Identification of maize kernel varieties based on interpretable ensemble algorithms

Identification of maize kernel varieties based on interpretable ensemble algorithms

IntroductionMaize kernel variety identification is crucial for reducing storage losses and ensuring food security. Traditional single models show limitations in processing large-scale multimodal data.MethodsThis study constructed an interpretable ensemble learning model for maize seed variety identi...

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Main Authors: Chunguang Bi, Xinhua Bi, Jinjing Liu, Hao Xie, Shuo Zhang, He Chen, Mohan Wang, Lei Shi, Shaozhong Song
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
Series:Frontiers in Plant Science
Subjects:
maize kernel
variety identification
stacking ensemble model
multimodal data
differential evolutionary algorithm
SHAP value
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2025.1511097/full
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Summary:IntroductionMaize kernel variety identification is crucial for reducing storage losses and ensuring food security. Traditional single models show limitations in processing large-scale multimodal data.MethodsThis study constructed an interpretable ensemble learning model for maize seed variety identification through improved differential evolutionary algorithm and multimodal data fusion. Morphological and hyperspectral data of maize samples were extracted and preprocessed, and three methods were used to screen features, respectively. The base learner of the Stacking integration model was selected using diversity and performance indices, with parameters optimized through a differential evolution algorithm incorporating multiple mutation strategies and dynamic adjustment of mutation factors and recombination rates. Shapley Additive exPlanation was applied for interpretable ensemble learning.ResultsThe HDE-Stacking identification model achieved 97.78% accuracy. The spectral bands at 784 nm, 910 nm, 732 nm, 962 nm, and 666 nm showed positive impacts on identification results.DiscussionThis research provides a scientific basis for efficient identification of different corn kernel varieties, enhancing accuracy and traceability in germplasm resource management. The findings have significant practical value in agricultural production, improving quality management efficiency and contributing to food security assurance.
ISSN:1664-462X

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