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 …
Crop genomic selection with deep learning and environmental data: A survey
Machine learning techniques for crop genomic selections, especially for single-environment
plants, are well-developed. These machine learning models, which use dense genome …
plants, are well-developed. These machine learning models, which use dense genome …
Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenoty** and deep learning
Integrating high-throughput phenoty** (HTP) based traits into phenomic and genomic
selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat …
selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat …
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 …
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 …
Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum
aestivum L.), causing considerable losses in yield and end‐use quality. Phenoty** of FHB …
aestivum L.), causing considerable losses in yield and end‐use quality. Phenoty** of FHB …
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 …
Using drone-retrieved multispectral data for phenomic selection in potato breeding
Predictive breeding approaches, like phenomic or genomic selection, have the potential to
increase the selection gain for potato breeding programs which are characterized by very …
increase the selection gain for potato breeding programs which are characterized by very …
Assessing drought stress of sugarcane cultivars using unmanned vehicle system (UAS)-based vegetation indices and physiological parameters
Sugarcane breeding for drought tolerance is a sustainable strategy to cope with drought. In
addition to biotechnology, high-throughput phenoty** has become an emerging tool for …
addition to biotechnology, high-throughput phenoty** has become an emerging tool for …