Adaptive multi-year machine learning model to predict sugarcane yield

R. Canal Filho; J.P. Molin; E.R.O. Silva; M.C.F. Wei; L.G.G. Sterle

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Adaptive multi-year machine learning model to predict sugarcane yield

In: Precision agriculture '25

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R. Canal Filho

R. Canal Filho ricardocanal@usp.br

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J.P. Molin

J.P. Molin
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E.R.O. Silva

E.R.O. Silva
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M.C.F. Wei

M.C.F. Wei
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L.G.G. Sterle

L.G.G. Sterle
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Type:: Chapter

Pages:: 716–722

DOI:: https://doi.org/10.1163/9789004725232_094

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Dar Hadith al Hassania

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Machine learning and high-resolution satellite imagery can be adapted to improve crop yield predictions across seasons, addressing the limitations of single-year model calibration. In this study, a strategy was developed to recalibrate sugarcane yield predictions using Sentinel-2A products. Random forest models were calibrated for 2018 and 2019 yield data in a 32-ha sugarcane field, with predictions evaluated for the current season’s model (Yld19cs), the prior season’s model (Yld19ps), and an adaptive temporal model (Yld19ps-adap). Temporal adjustments resulted in a reduction of root mean squared error by 23% and mean absolute error by 30%. The proposed method has been demonstrated as reliable for the dataset used, providing a cost-effective, non-destructive alternative to machinery-based yield mapping, supporting historical data generation for precision agriculture.

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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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Adaptive multi-year machine learning model to predict sugarcane yield

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Precision agriculture '25

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