Recognition and localization of ratoon rice rolled stubble rows based on monocular vision and model fusion
IntroductionRatoon 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.MethodsI...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1533206/full |
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| author | Yuanrui Li Yuanrui Li Liping Xiao Liping Xiao Zhaopeng Liu Zhaopeng Liu Muhua Liu Muhua Liu Peng Fang Peng Fang Xiongfei Chen Xiongfei Chen Jiajia Yu Jiajia Yu Jinlong Lin Jinlong Lin Jinping Cai Jinping Cai |
| author_facet | Yuanrui Li Yuanrui Li Liping Xiao Liping Xiao Zhaopeng Liu Zhaopeng Liu Muhua Liu Muhua Liu Peng Fang Peng Fang Xiongfei Chen Xiongfei Chen Jiajia Yu Jiajia Yu Jinlong Lin Jinlong Lin Jinping Cai Jinping Cai |
| author_sort | Yuanrui Li |
| collection | DOAJ |
| description | IntroductionRatoon 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.MethodsIn 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.ResultsTo 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. |
| format | Article |
| id | doaj-art-b4751941ca2347a1993aea15a8204cac |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-b4751941ca2347a1993aea15a8204cac2025-01-31T06:41:04ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011610.3389/fpls.2025.15332061533206Recognition and localization of ratoon rice rolled stubble rows based on monocular vision and model fusionYuanrui Li0Yuanrui Li1Liping Xiao2Liping Xiao3Zhaopeng Liu4Zhaopeng Liu5Muhua Liu6Muhua Liu7Peng Fang8Peng Fang9Xiongfei Chen10Xiongfei Chen11Jiajia Yu12Jiajia Yu13Jinlong Lin14Jinlong Lin15Jinping Cai16Jinping Cai17College of Engineering, Jiangxi Agricultural University, Nanchang, ChinaJiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang, ChinaCollege of Engineering, Jiangxi Agricultural University, Nanchang, ChinaJiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang, ChinaCollege of Engineering, Jiangxi Agricultural University, Nanchang, ChinaJiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang, ChinaCollege of Engineering, Jiangxi Agricultural University, Nanchang, ChinaJiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang, ChinaCollege of Engineering, Jiangxi Agricultural University, Nanchang, ChinaJiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang, ChinaCollege of Engineering, Jiangxi Agricultural University, Nanchang, ChinaJiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang, ChinaCollege of Engineering, Jiangxi Agricultural University, Nanchang, ChinaJiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang, ChinaCollege of Engineering, Jiangxi Agricultural University, Nanchang, ChinaJiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang, ChinaCollege of Engineering, Jiangxi Agricultural University, Nanchang, ChinaJiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang, ChinaIntroductionRatoon 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.MethodsIn 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.ResultsTo 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.https://www.frontiersin.org/articles/10.3389/fpls.2025.1533206/fullratoon ricemodel fusiondepth predictiondeep learningmonocular vision |
| spellingShingle | Yuanrui Li Yuanrui Li Liping Xiao Liping Xiao Zhaopeng Liu Zhaopeng Liu Muhua Liu Muhua Liu Peng Fang Peng Fang Xiongfei Chen Xiongfei Chen Jiajia Yu Jiajia Yu Jinlong Lin Jinlong Lin Jinping Cai Jinping Cai Recognition and localization of ratoon rice rolled stubble rows based on monocular vision and model fusion Frontiers in Plant Science ratoon rice model fusion depth prediction deep learning monocular vision |
| title | Recognition and localization of ratoon rice rolled stubble rows based on monocular vision and model fusion |
| title_full | Recognition and localization of ratoon rice rolled stubble rows based on monocular vision and model fusion |
| title_fullStr | Recognition and localization of ratoon rice rolled stubble rows based on monocular vision and model fusion |
| title_full_unstemmed | Recognition and localization of ratoon rice rolled stubble rows based on monocular vision and model fusion |
| title_short | Recognition and localization of ratoon rice rolled stubble rows based on monocular vision and model fusion |
| title_sort | recognition and localization of ratoon rice rolled stubble rows based on monocular vision and model fusion |
| topic | ratoon rice model fusion depth prediction deep learning monocular vision |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1533206/full |
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