scholarly journals Unmanned Aircraft System- (UAS-) Based High-Throughput Phenotyping (HTP) for Tomato Yield Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Anjin Chang ◽  
Jinha Jung ◽  
Junho Yeom ◽  
Murilo M. Maeda ◽  
Juan A. Landivar ◽  
...  
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
High Throughput ◽  
Unmanned Aircraft ◽  
Yield Prediction ◽  
Phenotypic Data ◽  
Breeding Programs ◽  
Tomato Yield ◽  
High Throughput Phenotyping ◽  
Aircraft System

Yield prediction and variety selection are critical components for assessing production and performance in breeding programs and precision agriculture. Since plants integrate their genetics, surrounding environments, and management conditions, crop phenotypes have been measured over cropping seasons to represent the traits of varieties. These days, UAS (unmanned aircraft system) provides a new opportunity to collect high-quality images and generate reliable phenotypic data efficiently. Here, we propose high-throughput phenotyping (HTP) from multitemporal UAS images for tomato yield estimation. UAS-based RGB and multispectral images were collected weekly and biweekly, respectively. The shape of the features of tomatoes such as canopy cover, canopy, volume, and vegetation indices derived from UAS imagery was estimated throughout the entire season. To extract time-series features from UAS-based phenotypic data, crop growth and growth rate curves were fitted using mathematical curves and first derivative equations. Time-series features such as the maximum growth rate, day at a specific event, and duration were extracted from the fitted curves of different phenotypes. The linear regression model produced high R 2 values even with different variable selection methods: all variables (0.79), forward selection (0.7), and backward selection (0.77). With factor analysis, we figured out two significant factors, growth speed and timing, related to high-yield varieties. Then, five time-series phenotypes were selected for yield prediction models explaining 65 percent of the variance in the actual harvest. The phenotypic features derived from RGB images played more important roles in prediction yield. This research also demonstrates that it is possible to select lower-performing tomato varieties successfully. The results from this work may be useful in breeding programs and research farms for selecting high-yielding and disease-/pest-resistant varieties.

scholarly journals Correction to: Soybean iron deficiency chlorosis high throughput phenotyping using an unmanned aircraft system

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Austin A. Dobbels ◽  
Aaron J. Lorenz
Keyword(s):  
Iron Deficiency ◽  
Data Collection ◽  
Data Processing ◽  
High Throughput ◽  
Image Data ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft System ◽  
Iron Deficiency Chlorosis ◽  
High Throughput Phenotyping ◽  
Aircraft System

In the original article [1], under the subheading “Image data processing”, last paragraph, last sentence that reads as “The least …… data collection” was incorrectly published. The correct sentence should read as “Least-significant differences (P < 0.20) were calculated for all 36 trials on both ground-based and UAS-image based scores for both dates of data collection.” The original article has been corrected.

scholarly journals High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

scholarly journals Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Tang ◽  
Atit Parajuli ◽  
Chunpeng James Chen ◽  
Yang Hu ◽  
Samuel Revolinski ◽  
...  
Keyword(s):  
Prediction Model ◽  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Selection Process ◽  
Forage Legume ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Fully Automatic ◽  
Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

scholarly journals OpenStats: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput Phenotypic Data

2020 ◽  
Author(s):  
Hamed Haselimashhadi ◽  
Jeremy C Mason ◽  
Ann-Marie Mallon ◽  
Damian Smedley ◽  
Terrence F Meehan ◽  
...  
Keyword(s):  
High Throughput ◽  
Software Package ◽  
Computational Time ◽  
Phenotypic Data ◽  
Creative Commons ◽  
International Mouse Phenotyping Consortium ◽  
Software Packages ◽  
Current Production ◽  
High Throughput Phenotyping ◽  
Reproducible Analysis

AbstractReproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.

scholarly journals Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

2020 ◽  
Vol 12 (6) ◽  
pp. 998 ◽  
Author(s):  
GyuJin Jang ◽  
Jaeyoung Kim ◽  
Ju-Kyung Yu ◽  
Hak-Jin Kim ◽  
Yoonha Kim ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
New Technologies ◽  
Cost Effective ◽  
New Wave ◽  
Breeding Programs ◽  
Modern Agriculture ◽  
Aerial Vehicle ◽  
High Throughput Phenotyping

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

scholarly journals High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

2021 ◽  
Vol 8 ◽  
Author(s):  
Daoliang Li ◽  
Chaoqun Quan ◽  
Zhaoyang Song ◽  
Xiang Li ◽  
Guanghui Yu ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Agronomic Traits ◽  
Global Climate ◽  
Plant Phenotyping ◽  
Phenotypic Data ◽  
Operating Modes ◽  
Sensing Platform ◽  
Exploitation And Utilization ◽  
High Throughput Phenotyping

Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

scholarly journals Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

2020 ◽  
Author(s):  
Margaret R. Krause ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Ravi P. Singh ◽  
Francisco Pinto ◽  
...  
Keyword(s):  
Grain Yield ◽  
High Throughput ◽  
Vegetation Indices ◽  
Early Generation ◽  
Breeding Programs ◽  
Breeding Lines ◽  
High Throughput Phenotyping ◽  
Seed Increase ◽  
Yield Trials ◽  
Selection Of

ABSTRACTBreeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

scholarly journals Genetic interactions derived from high-throughput phenotyping of 7,350 yeast cell cycle mutants

2019 ◽  
Author(s):  
Jenna E. Gallegos ◽  
Neil R. Adames ◽  
Mark F. Rogers ◽  
Pavel Kraikivski ◽  
Aubrey Ibele ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Growth Rate ◽  
Cell Division ◽  
Yeast Cell ◽  
High Throughput ◽  
Genetic Interactions ◽  
Control Network ◽  
Cell Cycle Genes ◽  
High Throughput Phenotyping ◽  
Yeast Mutants

AbstractOver the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.Author SummaryThe process of cell division, also called the cell cycle, is controlled by a highly complex network of interconnected genes. If this process goes awry, diseases such as cancer can result. In order to unravel the complex interactions within the cell cycle control network, computational biologists have developed mathematical models that describe how different cell cycle genes are related. These models are built using large datasets describing the effect of mutating one or more genes within the network. In this manuscript, we present a novel method for producing such datasets. Using our method, we generate 7,350 yeast mutants to explore the interactions between key cell cycle genes. We measure the effect of the mutations by monitoring the growth rate of the yeast mutants under different environmental conditions. We use our mutants to revise an existing model of the yeast cell cycle and present a dataset of ∼44,000 gene by environment combinations as a resource to the yeast genetics and modeling communities.

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