Optimizing maize germination forecasts with random forest and data fusion techniques

Optimizing maize germination forecasts with random forest and data fusion techniques

Traditional methods for detecting seed germination rates often involve lengthy experiments that result in damaged seeds. This study selected the Zheng Dan-958 maize variety to predict germination rates using multi-source information fusion and a random forest (RF) algorithm. Images of the seeds and...

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Bibliographic Details
Main Authors: Lili Wu, Yuqing Xing, Kaiwen Yang, Wenqiang Li, Guangyue Ren, Debang Zhang, Huiping Fan
Format: Article
Language:English
Published: PeerJ Inc. 2024-11-01
Series:PeerJ Computer Science
Subjects:
Maize seeds
Germination rate
Non-destructive prediction
Random forest algorithm
Image processing
Online Access:https://peerj.com/articles/cs-2468.pdf
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