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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1533206
This article is part of the Research Topic Machine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume II View all 3 articles

Recognition and Localization of Ratoon Rice Rolled Stubble Rows Based on Monocular Vision and Model Fusion

Provisionally accepted
YuanRui Li YuanRui Li 1,2Liping Xiao Liping Xiao 1,2Zhaopeng Liu Zhaopeng Liu 1,2Muhua Liu Muhua Liu 1,2Peng Fang Peng Fang 1,2*Xiongfei Chen Xiongfei Chen 1,2Jiajia Yu Jiajia Yu 1,2Jinlong Lin Jinlong Lin 1,2Jinping Cai Jinping Cai 1,2
  • 1 Jiangxi Provincial Key Laboratory of Modern Agricultural Equipment, Jiangxi Agricultural University, Nanchang, China
  • 2 Jiangxi Agricultural University, Nanchang, Jiangxi, China

The final, formatted version of the article will be published soon.

    Ratoon rice, as a high-efficiency rice cultivation mode, is widely applied around the world. Mechanical righting of rolled rice stubble can significantly improve yield in regeneration season, but lack of automation has become an important factor restricting its further promotion. In order to realize automatic navigation of the righting machine, a method of fusing an instance segmentation model and a monocular depth prediction model was used to realize monocular localization of the rolled rice stubble rows in this study. To achieve monocular depth prediction, a depth estimation model was trained on training set we made, and absolute relative error of trained model on validation set was only 7.2%. To address the problem of degradation of model's performance when migrated to other monocular cameras, based on the law of the input image's influence on model's output results, two optimization methods of adjusting inputs and outputs were used that decreased the absolute relative error from 91.9% to 8.8%. After that, we carried out model fusion experiments, which showed that CD (chamfer distance) between predicted 3D coordinates of navigation points obtained by fusing the results of the two models and labels was only 0.0990. The CD between predicted point cloud of rolled rice stubble rows and label was only 0.0174.

    Keywords: Ratoon rice, Model fusion, Depth prediction, deep learning, monocular vision

    Received: 23 Nov 2024; Accepted: 14 Jan 2025.

    Copyright: © 2025 Li, Xiao, Liu, Liu, Fang, Chen, Yu, Lin and Cai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Peng Fang, Jiangxi Agricultural University, Nanchang, 330029, Jiangxi, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.