scholarly journals Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology

2022 ◽  
Vol 12 ◽  
Author(s):  
Jing Zhou ◽  
Eduardo Beche ◽  
Caio Canella Vieira ◽  
Dennis Yungbluth ◽  
Jianfeng Zhou ◽  
...  

The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.

2020 ◽  
Author(s):  
Margaret R. Krause ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Ravi P. Singh ◽  
Francisco Pinto ◽  
...  

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.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Tang ◽  
Atit Parajuli ◽  
Chunpeng James Chen ◽  
Yang Hu ◽  
Samuel Revolinski ◽  
...  

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.


2020 ◽  
Vol 12 (6) ◽  
pp. 998 ◽  
Author(s):  
GyuJin Jang ◽  
Jaeyoung Kim ◽  
Ju-Kyung Yu ◽  
Hak-Jin Kim ◽  
Yoonha Kim ◽  
...  

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.


2015 ◽  
Vol 22 (5) ◽  
pp. 993-1000 ◽  
Author(s):  
Sheng Yu ◽  
Katherine P Liao ◽  
Stanley Y Shaw ◽  
Vivian S Gainer ◽  
Susanne E Churchill ◽  
...  

Abstract Objective Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Materials and methods Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. Results The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Discussion Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. Conclusion The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.


2018 ◽  
Author(s):  
Florie Gosseau ◽  
Nicolas Blanchet ◽  
Didier Varès ◽  
Philippe Burger ◽  
Didier Campergue ◽  
...  

AbstractHeliaphen is an outdoor pot platform designed for high-throughput phenotyping. It allows automated management of drought scenarios and plant monitoring during the whole plant cycle. A robot moving between plants growing in 15L pots monitors plant water status and phenotypes plant or leaf morphology, from which we can compute more complex traits such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit. Here, we illustrate the platform capabilities for sunflower on two practical cases: a genetic and genomics study for the response to drought of yield-related traits and a simulation study, where we use measured parameters as inputs for a crop simulation model. For the genetic study, classical measurements of thousand-kernel weight (TKW) were done on a sunflower bi-parental population under water stress and control conditions managed automatically. The association study using the TKW drought-response highlighted five genetic markers. A complementary transcriptomic experiment identified closeby candidate genes differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used the SUNFLO crop simulation model to assess the impact of two traits measured on the platform (LE and TR) on crop yield in a large population of environments. We conducted simulations in 42 contrasted locations across Europe and 21 years of climate data. We defined the pattern of abiotic stresses occurring at this continental scale and identified ideotypes (i.e. genotypes with specific traits values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can help with the identification of the genetic architecture of complex response traits and the estimation of eco-physiological model parameters in order to define ideotypes adapted to different environmental conditions.


Author(s):  
Rodrigo Trevisan ◽  
Osvaldo Pérez ◽  
Nathan Schmitz ◽  
Brian Diers ◽  
Nicolas Martin

Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture used solves limitations of previous research and can be used at scale in commercial breeding programs.


2018 ◽  
Vol 55 (2) ◽  
pp. 97-121 ◽  
Author(s):  
Anderson Cristiano Neisse ◽  
Jhessica Letícia Kirch ◽  
Kuang Hongyu

SummaryThe presence of genotype-environment interaction (GEI) influences production making the selection of cultivars in a complex process. The two most used methods to analyze GEI and evaluate genotypes are AMMI and GGE Biplot, being used for the analysis of multi environment trials data (MET). Despite their different approaches, both models complement each other in order to strengthen decision making. However, both models are based on biplots, consequently, biplot-based interpretation doesn’t scale well beyond two-dimensional plots, which happens whenever the first two components don’t capture enough variation. This paper proposes an approach to such cases based on cluster analysis combined with the concept of medoids. It also applies AMMI and GGE Biplot to the adjusted data in order to compare both models. The data is provided by the International Maize and Wheat Improvement Center (CIMMYT) and comes from the 14th Semi-Arid Wheat Yield Trial (SAWYT), an experiment concerning 50 genotypes of spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall. It was performed in 36 environments across 14 countries. The analysis provided 25 genotypes clusters and 6 environments clusters. Both models were equivalent for the data’s evaluation, permitting increased reliability in the selection of superior cultivars and test environments.


Author(s):  
M. Herrero-Huerta ◽  
K. M. Rainey

<p><strong>Abstract.</strong> Nowadays, an essential tool to improve the efficiency of crop genetics is automated, precise and cost-effective phenotyping of the plants. The aim of this study is to generate a methodology for high throughput phenotyping the physiological growth dynamics of soybeans by UAS-based 3D modelling. During the 2018 growing season, a soybean experiment was performed at the Agronomy Center for Research and Education (ACRE) in West-Lafayette (Indiana, USA). Periodic images were acquired by G9X Canon compact digital camera on board senseFly eBee. The study area is reconstructed in 3D by Image-based modelling. Algorithms and techniques were combined to analyse growth dynamics of the crop via height variations and to quantify biomass. Results provide practical information for the selection of phenotypes for breeding.</p>


2012 ◽  
Vol 40 (1) ◽  
pp. 293 ◽  
Author(s):  
Rahmatollah KARIMIZADEH ◽  
Mohtasham MOHAMMADI ◽  
Naser SABAGHNIA ◽  
Mohammad Kazem SHEFAZADEH

Genotype × environment interaction (GEI) is of special interest in breeding programs to identify adaptation targets and test locations as well as to determine the most favorable genotypes. There are several nonparametric procedures used to interpret the GEI in multi-environmental trials. The purposes of this investigation were (i) to compare the effect of correction on Huehn’s nonparametric stability statistics and (ii) to use nonparametric statistics for a GEI study on lentil. Nine improved lentil genotypes and one local cultivar were grown in 5 sites during two consecutive years. Results of the nonparametric analysis demonstrated both additive and crossover GEIs. According to uncorrected nonparametric statistics, genotypes G8 and G9 were the most stable and based on corrected nonparametric statistics of Huehn, genotypes G1, G2 and G10 were the most stable. In this investigation, mean of ranks (MR) and coefficient of variation of ranks (CV) with (6)iSwere associated with high mean yield (within the dynamic concept of stability), but the other nonparametric statistics were not positively correlated with mean yield and were identified within a static concept of stability. Results also indicated that corrected nonparametric statistics were not suitable for simultaneous selection of mean yield and stability. Such an outcome could be used to delineate predictive, more rigorous recommendation strategies as well as to help define stability concepts to identify recommendations for lentil and other crops.


2019 ◽  
Vol 11 (4) ◽  
pp. 410 ◽  
Author(s):  
Yiannis Ampatzidis ◽  
Victor Partel

Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.


Sign in / Sign up

Export Citation Format

Share Document