Prospector: a mobile app for high-throughput NIRS phenotyping
Quality traits are some of the most important and time-consuming phenotypes to evaluate in plant breeding programs. These traits are often evaluated late in the breeding pipeline due to their cost, resulting in the potential advancement of many lines that are not suitable for release. Near-infrared spectroscopy (NIRS) is a non-destructive tool that can rapidly increase the speed at which quality traits are evaluated. However, most spectrometers are non-portable or prohibitively expensive. Recent advancements have led to the development of consumer-targeted, inexpensive spectrometers with demonstrated potential for breeding applications. Unfortunately, the mobile applications for these spectrometers are not designed to rapidly collect organized samples at the scale necessary for breeding programs. To that end, we developed Prospector, a mobile application that connects with LinkSquare portable NIR spectrometers and allows breeders to efficiently capture NIR data. In this report, we outline the core functionality of the app and how it can easily be integrated into breeding workflows as well as the opportunities for further development. Prospector and other high throughput phenotyping tools and technologies are required for plant breeders to develop the next generation of improved varieties necessary to feed a growing global population.
Code (0)
등록된 구현이 없습니다.
Tasks
Vocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models
Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic health records requires high throughput metho…
Language ModelingLanguage ModellingLarge Language ModelA Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential to gaining value from electronic health records (EHR) in the support of precision medicine.…
Language ModelingLanguage ModellingLarge Language ModelHemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks
We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN's decoding capabilities to decode in a streaming way…
EEGElectroencephalogram (EEG)High-Throughput Phenotyping of Clinical Text Using Large Language Models
High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This study evaluates the automation of phenotyping of clinical summaries from …
High-throughput Phenotyping of Nematode Cysts
The beet cyst nematode (BCN) Heterodera schachtii is a plant pest responsible for crop loss on a global scale. Here, we introduce a high-throughput system based on computer vision that allows quantifying BCN infestation …
Instance SegmentationSemantic SegmentationVocal Bursts Intensity Prediction