","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":"3D Models of Sarcomas: The Next-generation Tool for Personalized Medicine","authors":"Ruiling Xu, Ruiqi Chen, Chao Tu, Xiaofeng Gong, Zhongyue Liu, Lin Mei, Xiaolei Ren, Zhihong Li","doi":"10.1007/s43657-023-00111-3","DOIUrl":"https://doi.org/10.1007/s43657-023-00111-3","url":null,"abstract":"","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frost Damage Index: The Antipode of Growing Degree Days.","authors":"Flavian Tschurr,&nbsp;Norbert Kirchgessner,&nbsp;Andreas Hund,&nbsp;Lukas Kronenberg,&nbsp;Jonas Anderegg,&nbsp;Achim Walter,&nbsp;Lukas Roth","doi":"10.34133/plantphenomics.0104","DOIUrl":"10.34133/plantphenomics.0104","url":null,"abstract":"<p><p>Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed \"frost damage index\" (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10550053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41134243","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}