Edge-Guided DETR Model for Intelligent Sensing of Tomato Ripeness Under Complex Environments

Jiamin Yao (), Jianxuan Zhou, Yangang Nie, Jun Xue, Kai Lin and Liwen Tan
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Jiamin Yao: School of Computer Science and Network Engineering, Guangzhou University, Guangzhou 510006, China
Jianxuan Zhou: School of Computer Science and Network Engineering, Guangzhou University, Guangzhou 510006, China
Yangang Nie: School of Education, Guangzhou University, Guangzhou 510006, China
Jun Xue: School of Computer Science and Network Engineering, Guangzhou University, Guangzhou 510006, China
Kai Lin: School of Computer Science and Network Engineering, Guangzhou University, Guangzhou 510006, China
Liwen Tan: School of Computer Science and Network Engineering, Guangzhou University, Guangzhou 510006, China

Mathematics, 2025, vol. 13, issue 13, 1-17

Abstract: Tomato ripeness detection in open-field environments is challenged by dense planting, heavy occlusion, and complex lighting conditions. Existing methods mainly rely on color and texture cues, limiting boundary perception and causing redundant predictions in crowded scenes. To address these issues, we propose an improved detection framework called Edge-Guided DETR (EG-DETR), based on the DEtection TRansformer (DETR). EG-DETR introduces edge prior information by extracting multi-scale edge features through an edge backbone network. These features are fused in the transformer decoder to guide queries toward foreground regions, which improves detection under occlusion. We further design a redundant box suppression strategy to reduce duplicate predictions caused by clustered fruits. We evaluated our method on a multimodal tomato dataset that included varied lighting conditions such as natural light, artificial light, low light, and sodium yellow light. Our experimental results show that EG-DETR achieves an A P of 83.7% under challenging lighting and occlusion, outperforming existing models. This work provides a reliable intelligent sensing solution for automated harvesting in smart agriculture.

Keywords: DETR; object detection; intelligent sensing; transformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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