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DEEP LEARNING-BASED TARGET RECOGNITION AND LOCALIZATION IN CITRUS PICKING ROBOT

Yang Yu, Linhui Wang, Shuwei DENG, Zhengqi Zhou, Zhizhuang Liu
August 27, 2026
Published Date

Research Abstract & Technology Focus

Target recognition and localization of picking robots are very important in orchard environments. This paper analyzed the You Only Look Once version 8 normal (YOLOv8n) method in deep learning for citrus picking robots, introduced FasterNet, efficient multi-scale attention (EMA) mechanism, and Wise Intersection over Union (WIoU) loss function, developed an improved YOLOv8n method, and collected a citrus fruit dataset to analyze the recognition and positioning effects of the proposed methods. The selected FasterNet, EMA, and WIoU were all superior to the other types compared, and the improved YOLOv8n method achieved an accuracy of 0.893, a recall rate of 0.894, and a mean average percentage (mAP) of 0.895 for citrus recognition, outperforming other recognition methods. In terms of citrus localization, the average errors on X, Y, and Z axes were 3.56 mm, 3.54 mm, and 4.74 mm, respectively. The findings demonstrate the reliability of the proposed model, and it can be applied to actual citrus picking robots.
Artificial intelligence Robot Intersection (aeronautics) Computer science Computer vision Pattern recognition (psychology)
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What is the core focus of the research titled 'DEEP LEARNING-BASED TARGET RECOGNITION AND LOCALIZATION IN CITRUS PICKING ROBOT'?

This literature focuses on: Target recognition and localization of picking robots are very important in orchard environments. This paper analyzed the You Only Look Once version 8 normal (YOLOv8n) method in deep learning for citrus picking robots, introduced FasterNet, effici...

Are there open-source GitHub repositories related to DEEP LEARNING-BASED TARGET RECOGNITION AND LOCALIZATION IN CITRUS PICKING ROBOT?

Yes, open-source projects like Flaminis/Dalaran (Dalaran — Apache-2.0, robotics-first visualization and data infrastructure for multimodal time-series. ROS 2 native, reads existing .rrd recordings...) are actively building upon these concepts.

Which startups are commercializing the technology behind DEEP LEARNING-BASED TARGET RECOGNITION AND LOCALIZATION IN CITRUS PICKING ROBOT?

Products like Gemini Robotics ER 1.6 are bringing this to market. Their focus is: Google's SOTA robotics model for visual & spatial reasoning!.

What other academic literature is closely related to 'DEEP LEARNING-BASED TARGET RECOGNITION AND LOCALIZATION IN CITRUS PICKING ROBOT'?

Yes, highly correlated activity was mapped. An entry titled 'Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture' discusses this: Abstract Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food se...

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