Research on Walnut Yield Estimation Based on Interpretable Machine Learning and Stacked Integration Under Different Water–Fertilizer Coupling Regimes.
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| Title: | Research on Walnut Yield Estimation Based on Interpretable Machine Learning and Stacked Integration Under Different Water–Fertilizer Coupling Regimes. |
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| Authors: | Yerzati, Yerhazi1,2,3 (AUTHOR), Xia, Qiuhao1,2,3 (AUTHOR), Luo, Langqin1,2,3 (AUTHOR), Chen, Jiaxing1,2,3,4 (AUTHOR), Qi, Jiahui1,2,3,5 (AUTHOR), Guo, Zhongzhong1,3 (AUTHOR), Zhai, Changyuan2,4 (AUTHOR), Zhang, Yunqi3,5 (AUTHOR), Zhang, Rui2,3,4 (AUTHOR) 120100041@taru.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1449. 23p. |
| Subjects: | Crop yields, Machine learning, Remote sensing, Plant canopies, Ensemble learning |
| Geographic Terms: | Xinjiang Uygur Zizhiqu (China) |
| Abstract: | Highlights: What are the main findings? Red-edge texture features showed higher correlation coefficients and SHAP importance values than traditional vegetation indices for walnut yield prediction, suggesting they may be more sensitive to canopy structural heterogeneity under varying water and fertilizer regimes. The proposed growth stage stacking ensemble (GSSE) model in this dataset achieved an R2 of 0.789, and the characteristic coefficients suggested that the oil conversion stage had the highest estimated contribution (60%) to the final prediction. What are the implications of the main findings? Identifies a growth stage for precise management, with the oil conversion period serving as a window for targeted water and fertilizer interventions to maximize resource efficiency. Enhances model reliability for decision support, as the high accuracy and interpretability of the approach provide a transparent foundation for intelligent orchard management. To overcome the limitations of traditional yield estimation methods—which are often subjective, costly, and difficult to implement at scale—this study developed a high-precision, interpretable model for predicting walnut yield by integrating multi-source remote sensing technology with interpretable machine learning. To provide a theoretical foundation for precise water and fertilizer management as well as intelligent production in walnut orchards. By employing interpretable machine learning and a multi-stage integration strategy, the model achieves not only high-precision yield estimation but also elucidates the influence pathways of water–fertilizer coupling on yield formation at a mechanistic level. This advancement offers reliable technical support and a decision-making framework for the precise management of orchards. This study focused on the Xinjiang 'Wen 185' walnut, employing field experiments with varying water and fertilizer gradients. A UAV equipped with a multispectral sensor was utilized to capture canopy images, from which vegetation indices and texture features were extracted. This process resulted in a comprehensive dataset that integrated remotely sensed features with management practices. Various machine learning algorithms, including random forest, support vector machine, partial least squares regression, and ridge regression, were applied. An innovative stacked integration model for growth stages was proposed, and the SHAP framework was incorporated to analyze feature contributions and enhance model interpretability. In this study, texture features—particularly those derived from the red-edge band—showed higher predictive importance than traditional vegetation indices. This suggests that they may be more sensitive to canopy structural heterogeneity under the tested conditions. Among the models, random forest showed numerically higher values in terms of R2 and RPD compared to the other individual models under the present dataset, achieving a validation R2 of 0.670 and an RPD of 1.836. The proposed growth stage stacking ensemble (GSSE) model further enhanced prediction accuracy, achieving validation R2 of 0.789, an RMSE of 0.494, and an RPD of 2.296. Additionally, the results revealed that texture may have a potential ability to captured canopy heterogeneity as the primary mechanism underlying yield variation, and the integration of multi-stage spectral information was associated with higher estimation accuracy in this dataset in improving estimation accuracy, with the oil conversion stage contributing up to 60% to the final prediction. [ABSTRACT FROM AUTHOR] |
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| Header | DbId: egs DbLabel: Engineering Source An: 194140974 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Research on Walnut Yield Estimation Based on Interpretable Machine Learning and Stacked Integration Under Different Water–Fertilizer Coupling Regimes. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yerzati%2C+Yerhazi%22">Yerzati, Yerhazi</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xia%2C+Qiuhao%22">Xia, Qiuhao</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Langqin%22">Luo, Langqin</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Jiaxing%22">Chen, Jiaxing</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qi%2C+Jiahui%22">Qi, Jiahui</searchLink><relatesTo>1,2,3,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Zhongzhong%22">Guo, Zhongzhong</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhai%2C+Changyuan%22">Zhai, Changyuan</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yunqi%22">Zhang, Yunqi</searchLink><relatesTo>3,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Rui%22">Zhang, Rui</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<i> 120100041@taru.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1449. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Crop+yields%22">Crop yields</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+canopies%22">Plant canopies</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Xinjiang+Uygur+Zizhiqu+%28China%29%22">Xinjiang Uygur Zizhiqu (China)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Red-edge texture features showed higher correlation coefficients and SHAP importance values than traditional vegetation indices for walnut yield prediction, suggesting they may be more sensitive to canopy structural heterogeneity under varying water and fertilizer regimes. The proposed growth stage stacking ensemble (GSSE) model in this dataset achieved an R2 of 0.789, and the characteristic coefficients suggested that the oil conversion stage had the highest estimated contribution (60%) to the final prediction. What are the implications of the main findings? Identifies a growth stage for precise management, with the oil conversion period serving as a window for targeted water and fertilizer interventions to maximize resource efficiency. Enhances model reliability for decision support, as the high accuracy and interpretability of the approach provide a transparent foundation for intelligent orchard management. To overcome the limitations of traditional yield estimation methods—which are often subjective, costly, and difficult to implement at scale—this study developed a high-precision, interpretable model for predicting walnut yield by integrating multi-source remote sensing technology with interpretable machine learning. To provide a theoretical foundation for precise water and fertilizer management as well as intelligent production in walnut orchards. By employing interpretable machine learning and a multi-stage integration strategy, the model achieves not only high-precision yield estimation but also elucidates the influence pathways of water–fertilizer coupling on yield formation at a mechanistic level. This advancement offers reliable technical support and a decision-making framework for the precise management of orchards. This study focused on the Xinjiang 'Wen 185' walnut, employing field experiments with varying water and fertilizer gradients. A UAV equipped with a multispectral sensor was utilized to capture canopy images, from which vegetation indices and texture features were extracted. This process resulted in a comprehensive dataset that integrated remotely sensed features with management practices. Various machine learning algorithms, including random forest, support vector machine, partial least squares regression, and ridge regression, were applied. An innovative stacked integration model for growth stages was proposed, and the SHAP framework was incorporated to analyze feature contributions and enhance model interpretability. In this study, texture features—particularly those derived from the red-edge band—showed higher predictive importance than traditional vegetation indices. This suggests that they may be more sensitive to canopy structural heterogeneity under the tested conditions. Among the models, random forest showed numerically higher values in terms of R2 and RPD compared to the other individual models under the present dataset, achieving a validation R2 of 0.670 and an RPD of 1.836. The proposed growth stage stacking ensemble (GSSE) model further enhanced prediction accuracy, achieving validation R2 of 0.789, an RMSE of 0.494, and an RPD of 2.296. Additionally, the results revealed that texture may have a potential ability to captured canopy heterogeneity as the primary mechanism underlying yield variation, and the integration of multi-stage spectral information was associated with higher estimation accuracy in this dataset in improving estimation accuracy, with the oil conversion stage contributing up to 60% to the final prediction. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18101449 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1449 Subjects: – SubjectFull: Crop yields Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Plant canopies Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Xinjiang Uygur Zizhiqu (China) Type: general Titles: – TitleFull: Research on Walnut Yield Estimation Based on Interpretable Machine Learning and Stacked Integration Under Different Water–Fertilizer Coupling Regimes. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yerzati, Yerhazi – PersonEntity: Name: NameFull: Xia, Qiuhao – PersonEntity: Name: NameFull: Luo, Langqin – PersonEntity: Name: NameFull: Chen, Jiaxing – PersonEntity: Name: NameFull: Qi, Jiahui – PersonEntity: Name: NameFull: Guo, Zhongzhong – PersonEntity: Name: NameFull: Zhai, Changyuan – PersonEntity: Name: NameFull: Zhang, Yunqi – PersonEntity: Name: NameFull: Zhang, Rui IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 10 Titles: – TitleFull: Remote Sensing Type: main |
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