Article

Explainable machine learning models of major crop traits from satellite-monitored continent-wide field trial data

2021-03-09

Abstract excerpt

Four species of grass generate half of all human-consumed calories 1 . However, abundant biological data on species that produce our food remains largely inaccessible, imposing direct barriers to understanding crop yield and fitness traits. Here, we assemble and analyse a continent-wide database of field experiments spanning ten years and hundreds of thousands of machine-phenotyped populations of ten major crop s...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 45f8b577-2dee-5993-a541-9777dfe0e09e
DOI: 10.1101/2021.03.08.434495

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Explainable machine learning models of major crop traits from satellite-monitored continent-wide field trial dataDOI 10.1101/2021.03.08.434495Read

A multiple species, continent-wide, million-phenotype agronomic plant dataset.Semantic proximity Read

A Large Yield Model for Crop Production and Design in Western CanadaSemantic proximity Read

On to the next chapter for crop breeding: Convergence with data scienceSemantic proximity Read

On to the Next Chapter for Crop Breeding: Convergence with Data ScienceSemantic proximity Read

Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance Spectra.Semantic proximity Read

Decoding living systems: Reassessing crop model frontiers via biological dynamics and optimized phenotype.Semantic proximity Read

Machine Learning Approach for Prescriptive Plant Breeding.Semantic proximity Read

Breeding 5.0: Artificial intelligence (AI)-decoded germplasm for accelerated crop innovation.Semantic proximity Read

Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.Semantic proximity Read

Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates.Semantic proximity Read

Quantitative research from the perspective of mathematical and physical crop science: a review of phenotyping, mechanics, and modeling.Semantic proximity Read

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.Semantic proximity Read