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July 8, 2026BMC Plant BiologyOpen Access

A Lightweight model for accurate soybean pod detection: YOLO-MobilePod

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Authors

YSYi ShiFWFei WangJSJianbo Shen

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Overview

Randomized trial demonstrates improved detection of soybean pods in natural environments, indicating enhanced agricultural monitoring.

Key Points

  • This study aims to develop a lightweight and efficient model for detecting soybean pods with high accuracy despite their morphological variability.
  • Developed YOLO-MobilePod based on YOLOv12 using MobileNetV4 as the backbone network.
  • Implemented high-resolution feature fusion and dynamic convolution to enhance detection capabilities.
  • Conducted ablation and comparative experiments on integrated datasets.
  • YOLO-MobilePod achieved a 2.07% increase in recall and a 4.10% increase in mAP50-95 compared to YOLOv12n.
  • Model size was reduced by 38.18%, FLOPs by 4.762%, and number of parameters by 45.31%.
  • Inference speed improved by 8.768%.

Cite This Study

Shi et al. (2026) studied this question.

synapsesocial.com/papers/6a4de8ebd2ea289ef6283680https://doi.org/10.1186/s12870-026-09336-6
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