Breaking the barrier of human-annotated training data for machine-learning-aided plant research using aerial imagery

Abstract excerpt

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by: (1) the need for a large amount of human-annotated training data for each context in which the tool is used; and (2) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training dat...

Topics

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• Artificial Intelligence
• Crops, Agricultural
• Deep Learning
• Genomics
• Image Processing, Computer-Assisted
• Machine Learning
• Phenomics
• Phenotype

Identifiers and source

Literature Corpus work

b298a1a9-23c4-5a5c-918e-cb2fcbb3acda

DOI

10.1101/2025.04.02.646808