Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield

Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield

Maize (<i>Zea mays</i> L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the development of a multimodal de...

Full description

Saved in:
Bibliographic Details
Main Authors: Danial Shamsuddin, Monica F. Danilevicz, Hawlader A. Al-Mamun, Mohammed Bennamoun, David Edwards
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Remote Sensing
Subjects:
machine learning
high-throughput phenotyping
uncrewed aerial vehicles
computer vision
crop breeding
Online Access:https://www.mdpi.com/2072-4292/16/21/4043
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2072-4292/16/21/4043

Similar Items