AI-enhanced potato yield assessment using Sentinel-2 imaging

B. Javed; A.N.` Cambouris; L. Longchamps; P.S. Basran; M. Duchemin; N. Ziadi; S. Arnold; A. Fenech; A. Karam

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AI-enhanced potato yield assessment using Sentinel-2 imaging

于Precision agriculture '25

著者：

B. Javed

B. Javed

bilal.javed.1@ulaval.ca

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A.N.` Cambouris

A.N.` Cambouris

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L. Longchamps

L. Longchamps

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P.S. Basran

P.S. Basran

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M. Duchemin

M. Duchemin

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N. Ziadi

N. Ziadi

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S. Arnold

S. Arnold

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A. Fenech

A. Fenech

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A. Karam

A. Karam

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类型:: 章节

Page Range:: 681–687

DOI:: https://doi.org/10.1163/9789004725232_089

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Dar Hadith al Hassania

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This paper presents a hybrid approach using AI and satellite remote sensing to forecast pre-harvest potato yields. Experiments on four fields in Prince Edward Island, Canada, involved multispectral images from Sentinel-2 and ground truth data from 3 m harvest tests. Three machine learning models were tested, with the random forest model showing the best results (RRMSE=13.8%, RMAE=11.3%). The study confirmed the potential of AI and multispectral data in improving yield estimation accuracy, marking a significant step forward for digital agriculture. Future work should integrate more data sources and advanced AI methods.

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Precision agriculture '25

ISBN:: 9789004725232

出版社:: Wageningen Academic

印刷出版日期:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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AI-enhanced potato yield assessment using Sentinel-2 imaging

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