Xuan lv , Xiaole Wang , Yu Wang , Fugui Zhang , Lu Liu , Zhenchao Wu , Yujie Liu , Yuang Yang , Xueqing Li , Liqing Chen , Yang Yang
{"title":"基于点云和作物根区定位的油菜单株高度全生命周期动态测量","authors":"Xuan lv , Xiaole Wang , Yu Wang , Fugui Zhang , Lu Liu , Zhenchao Wu , Yujie Liu , Yuang Yang , Xueqing Li , Liqing Chen , Yang Yang","doi":"10.1016/j.compag.2025.110505","DOIUrl":null,"url":null,"abstract":"<div><div>Plant height (PH) of oilseed rape, as a crucial phenotypic indicator, provides essential data for seedling diagnosis and breeding selection when accurately monitored throughout the life cycle of individual plants. However, it is a challenge to obtain precise PH measurements as rapeseed leaves and other crops shade each other after flowering. In this study, a rail-based platform equipped with LiDAR and the BeiDou differential positioning system was designed and manufactured to autonomously collect time-series point cloud of oilseed rape populations in the field. The point cloud data of oilseed rape during the regreening stage was segmented using an improved fast Euclidean clustering algorithm, followed by extraction of the root collar region via an objective function. Centered on the identified root collar region, an adaptive plant envelope area (PEA) was generated based on the distance between adjacent plants to isolate individual rapeseed specimens. Within the PEA corresponding to each plant’s root collar region, the ground position during sowing and the canopy apex at distinct growth stages were precisely localized, enabling automated extraction of individual PH across the full life cycle. The coefficient of determination (<em>R</em><sup>2</sup>) between the algorithm and the manual measurement results at 140, 150 and 165 days after sowing were 0.9742, 0.9667, and 0.9208, respectively. And Root Mean Square Error (<em>RMSE</em>) between the algorithm and the manual measurement results at 140, 150 and 165 days were 0.038, 0.043 and 0.061 m, respectively. These results confirm that integrating BeiDou positioning with 3D point cloud processing achieves high-precision phenotyping of crop height dynamics. Furthermore, PHs were applied to frost damage and lodging susceptibility analysis, which indicate that the growth rate of rapeseed slows down as the severity of frost damage increases, and plants that reach a height of approximately 1 m during the flowering stage are prone to lodging after rainfall. These results have the potential to provide guidance for frost damage assessment and variety selection in smart agriculture applications.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110505"},"PeriodicalIF":8.9000,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic whole-life cycle measurement of individual plant height in oilseed rape through the fusion of point cloud and crop root zone localization\",\"authors\":\"Xuan lv , Xiaole Wang , Yu Wang , Fugui Zhang , Lu Liu , Zhenchao Wu , Yujie Liu , Yuang Yang , Xueqing Li , Liqing Chen , Yang Yang\",\"doi\":\"10.1016/j.compag.2025.110505\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Plant height (PH) of oilseed rape, as a crucial phenotypic indicator, provides essential data for seedling diagnosis and breeding selection when accurately monitored throughout the life cycle of individual plants. However, it is a challenge to obtain precise PH measurements as rapeseed leaves and other crops shade each other after flowering. In this study, a rail-based platform equipped with LiDAR and the BeiDou differential positioning system was designed and manufactured to autonomously collect time-series point cloud of oilseed rape populations in the field. The point cloud data of oilseed rape during the regreening stage was segmented using an improved fast Euclidean clustering algorithm, followed by extraction of the root collar region via an objective function. Centered on the identified root collar region, an adaptive plant envelope area (PEA) was generated based on the distance between adjacent plants to isolate individual rapeseed specimens. Within the PEA corresponding to each plant’s root collar region, the ground position during sowing and the canopy apex at distinct growth stages were precisely localized, enabling automated extraction of individual PH across the full life cycle. The coefficient of determination (<em>R</em><sup>2</sup>) between the algorithm and the manual measurement results at 140, 150 and 165 days after sowing were 0.9742, 0.9667, and 0.9208, respectively. And Root Mean Square Error (<em>RMSE</em>) between the algorithm and the manual measurement results at 140, 150 and 165 days were 0.038, 0.043 and 0.061 m, respectively. These results confirm that integrating BeiDou positioning with 3D point cloud processing achieves high-precision phenotyping of crop height dynamics. Furthermore, PHs were applied to frost damage and lodging susceptibility analysis, which indicate that the growth rate of rapeseed slows down as the severity of frost damage increases, and plants that reach a height of approximately 1 m during the flowering stage are prone to lodging after rainfall. These results have the potential to provide guidance for frost damage assessment and variety selection in smart agriculture applications.</div></div>\",\"PeriodicalId\":50627,\"journal\":{\"name\":\"Computers and Electronics in Agriculture\",\"volume\":\"236 \",\"pages\":\"Article 110505\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers and Electronics in Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168169925006118\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169925006118","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
Dynamic whole-life cycle measurement of individual plant height in oilseed rape through the fusion of point cloud and crop root zone localization
Plant height (PH) of oilseed rape, as a crucial phenotypic indicator, provides essential data for seedling diagnosis and breeding selection when accurately monitored throughout the life cycle of individual plants. However, it is a challenge to obtain precise PH measurements as rapeseed leaves and other crops shade each other after flowering. In this study, a rail-based platform equipped with LiDAR and the BeiDou differential positioning system was designed and manufactured to autonomously collect time-series point cloud of oilseed rape populations in the field. The point cloud data of oilseed rape during the regreening stage was segmented using an improved fast Euclidean clustering algorithm, followed by extraction of the root collar region via an objective function. Centered on the identified root collar region, an adaptive plant envelope area (PEA) was generated based on the distance between adjacent plants to isolate individual rapeseed specimens. Within the PEA corresponding to each plant’s root collar region, the ground position during sowing and the canopy apex at distinct growth stages were precisely localized, enabling automated extraction of individual PH across the full life cycle. The coefficient of determination (R2) between the algorithm and the manual measurement results at 140, 150 and 165 days after sowing were 0.9742, 0.9667, and 0.9208, respectively. And Root Mean Square Error (RMSE) between the algorithm and the manual measurement results at 140, 150 and 165 days were 0.038, 0.043 and 0.061 m, respectively. These results confirm that integrating BeiDou positioning with 3D point cloud processing achieves high-precision phenotyping of crop height dynamics. Furthermore, PHs were applied to frost damage and lodging susceptibility analysis, which indicate that the growth rate of rapeseed slows down as the severity of frost damage increases, and plants that reach a height of approximately 1 m during the flowering stage are prone to lodging after rainfall. These results have the potential to provide guidance for frost damage assessment and variety selection in smart agriculture applications.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.