Enabling 24-hour Agricultural Robotics: Unsupervised Day-to-Night Cross-Modal Image Translation for Nighttime Visual Navigation
Proposes an unsupervised image translation framework to convert daytime plant-row RGB images to near-infrared (NIR) nighttime counterparts without pixel supervision, enabling reuse of daytime semantic labels for training nighttime perception models. Leverages pre-trained CLIP model for semantic consistency and introduces a visibility mask for limited NIR illumination. Evaluated on AgriNight dataset (428 day, 549 night images) as the first benchmark for nighttime agricultural visual navigation. Real robot experiments confirm effectiveness.
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[Submitted on 13 Jul 2026]
Title:Enabling 24-hour Agricultural Robotics: Unsupervised Day-to-Night Cross-Modal Image Translation for Nighttime Visual Navigation
View a PDF of the paper titled Enabling 24-hour Agricultural Robotics: Unsupervised Day-to-Night Cross-Modal Image Translation for Nighttime Visual Navigation, by Robel Mamo and 3 other authors
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Abstract:While visual navigation has been extensively studied in agricultural robotics, most existing systems assume daytime conditions. In fact, deploying autonomous robots at night offers significant advantages, including 24-hour crop and soil monitoring, fruit harvesting, and nocturnal pest detection. Modern vision-based systems, however, rely heavily on large-scale well-annotated image datasets, which remains challenging to obtain for nighttime operation scenarios. To address this, we propose an unsupervised image translation framework that converts daytime plant-row RGB images into near-infrared (NIR) nighttime counterparts without requiring pixel-to-pixel supervision. This enables the direct reuse of daytime semantic labels for training nighttime perception models. In particular, by incorporating a pre-trained Contrastive Language-Image Pre-training (CLIP) model, the proposed framework is designed to preserve semantic consistency during day-to-night translation. Additionally, a visibility mask is introduced to account for the limited effective range of NIR illumination in nighttime scenes. We conduct comparative evaluations with state-of-the-art image translation baselines and demonstrate higher image qualities, as supported by improved performance in downstream semantic segmentation for nighttime visual navigation. For evaluation, we utilize AgriNight--a novel dataset comprising 428 daytime and 549 nighttime images collected using night-vision-equipped mobile robots in agricultural fields and manually annotated with pixel-wise semantic labels--and introduce it as the first benchmark for nighttime agricultural visual navigation. We also perform real-time autonomous navigation experiments with a physical robot operating at night. The data and code are available at: this https URL.
Comments: Accepted to IROS2026
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.12065 [cs.RO]
(or arXiv:2607.12065v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.12065
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Taeyeong Choi [view email] [v1] Mon, 13 Jul 2026 18:37:35 UTC (3,776 KB)
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