scholarly journals Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Anju Biswas ◽  
Mario Henrique Murad Leite Andrade ◽  
Janam P. Acharya ◽  
Cleber Lopes de Souza ◽  
Yolanda Lopez ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Medicago Sativa ◽  
Missing Values ◽  
Genetic Parameters ◽  
Vegetation Indices ◽  
Breeding Population ◽  
Full Dataset ◽  
Bivariate Models ◽  
High Throughput Phenotyping ◽  
Predicted Values

The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.

scholarly journals High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

2021 ◽  
Author(s):  
Marcus Vinicius Vieira Borges ◽  
Janielle de Oliveira Garcia ◽  
Tays Silva Batista ◽  
Alexsandra Nogueira Martins Silva ◽  
Fabio Henrique Rojo Baio ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Throughput ◽  
Mean Squared Error ◽  
Vegetation Indices ◽  
Plant Size ◽  
Eucalyptus Species ◽  
Forest Inventories ◽  
Spectral Bands ◽  
High Throughput Phenotyping ◽  
Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

Estimation of genetic parameters and prediction of breeding values in an autotetraploid blueberry breeding population with extensive pedigree data

Euphytica ◽  
2018 ◽  
Vol 214 (5) ◽  
Author(s):  
Catherine Cellon ◽  
Rodrigo R. Amadeu ◽  
James W. Olmstead ◽  
Matthew R. Mattia ◽  
Luis Felipe V. Ferrao ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Breeding Population ◽  
Pedigree Data ◽  
Breeding Values ◽  
Estimation Of Genetic Parameters

scholarly journals Genetic parameters, prediction, and selection in a white Guinea yam early‐generation breeding population using pedigree information

Crop Science ◽  
2020 ◽  
Author(s):  
Asrat Asfaw ◽  
Dotun Samuel Aderonmu ◽  
Kwabena Darkwa ◽  
David De Koeyer ◽  
Paterne Agre ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Breeding Population ◽  
Pedigree Information ◽  
Early Generation ◽  
Guinea Yam

scholarly journals Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging

Plant Methods ◽  
2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Zohaib Khan ◽  
Vahid Rahimi-Eichi ◽  
Stephan Haefele ◽  
Trevor Garnett ◽  
Stanley J. Miklavcic
Keyword(s):  
High Throughput ◽  
Vegetation Indices ◽  
Aerial Imaging ◽  
High Throughput Phenotyping

scholarly journals Bayesian genetic parameters for body weight and survival of Nile tilapia farmed in Brazil

2011 ◽  
Vol 46 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Alexandra Inês Santos ◽  
Ricardo Pereira Ribeiro ◽  
Lauro Vargas ◽  
Freddy Mora ◽  
Luiz Alexandre Filho ◽  
...  
Keyword(s):  
Genetic Gain ◽  
Nile Tilapia ◽  
Genetic Parameters ◽  
Positive Impact ◽  
Genetic Correlations ◽  
Breeding Population ◽  
Linear Threshold ◽  
Nile Tilapia Oreochromis Niloticus ◽  
Impact On Survival ◽  
Credible Intervals

The objective of this study was to estimate genetic parameters for survival and weight of Nile tilapia (Oreochromis niloticus), farmed in cages and ponds in Brazil, and to predict genetic gain under different scenarios. Survival was recorded as a binary response (dead or alive), during harvest time in the 2008 grow-out period. Genetic parameters were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. The breeding population consisted of 2,912 individual fish, which were analyzed together with the pedigree of 5,394 fish. The heritabilities estimates, with 95% posterior credible intervals, for tagging weight, harvest weight and survival were 0.17 (0.09-0.27), 0.21 (0.12-0.32) and 0.32 (0.22-0.44), respectively. Credible intervals show a 95% probability that the true genetic correlations were in a favourable direction. The selection for weight has a positive impact on survival. Estimated genetic gain was high when selecting for harvest weight (5.07%), and indirect gain for tagging weight (2.17%) and survival (2.03%) were also considerable.

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 Determining the Optimal Age For Selection by Modelling the Age-Related Trends in Genetic Parameters in Eucalyptus Hybrid Populations

2009 ◽  
Vol 58 (1-6) ◽  
pp. 102-112 ◽  
Author(s):  
Jean-Marc Bouvet ◽  
P. Vigneron ◽  
E. Villar ◽  
A. Saya
Keyword(s):  
Genetic Gain ◽  
Genetic Parameters ◽  
Breeding Population ◽  
Nonlinear Functions ◽  
Pure Species ◽  
Male And Female ◽  
Age Related ◽  
Factorial Mating ◽  
Efficiency Of Selection ◽  
Individual Trees

Abstract Ten factorial mating designs using a combined total of 88 females, 107 males, 684 families and 37,206 individual trees were used to model the age-related trends in genetic parameters and genetic gain between four and 65 months in the Eucalyptus urophylla x grandis breeding population in Republic of Congo. Selection was either of pure species (as parents for continued breeding) or individual hybrids for commercial plantations based on clonal varieties. The variance components were significantly different from zero for female, male and female-by-male interaction effects for volume. The age-related trends in additive, dominance and environmental variances, modelled by nonlinear functions, showed three phases corresponding to different stages of competition and growth. Male and female narrow sense heritabilities were high (h2 Am= 0.70 and h2 Af = 0.90, respectively for highest estimates) compared with individual broad sense heritability (h2 ind= 0.45). They were modelled by polynomial functions that did not display specific trends with age. The age-age correlations, modelled by a response surface, were higher than 0.8 after 36 months. A similar trend with age was observed for additive and total genetic effects. Genetic gain was calculated by combining the different models. The genetic gain was higher for female than for male. Considerable gains can be achieved by clone selection. The efficiency of selection indicated an optimal age of 54 months for juvenile selection of males and females and a mature age for ortets. The trend in efficiency of selection per time unit showed that juvenile selection for volume is much more efficient than adult selection whatever the age.

scholarly journals SVM and Cross-validation using R Studio

2020 ◽  
Vol 10 (1) ◽  
pp. 46-54
Keyword(s):  
Data Mining ◽  
Missing Values ◽  
Cross Validation ◽  
Confusion Matrix ◽  
Tourism Industry ◽  
Support Vector ◽  
Full Dataset ◽  
The Matrix ◽  
Social Media Platforms ◽  
Classification Of Images

Each passing day data is getting multiplied. It is difficult to extract useful information from such big data. Data Mining is used to extract useful information. Data mining is used in majorly all fields like healthcare, marketing, social media platforms and so on. In this paper, data is loaded and preprocessed by dealing with some missing values. The dataset used is of Airbnb, the platform used for lodging and tourism industry. Analyzing the data by plotting correlation using spearman method. Further, applying PCA and Support Vector Machine classification technique on the dataset. There are various applications of SVM, it is used in face-detection, text and hypertext categorization, classification of images, bioinformatics and so on. SVM has high dimensional input space, sparse document vectors and regularization parameters therefore it is appropriate to use SVM. Cross-validation gives more accurate result. The dataset is divided into folds. The end product is the test set which is similar to full dataset. Confusion matrix is evaluated, grid approach is followed for building the matrix at various seeds and kernels (RBF, Polynomial). The aim of this research is to see which is the best kernel for the dataset.

An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems

2020 ◽  
Vol 63 (4) ◽  
pp. 1133-1146
Author(s):  
Beichen Lyu ◽  
Stuart D. Smith ◽  
Yexiang Xue ◽  
Katy M. Rainey ◽  
Keith Cherkauer
Keyword(s):  
Data Processing ◽  
High Throughput ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Unmanned Aerial Systems ◽  
Growth Stages ◽  
Processing Pipeline ◽  
High Throughput Phenotyping ◽  
Aerial Systems

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

Development of a Mobile Platform for Wheat Phenotyping

2020 ◽  
Author(s):  
Majid Khak Pour ◽  
Reza Fotouhi ◽  
Pierre Hucl
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Mobile Platform ◽  
Mechatronic System ◽  
High Quality ◽  
Wheat Field ◽  
Crop Monitoring ◽  
High Throughput Phenotyping

Abstract Designing and implementing an affordable High-Throughput Phenotyping Platform (HTPP) for monitoring crops’ features in different stages of their growth can provide valuable information for crop-breeders to study possible correlation between genotypes and phenotypes. Conducting automatic field measurements can improve crop productions. In this research, we have focused on development of a mechatronic system, hardware and software, for a mobile field-based HTPP for autonomous crop monitoring for wheat field. The system can measure canopy’s height, temperature, vegetation indices and is able to take high quality photos of crops. The system includes developed software for data and image acquisition. The main contribution of this study is autonomous, reliable, and fast data collection for wheat and similar crops.

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