基于多尺度卷积神经网络特征融合的植株叶片检测技术

李颖; 陈龙; 黄钊宏; 孙杨; 蔡国榕

doi:10.11959/j.issn.2096-6652.202131

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基于多尺度卷积神经网络特征融合的植株叶片检测技术

专刊：目标智能检测与识别 | 更新时间：2024-06-05

- 基于多尺度卷积神经网络特征融合的植株叶片检测技术
- Plant leaf detection technology based on multi-scale CNN feature fusion
- 智能科学与技术学报 2021年3卷第3期页码：304-311
- 作者机构：
  
  1. 集美大学诚毅学院，福建厦门 361000
  2. 集美大学计算机工程学院，福建厦门 361000
- 作者简介：
  
  [ "李颖（1983‒ ），女，集美大学诚毅学院副教授，主要研究方向为图像处理、机器学习、计算机视觉、无线传感网络" ]
  [ "陈龙（2000‒ ），男，集美大学诚毅学院在读，主要研究方向为机器学习、目标检测、网络通信" ]
  [ "黄钊宏（2001‒ ），男，集美大学计算机工程学院在读，主要研究方向为深度学习、目标检测，图像识别、知识图谱" ]
  [ "孙杨（1975‒ ），女，集美大学计算机工程学院讲师，主要研究方向为系统结构和计算机图形图像" ]
  [ "蔡国榕（1979‒ ），男，博士，集美大学计算机工程学院教授，主要研究方向为图像处理、机器学习、计算机视觉" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(41971424);国家自然科学基金资助项目;The National Natural Science Foundation of China(61702251);国家自然科学基金资助项目;The National Natural Science Foundation of China(61701191);国家自然科学基金资助项目;The National Natural Science Foundation of China(41871380);国家自然科学基金资助项目;The National Natural Science Foundation of China(U1605254);厦门科学技术项目;The Science and Technology Project of Xiamen(3502Z20183032);福建省中青年教师教育科研项目;The Education and Scientific Research Project for Middle-Aged and Young Teachers of Fujian Province(JT180877)
- DOI：10.11959/j.issn.2096-6652.202131
  中图分类号： TP391.41，TN41
- 网络出版日期：2021-09，
  
  纸质出版日期：2021-09-15
- 稿件说明：
移动端阅览
李颖, 陈龙, 黄钊宏, 等. 基于多尺度卷积神经网络特征融合的植株叶片检测技术[J]. 智能科学与技术学报, 2021,3(3):304-311.

Ying LI, Long CHEN, Zhaohong HUANG, et al. Plant leaf detection technology based on multi-scale CNN feature fusion[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 304-311.
李颖, 陈龙, 黄钊宏, 等. 基于多尺度卷积神经网络特征融合的植株叶片检测技术[J]. 智能科学与技术学报, 2021,3(3):304-311. DOI： 10.11959/j.issn.2096-6652.202131.

Ying LI, Long CHEN, Zhaohong HUANG, et al. Plant leaf detection technology based on multi-scale CNN feature fusion[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 304-311. DOI： 10.11959/j.issn.2096-6652.202131.

摘要

植株叶片检测是植株科学培育和精准农业过程中重要的环节之一。传统植株叶片检测的做法对操作人员的专业知识提出了较高要求，且人工成本高、耗时周期长。基于此，提出基于多尺度卷积神经网络特征融合（MCFF）的植株叶片检测技术。从深度学习技术辅助植株培育的需求出发，基于多尺度卷积神经网络特征融合，针对莲座模式植物、拟南芥和烟草3种不同类型、不同分辨率的植株进行叶片计数检测。经过与其他主流算法的比较，发现MCFF具备较高的检测精确度，平均精度均值（mAP）为0.662，实现了高度竞争的性能（AP=0.946），各项指标接近实用水平。

Abstract

Plant leaf detection is one of the essential aspects of the scientific plant breeding and precision agriculture process.The traditional practice of plant leaf detection requires professional knowledge of the operators

high labor costs

and long time-consuming cycles.The plant leaf detection technology based on multi-scale CNN feature fusion (MCFF) was proposed.Starting from the needs of deep learning technology assisted plant cultivation

a MCFF was used to detect leaf count for three different types and resolutions of rosette model plants

arabidopsisthaliana

and tobacco.Compared with the other three algorithms

the MCFF has a higher detection accuracy with an average detection rate of mAP 0.662

a highly competitive performance (AP = 0.946) has been achieved for each indicator close to the practical level.

关键词

Keywords

references

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