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Spatio-temporal stability of intelligent modeling for weed detection in tomato fields

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Listed:
  • Gómez, Adrià
  • Moreno, Hugo
  • Valero, Constantino
  • Andújar, Dionisio

Abstract

Site-specific Weed Management (SSWM) represents a shift towards precision and sustainability in agricultural weed control by applying treatments selectively. Leveraging machine learning (ML) and deep learning (DL), particularly convolutional neural networks (CNNs), enhances weed detection capabilities through automated image analysis. Challenges such as requiring extensive labeled datasets and spatio-temporal variability of weeds remain. Utilizing multi-year datasets provides an effective solution by reducing labor-intensive annotation efforts and improving model generalization across varying conditions.

Suggested Citation

  • Gómez, Adrià & Moreno, Hugo & Valero, Constantino & Andújar, Dionisio, 2025. "Spatio-temporal stability of intelligent modeling for weed detection in tomato fields," Agricultural Systems, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:agisys:v:228:y:2025:i:c:s0308521x25001349
    DOI: 10.1016/j.agsy.2025.104394
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    References listed on IDEAS

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    1. Qianjing Li & Jia Tian & Qingjiu Tian, 2023. "Deep Learning Application for Crop Classification via Multi-Temporal Remote Sensing Images," Agriculture, MDPI, vol. 13(4), pages 1-19, April.
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