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

Abstract

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

CLC Number:

TP391.41，TN41

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.

Figures/Tables 7

References 31

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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	mAP
Faster R-CNN	0.496
Retinanet	0.603
CenterNet	0.551
多尺度回归算法	0.662

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

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

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