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Predicting alfalfa leaf area index by non-linear models and deep learning models

dc.contributor.authorSongtao Yang
dc.contributor.authorYongqi Ge
dc.contributor.authorYongqi Ge
dc.contributor.authorJing Wang
dc.contributor.authorRui Liu
dc.contributor.authorRui Liu
dc.contributor.authorLi Fu
dc.contributor.otherCollege of Information Engineering, Ningxia University, Yinchuan, China
dc.contributor.otherCollege of Information Engineering, Ningxia University, Yinchuan, China
dc.contributor.otherNingxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Yinchuan, China
dc.contributor.otherCollege of Information Engineering, Ningxia University, Yinchuan, China
dc.contributor.otherCollege of Information Engineering, Ningxia University, Yinchuan, China
dc.contributor.otherNingxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Yinchuan, China
dc.contributor.otherCollege of Resources Environment and Life Sciences, Ningxia Normal University, Guyuan, China
dc.date.accessioned2024-11-11T04:33:22Z
dc.date.available2025-10-02T04:55:08Z
dc.date.issued01-11-2024
dc.identifier.issn-
dc.identifier.urihttps://www.frontiersin.org/articles/10.3389/fpls.2024.1458337/full
dc.description.abstractLeaf area index (LAI) of alfalfa is a crucial indicator of its growth status and a predictor of yield. The LAI of alfalfa is influenced by environmental factors, and the limitations of non-linear models in integrating these factors affect the accuracy of LAI predictions. This study explores the potential of classical non-linear models and deep learning for predicting alfalfa LAI. Initially, Logistic, Gompertz, and Richards models were developed based on growth days to assess the applicability of nonlinear models for LAI prediction of alfalfa. In contrast, this study combines environmental factors such as temperature and soil moisture, and proposes a time series prediction model based on mutation point detection method and encoder-attention-decoder BiLSTM network (TMEAD-BiLSTM). The model’s performance was analyzed and evaluated against LAI data from different years and cuts. The results indicate that the TMEAD-BiLSTM model achieved the highest prediction accuracy (R² > 0.99), while the non-linear models exhibited lower accuracy (R² > 0.78). The TMEAD-BiLSTM model overcomes the limitations of nonlinear models in integrating environmental factors, enabling rapid and accurate predictions of alfalfa LAI, which can provide valuable references for alfalfa growth monitoring and the establishment of field management practices.
dc.format-
dc.language.isoEN
dc.publisherFrontiers Media S.A.
dc.relation.uri['https://microbialcellfactories.biomedcentral.com/submission-guidelines', 'https://www.springernature.com/gp/open-research/policies/journal-policies/apc-waiver-countries', 'https://microbialcellfactories.biomedcentral.com/', 'https://microbialcellfactories.biomedcentral.com/about']
dc.rights['CC BY', 'CC BY-NC-ND']
dc.subject['applied microbiology', 'Microbiology', 'QR1-502']
dc.subject.lccPlant culture
dc.titlePredicting alfalfa leaf area index by non-linear models and deep learning models
dc.typeArticle
dc.description.keywordsalfalfa
dc.description.keywordsleaf area index
dc.description.keywordsnon-liner model
dc.description.keywordsdeep learning model
dc.description.keywordsMOSUM.
dc.description.pages-
dc.description.doi10.3389/fpls.2024.1458337
dc.title.journalFrontiers in Plant Science
dc.identifier.e-issn1664-462X
dc.identifier.oaioai:doaj.org/journal:c1fea89651734462a4556263611d9515
dc.journal.info-


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