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

Adrià Gómez, Hugo Moreno, Constantino Valero and Dionisio Andújar

Agricultural Systems, 2025, vol. 228, issue C

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.

Keywords: Tomato weeds; Site Specific Weed Management (SSWM); Deep learning; Object detection (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:228:y:2025:i:c:s0308521x25001349

DOI: 10.1016/j.agsy.2025.104394

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Agricultural Systems is currently edited by J.W. Hansen, P.K. Thornton and P.B.M. Berentsen

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