Downscaling crop data from low-resolution sensors for fine-scale monitoring: the case of grape yield

G. Brunel; Y. Valloo; J.P. Gras; M. Pount; R. Girardot; B. Tisseyre

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Downscaling crop data from low-resolution sensors for fine-scale monitoring: the case of grape yield

In: Precision agriculture '25

Authors:

G. Brunel

G. Brunel guilhem.brunel@supagro.fr

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Y. Valloo

Y. Valloo
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J.P. Gras

J.P. Gras
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M. Pount

M. Pount
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R. Girardot

R. Girardot
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B. Tisseyre

B. Tisseyre
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Type:: Chapter

Pages:: 894–900

DOI:: https://doi.org/10.1163/9789004725232_117

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Dar Hadith al Hassania

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This study proposes a methodology to generate high-resolution within-field yield maps by downscaling low-resolution yield data and incorporating accessible, low-cost auxiliary variables, such as soil and vegetation characteristics from common precision agriculture services/technologies. The method leverages correlations between yield data and these variables to create a spatial extrapolation model. Results show the model adapts well to each block, with yield estimations comparable to the literature (RRMSE <28%) and improved spatial resolution (F1-score >65%), reflecting its ability to accurately map high-yield zones. Indeed, by enhancing yield map resolution, this approach empowers small-scale vineyards with limited access to expensive technologies to implement site-specific management practices to manage within-field yield variability.

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Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Downscaling crop data from low-resolution sensors for fine-scale monitoring: the case of grape yield

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Precision agriculture '25

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