Unoccupied aerial systems imagery for phenoty** in cotton, maize, soybean, and wheat breeding
High‐throughput phenoty** (HTP) with unoccupied aerial systems (UAS), consisting of
unoccupied aerial vehicles (UAV; or drones) and sensor (s), is an increasingly promising …
unoccupied aerial vehicles (UAV; or drones) and sensor (s), is an increasingly promising …
Plant genotype to phenotype prediction using machine learning
Genomic prediction tools support crop breeding based on statistical methods, such as the
genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to …
genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to …
Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches
B Singh, S Kumar, A Elangovan, D Vasht… - Frontiers in Plant …, 2023 - frontiersin.org
Introduction Phenomics has emerged as important tool to bridge the genotype-phenotype
gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its …
gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its …
Temporal vegetation indices and plant height from remotely sensed imagery can predict grain yield and flowering time breeding value in maize via machine learning …
Unoccupied aerial system (UAS; ie, drone equipped with sensors) field-based high-
throughput phenoty** (HTP) platforms are used to collect high quality images of plant …
throughput phenoty** (HTP) platforms are used to collect high quality images of plant …
[PDF][PDF] Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions
A major challenge of genetic improvement and selection is to accurately predict individuals
with the highest fitness in a population without direct measurement. Over the last decade …
with the highest fitness in a population without direct measurement. Over the last decade …
Phenomic data-driven biological prediction of maize through field-based high-throughput phenoty** integration with genomic data
High-throughput phenoty** (HTP) has expanded the dimensionality of data in plant
research; however, HTP has resulted in few novel biological discoveries to date. Field …
research; however, HTP has resulted in few novel biological discoveries to date. Field …
Pedigree‐management‐flight interaction for temporal phenotype analysis and temporal phenomic prediction
Unoccupied aerial systems (UAS, aka drones) provide high dimensional temporal
phenotype data for predictive plant breeding and genetic dissection. Methods to assess …
phenotype data for predictive plant breeding and genetic dissection. Methods to assess …
Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms
Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora
Underw.) and subsequent crop senescence require expert observation and are resource …
Underw.) and subsequent crop senescence require expert observation and are resource …
Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations
Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental
conditions is crucial for understanding plant development, requiring further modeling …
conditions is crucial for understanding plant development, requiring further modeling …
Field-based high-throughput phenoty** enhances phenomic and genomic predictions for grain yield and plant height across years in maize
Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from
drone images, to predict key agronomic traits in maize, despite challenges in matching …
drone images, to predict key agronomic traits in maize, despite challenges in matching …