Drone-based weed mapping at the species level for precision weed control in maize and tomato fields

G.A. Mesías-Ruiz; J. Dorado; A.I. de Castro; I. Borra-Serrano; J.M. Peña

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Drone-based weed mapping at the species level for precision weed control in maize and tomato fields

In: Precision agriculture '25

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G.A. Mesías-Ruiz

G.A. Mesías-Ruiz null

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J. Dorado

J. Dorado null

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A.I. de Castro

A.I. de Castro null

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I. Borra-Serrano

I. Borra-Serrano null

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J.M. Peña

J.M. Peña null

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Type:: Chapter

Pages:: 363–368

DOI:: https://doi.org/10.1163/9789004725232_046

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Dar Hadith al Hassania

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Accurate weed identification is crucial for effective implementation of precision weed control strategies. This research evaluated several convolutional neural networks and vision transformer models aiming to map nine common weed species in drone-based RGB images collected in earlyseason maize and tomato fields. The Droneweed dataset (>65 000 labels) was used to train, validate and test the models. Swin-T showed most robust performance (98% F1-score) and EfficientNet-B0 the lowest computational cost, though YOLO-v8 achieved superior classification results (97.5% overall accuracy) and better speed-accuracy balance. Consequently, YOLO-v8m detector was applied to generate treatment maps, which reported high potential to reduce herbicide usage by 37% and 43% in both fields, respectively.

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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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Drone-based weed mapping at the species level for precision weed control in maize and tomato fields

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Precision agriculture '25

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