基于多尺度卷积神经网络特征融合的植株叶片检测技术

doi:10.11959/j.issn.2096-6652.202131

智能科学与技术学报 ›› 2021, Vol. 3 ›› Issue (3): 304-311.doi: 10.11959/j.issn.2096-6652.202131

• 专刊：目标智能检测与识别 • 上一篇下一篇

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¹, 陈龙¹, 黄钊宏², 孙杨², 蔡国榕²

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

修回日期:2021-08-18 出版日期:2021-09-15 发布日期:2021-09-01
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
基金资助:
国家自然科学基金资助项目(41971424);国家自然科学基金资助项目(61702251);国家自然科学基金资助项目(61701191);国家自然科学基金资助项目(41871380);国家自然科学基金资助项目(U1605254);厦门科学技术项目(3502Z20183032);福建省中青年教师教育科研项目(JT180877)

Plant leaf detection technology based on multi-scale CNN feature fusion

Ying LI¹, Long CHEN¹, Zhaohong HUANG², Yang SUN², Guorong CAI²

¹ Chengyi University College, Jimei University, Xiamen 361000, China
² College of Computer Engineering, Jimei University, Xiamen 361000, China

Revised:2021-08-18 Online:2021-09-15 Published:2021-09-01
Supported by:
The National Natural Science Foundation of China(41971424);The National Natural Science Foundation of China(61702251);The National Natural Science Foundation of China(61701191);The National Natural Science Foundation of China(41871380);The National Natural Science Foundation of China(U1605254);The Science and Technology Project of Xiamen(3502Z20183032);The Education and Scientific Research Project for Middle-Aged and Young Teachers of Fujian Province(JT180877)

摘要/Abstract

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。

关键词: 深度学习, 目标检测, 多尺度卷积神经网络特征融合, 植株叶片检测技术

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.

Key words: deep learning, object detection, multi-scale CNN feature fusion, plant leaf detection

中图分类号:

TP391.41，TN41

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.

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算法	mAP	烟草AP	莲座模式植物AP	拟南芥AP
Faster R-CNN	0.496	0.638	0.153	0.74
RetinaNet	0.603	0.7	0.212	0.875
CenterNet	0.551	0.749	0.109	0.8
多尺度回归算法	0.662	0.884	0.208	0.946

算法	准确度	平均检测时间/ms
Faster R-CNN	36.7%	159
CenterNet	87.82%	152
RetinaNet	79.14%	148
多尺度回归算法	88.73%	30.1

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

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