Plant Phenome Journal最新文献

{"title":"Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean.","authors":"Sarah E Jones, Timilehin T Ayanlade, Benjamin Fallen, Talukder Z Jubery, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Asheesh K Singh","doi":"10.1002/ppj2.70009","DOIUrl":"https://doi.org/10.1002/ppj2.70009","url":null,"abstract":"<p><p>Soybean (<i>Glycine max</i> [L.] Merr.) production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events. Water limiting stress, that is, drought, emerges as a significant risk for soybean production, underscoring the need for advancements in stress monitoring for crop breeding and production. This project combined multi-modal information to identify the most effective and efficient automated methods to study drought response. We investigated a set of diverse soybean accessions using multiple sensors in a time series high-throughput phenotyping manner to: (1) develop a pipeline for rapid classification of soybean drought stress symptoms, and (2) investigate methods for early detection of drought stress. We utilized high-throughput time-series phenotyping using unmanned aerial vehicles and sensors in conjunction with machine learning analytics, which offered a swift and efficient means of phenotyping. The visible bands were most effective in classifying the severity of canopy wilting stress after symptom emergence. Non-visual bands in the near-infrared region and short-wave infrared region contribute to the differentiation of susceptible and tolerant soybean accessions prior to visual symptom development. We report pre-visual detection of soybean wilting using a combination of different vegetation indices and spectral bands, especially in the red-edge. These results can contribute to early stress detection methodologies and rapid classification of drought responses for breeding and production applications.</p>","PeriodicalId":33084,"journal":{"name":"Plant Phenome Journal","volume":"7 1","pages":"e70009"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenotyping and predicting wheat spike characteristics using image analysis and machine learning","authors":"Mik Hammers, Zachary J. Winn, Asa Ben‐Hur, Dylan Larkin, Jamison Murry, Richard Esten Mason","doi":"10.1002/ppj2.20087","DOIUrl":"https://doi.org/10.1002/ppj2.20087","url":null,"abstract":"Abstract Improvements in trait phenotyping are needed to increase the quantity and quality of data available for genetic improvement of crops. In this study, we used moderate throughput image analysis and machine learning as a pipeline for phenotyping a key wheat spike characteristic: spikelet number per spike. A population of 594 soft red winter wheat inbred lines was evaluated in the field for 2 years and images of wheat spikes were taken and used to train deep‐learning algorithms to predict spikelet number. A total of 12,717 images were used to train, test, and validate a basic regression convolutional neural network (CNN), a visual geometry group application regression model, VGG16, the ResNet152V2 model, and the EfficientNetV2L model. The EfficientNetV2L model was the most accurate, having the lowest mean absolute error, second lowest root mean square error, and highest coefficient of determination (mean absolute error [MAE] = 0.60, root mean square error [RMSE] = 0.79, and R 2 = 0.90). The ResNet152V2 model was slightly less accurate with a slightly better fit (MAE = 0.61,m RMSE = 0.78, and R 2 = 0.87), followed by the basic CNN (MAE = 0.75, RMSE = 1.00, and R 2 = 0.74) and finally by the VGG16 (MAE = 1.51, RMSE = 1.29, and R 2 = 0.076). With an average error of just above one half of a spikelet, utilizing image analysis and machine learning counting methods could be used for multiple breeding applications, including direct selection of spikelet number, to provide data to identify quantitative trait loci, or for training whole genome selection models.","PeriodicalId":33084,"journal":{"name":"Plant Phenome Journal","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135888808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fourier‐transform infrared spectroscopy: An inexpensive, rapid, and less‐destructive tool for starch and resistant starch analysis from pulse flour","authors":"Nathan Johnson, Pushparajah Thavarajah, Amod Madurapperumage, Nathan Windsor, Leung Tang, Dil Thavarajah","doi":"10.1002/ppj2.20086","DOIUrl":"https://doi.org/10.1002/ppj2.20086","url":null,"abstract":"Abstract Pulse crops are a rich source of resistant starch (RS) (5–7 g/100 g), a prebiotic carbohydrate that promotes gut health. The standard method to measure total starch (TS) and RS is through enzymatic assay; however, this is both time consuming and expensive. Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy is a high‐throughput, cost‐effective method to quantify nutritional traits in seeds, but models have not been developed for starch in pulse crops. Therefore, this study aimed to develop and validate an FT‐MIR chemometric technique to estimate TS and RS in dry pea ( Pisum sativum L.), chickpea ( Cicer arietinum L.), and lentil ( Lens culinaris , Medikus) flours to accelerate global pulse breeding efforts, support industrial carbohydrate utilization, and develop healthier food‐feed calorie contents. Breeding lines were selected to capture the diversity of starch concentrations in each crop and were analyzed using an enzymatic assay. Models for each trait–crop combination were calibrated using partial least squares regression, resulting in R 2 and root means square error of prediction ranging from 0.91 to 0.96 and 0.16 to 4.0 g/100 g, respectively. These results demonstrate that FT‐MIR spectroscopy is a promising tool for estimating TS and RS concentrations in pulse crops at a reduced analysis time and cost, expediting plant breeding and starch use efforts in the food processing industry.","PeriodicalId":33084,"journal":{"name":"Plant Phenome Journal","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenomic versus genomic prediction—A comparison of prediction accuracies for grain yield in hard winter wheat lines","authors":"Zachary J. Winn, Amanda L. Amsberry, Scott D. Haley, Noah D. DeWitt, Richard Esten Mason","doi":"10.1002/ppj2.20084","DOIUrl":"https://doi.org/10.1002/ppj2.20084","url":null,"abstract":"Abstract Common bread wheat ( Triticum aestivum L.) is a key component of global diets, but the genetic improvement of wheat is not keeping pace with the growing demands of the world's population. To increase efficiency and reduce costs, breeding programs are rapidly adopting the use of unoccupied aerial vehicles to conduct high‐throughput spectral analyses. This study examined the effectiveness of multispectral indices in predicting grain yield compared to genomic prediction. Multispectral data were collected on advanced generation yield nursery trials during the 2019–2021 growing seasons in the Colorado State University Wheat Breeding Program. Genome‐wide genotyping was performed on these advanced generations and all plots were harvested to measure grain yield. Two methods were used to predict grain yield: genomic estimated breeding values (GEBVs) generated by a genomic best linear unbiased prediction (gBLUP) model and phenomic phenotypic estimates (PPEs) using only spectral indices via multiple linear regression (MLR), k‐nearest neighbors (KNNs), and random forest (RF) models. In cross‐validation, PPEs produced by MLR, KNN, and RF models had higher prediction accuracy () than GEBVs produced by gBLUP ( ). In leave‐one‐year‐out forward validation using only multispectral data for 2020 and 2021, PPEs from MLR and KNN models had higher prediction accuracy of grain yield than GEBVs of those same lines. These findings suggest that a limited number of spectra may produce PPEs that are more accurate than or equivalently accurate as GEBVs derived from gBLUP, and this method should be evaluated in earlier development material where sequencing is not feasible.","PeriodicalId":33084,"journal":{"name":"Plant Phenome Journal","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135899714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncrewed aerial vehicle radiometric calibration: A comparison of autoexposure and fixed‐exposure images","authors":"G. Cody Bagnall, John Alex Thomasson, Chenghai Yang, Tianyi Wang, Xiongzhe Han, Chao Sima, Anjin Chang","doi":"10.1002/ppj2.20082","DOIUrl":"https://doi.org/10.1002/ppj2.20082","url":null,"abstract":"Abstract Remote sensing with uncrewed aerial vehicles (UAVs) is increasingly being used in agriculture to provide data on the physical characteristics of plants under field conditions. Data accuracy is critical for decision making with a high degree of confidence. In this work, we compared two multispectral camera calibration methods for image data collected with a UAV: (1) an autoexposure method that relies on a single calibration panel and a post hoc calibration, and (2) a fixed‐exposure system that uses three in‐field gray calibration panels using the empirical line calibration method. Both methods were compared to reflectance data from (a) four ground calibration targets measured with a spectroradiometer and (b) a single manned aircraft image calibrated with commercial calibration tarps. In a band‐by‐band comparison, the autoexposure method produced almost twice as much radiometric error on average compared with fixed exposure. Because remote sensing data are commonly converted to spectral indices, the calibration methods were also evaluated by calculating the visible atmospherically resistant index (VARI) and comparing the resulting data to the manned aircraft image. Similarly, the autoexposure method in this case produced twice the error of the fixed‐exposure method. The effect of the error was considered in a production agriculture context by simulating a remote sensing‐based prescription map for pesticide application in a cotton ( Gossypium ) field and calculating the number of mislabeled management zones. The simulation showed that the autoexposure method would be more costly to the farm because of its higher error, roughly $8.00/ha based on the assumptions made.","PeriodicalId":33084,"journal":{"name":"Plant Phenome Journal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135441106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A greenhouse‐based high‐throughput phenotyping platform for identification and genetic dissection of resistance to Aphanomyces root rot in field pea","authors":"Md. Abdullah Al Bari, Dimitri Fonseka, John Stenger, Kimberly Zitnick‐Anderson, Sikiru Adeniyi Atanda, Mario Morales, Hannah Worral, Lisa Piche, Jeonghwa Kim, Josephine Johnson, Rica Amor Saludares, Paulo Flores, Julie Pasche, Nonoy Bandillo","doi":"10.1002/ppj2.20063","DOIUrl":"https://doi.org/10.1002/ppj2.20063","url":null,"abstract":"Abstract Aphanomyces root rot (ARR) is a devastating disease in field pea ( Pisum sativum L.) that can cause up to 100% crop failure. Assessment of ARR resistance can be a rigorous, costly, time‐demanding activity that is relatively low‐throughput and prone to human errors. These limits the ability to effectively and efficiently phenotype the disease symptoms arising from ARR infection which remains a perennial bottleneck to the successful evaluation and incorporation of disease resistance into new cultivars. In this study, we developed a greenhouse‐based high‐throughput phenotyping (HTP) platform that moves along the rails above the greenhouse benches and captures the visual symptoms caused by Aphanomyces euteiches in field pea. We pilot tested this platform alongside with conventional visual scoring in five experimental trials under greenhouse conditions, assaying over 12,600 single plants of advanced breeding lines developed by the North Dakota State University Pulse Breeding Program. Precision estimated through broad‐sense heritability ( H 2 ) was consistently higher for RGB‐derived indices ( H 2 , Exg = 0.86) than the conventional visual scores ( H 2 , disease severity index = 0.59). Prediction of disease severity using a random forest modeling of RGB‐derived indices achieved 0.69 accuracy on the test sets, with inaccurate classification partly attributed to the presence of tolerant lines (displaying root rot but no foliar symptoms) and within‐line genetic heterogeneity. We genetically dissected variation for ARR resistance from the population using RGB‐derived indices and visual scores through genome‐wide association mapping and identified a total of 260 associated single nucleotide polymorphism (SNP). The number of associated SNP for RGB‐derived indices was consistently higher than the number of associated SNP identified using visual scores, with the most significant SNP explaining about 5%–9% of variance per index. We identified previously mapped genes known to be involved in the biological pathways that trigger immunity against ARR and a few novel QTLs with small‐effect sizes that may be worthy of validation in the future. The newly identified QTLs and underlying genes, along with genotypes with promising resistance identified in this study, can be useful for improving a long‐term and durable resistance to ARR.","PeriodicalId":33084,"journal":{"name":"Plant Phenome Journal","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136297917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FIELDimageR.Extra: Advancing user experience and computational efficiency for analysis of orthomosaic from agricultural field trials","authors":"Popat S. Pawar, Filipe Inacio Matias","doi":"10.1002/ppj2.20083","DOIUrl":"https://doi.org/10.1002/ppj2.20083","url":null,"abstract":"Abstract FieldimageR.Extra is an advanced R package that enhances user experience and computational efficiency in analyzing orthomosaic images from agricultural field trials. Through the integration of modern GIS libraries like terra, stars, and sf, it surpasses traditional packages like raster and sp. The FieldimageR.Extra offers interactive visualization, vector feature creation, and spatial data editing within the R environment through the integration of mapview and mapedit packages. Notably, FieldimageR.Extra introduces new functions like fieldShape_render and fieldShape_edit, enabling flexible plot grid generation and editing capabilities. Comparative evaluations demonstrate the superior performance of FieldimageR.Extra in handling spatial data, crop and extract operations, and plot grid generation. With its comprehensive features, FieldimageR.Extra stands as a valuable addition to the FieldimageR R package, offering researchers efficient tools for unmanned aerial vehicle‐based orthomosaic image analysis in agricultural research. FIELDimageR.Extra is publicly available as a GitHub repository ( https://github.com/filipematias23/FIELDimageR.Extra ).","PeriodicalId":33084,"journal":{"name":"Plant Phenome Journal","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135494875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenotyping agronomic and physiological traits in peanut under mid‐season drought stress using UAV‐based hyperspectral imaging and machine learning","authors":"Kamand Bagherian, Rafael Bidese‐Puhl, Yin Bao, Qiong Zhang, Alvaro Sanz‐Saez, Phat M. Dang, Marshall C. Lamb, Charles Chen","doi":"10.1002/ppj2.20081","DOIUrl":"https://doi.org/10.1002/ppj2.20081","url":null,"abstract":"Abstract Agronomic and physiological traits in peanut ( Arachis hypogaea ) are important to breeders for selecting high‐yielding and resilient genotypes. However, direct measurement of these traits is labor‐intensive and time‐consuming. This study assessed the feasibility of using unmanned aerial vehicles (UAV)‐based hyperspectral imaging and machine learning (ML) techniques to predict three agronomic traits (biomass, pod count, and yield) and two physiological traits (photosynthesis and stomatal conductance) in peanut under drought stress. Two different approaches were evaluated. The first approach employed eighty narrowband vegetation indices as input features for an ensemble model that included K‐nearest neighbors, support vector regression, random forest, and multi‐layer perceptron (MLP). The second approach utilized mean and standard deviation of canopy spectral reflectance per band. The resultant 400 features were used to train a deep learning (DL) model consisting of one‐dimensional convolutional layers followed by an MLP regressor. Predictions of the agronomic traits obtained using feature learning and DL ( R 2 = 0.45–0.73; symmetric mean absolute percentage error [sMAPE] = 24%–51%) outperformed those obtained using feature engineering and conventional ML models ( R 2 = 0.44–0.61, sMAPE = 27%–59%). In contrast, the ensemble model had a slightly better performance in predicting physiological traits ( R 2 = 0.35–0.57; sMAPE = 37%–70%) compared to the results obtained from the DL model ( R 2 = 0.36–0.52; sMAPE = 47%–64%). The results showed that the combination of UAV‐based hyperspectral imaging and ML techniques have the potential to assist breeders in rapid screening of genotypes for improved yield and drought tolerance in peanut.","PeriodicalId":33084,"journal":{"name":"Plant Phenome Journal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135402193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}