A5033093479- Traffic Prediction and Management Techniques
- Time Series Analysis and Forecasting
- Anomaly Detection Techniques and Applications
- Leaf Properties and Growth Measurement
- Genetics and Plant Breeding
- Smart Agriculture and AI
- Pharmacogenetics and Drug Metabolism
- Computational Drug Discovery Methods
- Metabolomics and Mass Spectrometry Studies
- Remote Sensing in Agriculture
- Genetic and phenotypic traits in livestock
- Crop Yield and Soil Fertility
University of Nebraska–Lincoln
2021-2024
Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods obtaining automated tassel counts at plot level. We employed both object-based...
Abstract Predicting phenotypes from a combination of genetic and environmental factors is grand challenge modern biology. Slight improvements in this area have the potential to save lives, improve food fuel security, permit better care planet, create other positive outcomes. In 2022 2023, first open-to-the-public Genomes Fields initiative Genotype by Environment prediction competition was held using large dataset including genomic variation, phenotype weather measurements, field management...
Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment will likely emerge from collaborative research efforts. This highlights need for reusable adaptable improved tested by wider scientific...
Counting maize tassels in field conditions is predominantly done manually. Recently, computer-vision based methods have been utilized to detect from images captured by UAV transects or poled-mounted cameras [1], [2], [3]. Once are detected, deep-learning local regression methods, Tasselnet, used estimate in-field tassel counts [4]. However, mostly over a period of time. Consequently, the input foregoing Tasselnet technique not independent but often form unequal sequences correlated images....
Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Deep Learning Methods for Tassel Count Time-SeriesAuthorsGayaraFernandoiDVedPiyushSouparnoGhoshSee all authors Gayara FernandoiDCorresponding Author• Submitting AuthorDepartment of Statistics, University Nebraska - LincolniDhttps://orcid.org/0000-0002-9010-0210view email addressThe was not providedcopy addressVed PiyushDepartment Lincolnview addressSouparno GhoshDepartment address