Leaf area estimation in small-seeded broccoli using a lightweight instance segmentation framework based on improved YOLOv11-AreaNet
文献类型： 外文期刊
作者： Zhang, Yaben 1 ; Li, Yifan 1 ; Cao, Xiaowei 1 ; Wang, Zikun 1 ; Chen, Jiachi 1 ; Li, Yingyue 1 ; Zhong, Zhibo 2 ; Bai, Ruxiao 2 ; Yang, Peng 2 ; Pan, Feng 3 ; Fu, Xiuqing 1 ;
作者机构： 1.Nanjing Agr Univ, Coll Engn, Nanjing, Peoples R China
2.Xinjiang Acad Agr Reclamat Sci, Inst Farmland Water Conservancy & Soil Fertilizer, Shihezi, Xinjiang, Peoples R China
3.Xinjiang Acad Agr Reclamat Sci, Inst Mech Equipment, Shihezi, Xinjiang, Peoples R China
关键词： broccoli seedlings; improved YOLOv11; lightweight model; leaf area segmentation; plant trait quantification; smart agriculture
期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）
ISSN： 1664-462X
年卷期： 2025 年 16 卷
页码：
收录情况： SCI
摘要： Introduction Accurate leaf area quantification is vital for early phenotyping in small-seeded crops such as broccoli (Brassica oleracea var. italica), where dense, overlapping, and irregular foliage makes traditional measurement methods inefficient.Methods This study presents YOLOv11-AreaNet, a lightweight instance segmentation model specifically designed for precise leaf area estimation in small-seeded broccoli seedlings. The model incorporates an EfficientNetV2 backbone, Focal Modulation, C2PSA-iRMB attention, LDConv, and CCFM modules, optimizing spatial sensitivity, multiscale fusion, and computational efficiency. A total of 6,192 germination-stage images were captured using a custom phenotyping system, from which 2,000 were selected and augmented to form a 5,000-image training set. Post-processing techniques-including morphological optimization, edge enhancement, and watershed segmentation-were employed to refine leaf boundaries and compute geometric area.Results Compared to the original YOLOv11 model, YOLOv11-AreaNet achieves comparable segmentation accuracy while significantly reducing the number of parameters by 57.4% (from 2.84M to 1.21M), floating point operations by 25.9% (from 10.4G to 7.7G), and model weight size by 51.7% (from 6.0MB to 2.9MB), enabling real-time deployment on edge devices. Quantitative validation against manual measurements showed high correlation (R-2 = 0.983), confirming the system's precision. Additionally, dynamic tracking revealed individual growth differences, with relative leaf area growth rates reaching up to 26.6% during early germination.Discussion YOLOv11-AreaNet offers a robust and scalable solution for automated leaf area measurement in small-seeded crops, supporting high-throughput screening and intelligent crop monitoring under real-world agricultural conditions.
- 相关文献
作者其他论文 更多>>
-
Soil salinization prediction through feature selection and machine learning at the irrigation district scale
作者：Xie, Junbo;Liu, Yang;Wang, Qi;Xie, Junbo;Liu, Yang;Wang, Qi;Zhong, Zhibo;He, Shuai;Wang, Xingpeng;Shi, Cong;Zhong, Zhibo;He, Shuai;Zhong, Zhibo;He, Shuai
关键词：remote sensing; Landsat 8; agricultural sustainability; soil salinity; machine learning; feature selection
-
Biocontrol activity and antifungal mechanisms of volatile organic compounds produced by Trichoderma asperellum XY101 against pear Valsa canker
作者：Zhang, Yang;Tang, Jian-Hong;He, Yujian;Zhang, Yang;Wu, Li;Lu, Ying;Li, Bo;Jin, Zhaoyang;Wang, Jun;Bai, Ruxiao;Wu, Qifeng;Fan, Qinglu;Yin, Feihu;He, Yujian;Wang, Jun;Bai, Ruxiao;Wu, Qifeng;Fan, Qinglu;Yin, Feihu;He, Yujian;Wang, Jun;Bai, Ruxiao;Wu, Qifeng;Fan, Qinglu;Yin, Feihu;He, Yujian
关键词：
Valsa pyri ;Trichoderma asperellum ; volatile organic compounds; antifungal activity; transcriptomics -
Full-time sequence assessment of okra seedling vigor under salt stress based on leaf area and leaf growth rate estimation using the YOLOv11-HSECal instance segmentation model
作者：Cao, Xiaowei;Li, Yifan;Zhang, Yaben;Fu, Xiuqing;Zhong, Zhibo;Bai, Ruxiao;Yang, Peng;Pan, Feng
关键词：YOLOv11-HSECal model; okra; salt stress; time-series; leaf area; leaf growth rate; plant vitality evaluation
-
Design and Field Experiment of Synchronous Hole Fertilization Device for Maize Sowing
作者：Pan, Feng;Chen, Jincheng;Wang, Baiwei;Fang, Ziheng;Liang, Jinxin;He, Kangkang;Ji, Chao;Pan, Feng;Chen, Jincheng;Wang, Baiwei;Fang, Ziheng;Liang, Jinxin;He, Kangkang;Ji, Chao;Fang, Ziheng;Liang, Jinxin;He, Kangkang;Ji, Chao
关键词：precision agriculture; maize; seed-fertilizer synchronous application; hole fertilizing; regulatory systems; field experiment
-
Adaptive negative-pressure control system for electric-driven seeding blowers based on a DILPSO-fuzzy PID controller
作者：He, Kangkang;Chen, Jincheng;Pan, Feng;Wang, Baiwei;Ji, Chao;He, Kangkang;He, Kangkang;Chen, Jincheng;Pan, Feng;Wang, Baiwei;Ji, Chao
关键词：Negative pressure control; Fuzzy PID; Particle swarm optimization; Adaptive control
-
YOLOv8-segANDcal: segmentation, extraction, and calculation of soybean radicle features
作者：Wu, Yijie;Li, Zhengjun;Jiang, Haoyu;Li, Qianyun;Fu, Xiuqing;Guo, Biao;Qiao, Jinxin;Pan, Feng
关键词：YOLOv8; soybean germination; image segmentation; feature extraction; radicle length calculation
-
Numerical Simulation and Bench Test of Crawler-Type Cotton Time-Delay Hole-Forming Device Based on RecurDyn-EDEM Coupling
作者：Pan, Feng;Chen, Jincheng;Zhang, Hualin;Wang, Baiwei;Jiang, Xizhen;Ji, Chao;Pan, Feng;Chen, Jincheng;Zhang, Hualin;Wang, Baiwei;Jiang, Xizhen;Ji, Chao;Zhang, Hualin;Jiang, Xizhen
关键词：cotton; crawler-type; delayed hole-forming device; coupling simulation; field experiment; soil bin test
