UAV-based LiDAR and multispectral sensors fusion for cotton yield estimation: Plant height and leaf chlorophyll content as a bridge linking remote sensing data to yield
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Le résumé fourni par la source
Accurate crop yield prediction is essential for enhancing agricultural sustainability and guiding economic policy decisions. It is effective to fuse multi-source remote sensing data to predict crop yields, but difficult to reveal the effects of physiological processes on yield estimation models, and challenging to guide crop field production and management. In this study, an innovative framework was introduced to construct plant height (PH) and leaf chlorophyll content (LCC) inversion models for UAV LiDAR and multispectral data through different strategies. PH and LCC, two key growth features affecting cotton yield, were evaluated using multiple linear regression (MLR), partial least squares regression (PLSR), and extreme gradient boosting (XGBoost) algorithms for single-feature and multi-feature fusion, respectively. The multi-feature fusion model based on the XGBoost algorithm was significantly better than the single-feature model (R²=0.744). Further optimization of the multi-feature fusion model revealed that multi-temporal growth features as input variables significantly improved the accuracy of the multi-feature fusion model compared with that based on single-temporal (R²=0.802). Shapley additive explanations (SHAP) analysis revealed the key contribution of LCC to yield formation at the flowering and boll development stage in different cotton varieties. Cluster analysis confirmed that the dynamic trends of PH and LCC were closely related to yield, indicating that PH and LCC could be used as a bridge between remote sensing data and yield. This study highlights the value of UAV-based multi-dimensional and multi-temporal data fusion of growth features in yield estimation models, enabling a deeper understanding of yield formation mechanisms and providing novel methodological tools for phenomics research and precision agriculture management. • UAV-LiDAR multi-temporal data achieved accurate reconstruction of PH by linear regression. • UAV-multispectral multi-temporal data enabled precise inversion of LCC by XGBoost algorithm. • PH and LCC dynamics data fusion provide mechanistic insights into cotton yield formation. • LCC at the flowering and boll development stage makes a key contribution to yield formation.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- UAV-based LiDAR and multispectral sensors fusion for cotton yield estimation: Plant height and leaf chlorophyll content as a bridge linking remote sensing data to yield
- Date Crossref
- 01/08/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Xinjiang Normal University pays non établi dans la noticeUniversité ou école supérieure
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Xinjiang Academy of Agricultural Sciences pays non établi dans la noticeInstitution
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Xinjiang Agricultural University pays non établi dans la noticeUniversité ou école supérieure
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Cotton Research Institute pays non établi dans la noticeStructure de recherche
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Xinjiang Academy of Agricultural and Reclamation Science pays non établi dans la noticeStructure de recherche
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Xinjiang Institute of Engineering pays non établi dans la noticeUniversité ou école supérieure
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Xinjiang University pays non établi dans la noticeUniversité ou école supérieure
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Xinjiang Key Laboratory of Crop Gene Editing and Germplasm Innovation pays non établi dans la noticeStructure de recherche
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College of Life Science Xinjiang Key Laboratory of Special Species Conservation and Regulatory Biology pays non établi dans la noticeUniversité ou école supérieure
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Ministry of Education/College of Agriculture Engineering Research Centre of Cotton pays non établi dans la noticeUniversité ou école supérieure
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State Key Laboratory of Cotton Bio-breeding and Integrated Utilization pays non établi dans la noticeStructure de recherche
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College of Smart Agriculture (Research Institute) pays non établi dans la noticeUniversité ou école supérieure
Xinjiang Normal University, Xinjiang Academy of Agricultural Sciences et Xinjiang Agricultural University, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.