A. Adak

Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence

This paper links functional data analysis with deep learning to analyze single-plant cotton senescence using temporally concatenated UAS images.

DEEP LEARNING-BASED HIGH-THROUGHPUT PHENOTYPING OF MAIZE (Zea mays L.) TASSELING FROM UAS IMAGERY ACROSS ENVIRONMENTS

This paper develops a CNN-based method for high-throughput maize tasseling phenotyping using UAS imagery across multiple environments, achieving high classification accuracy.

Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments

This study combines genomic and NIRS data to predict maize agronomic and composition traits across environments, finding that NIRS improves predictions in some scenarios but not all.

Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions

This paper shows that temporal phenomic data from drones can predict plant fitness as well as genomic data in maize hybrids.

Validation of Functional Polymorphisms Affecting Maize Plant Height by Unoccupied Aerial Systems (UAS) allows Novel Temporal Phenotypes

The paper validates two maize PHT QTVs using UAS to show their dynamic effects across genetic backgrounds and growth stages, revealing overlooked phenotypic variability.