Machine learning for cotton yield prediction using UAV colour images

H. Verthaim; V. Alchanatis; E. Goldshtein; O. Keisar; G. Lidor; A. Ben-Gal; G. Ravid; Y. Cohen

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Machine learning for cotton yield prediction using UAV colour images

In: Precision agriculture '25

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H. Verthaim

H. Verthaim

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V. Alchanatis

V. Alchanatis
victor@volcani.agri.gov.il

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E. Goldshtein

E. Goldshtein

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O. Keisar

O. Keisar

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G. Lidor

G. Lidor

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A. Ben-Gal

A. Ben-Gal

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G. Ravid

G. Ravid

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Y. Cohen

Y. Cohen

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Type:: Chapter

Pages:: 614–621

DOI:: https://doi.org/10.1163/9789004725232_080

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Dar Hadith al Hassania

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A pipeline for cotton yield prediction was developed based on colour aerial imagery captured during the flowering stage while minimizing annotation and labelling efforts. In the initial stage, a weak supervision approach was employed to identify and quantify cotton flowers in colour aerial images. The second stage utilized a machine learning algorithm to estimate yield. The precision, recall, and F1 score of flowers count were 0.88, 0.9 and 0.9, respectively. Furthermore, the root mean square error (RMSE) of flower count was 0.38 flowers/m². Yield prediction models resulted in an R² of 0.91 and an RMSE of 6.1 kg/ha.

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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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Machine learning for cotton yield prediction using UAV colour images

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Precision agriculture '25

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