Chinese Journal of Intelligent Science and Technology ›› 2021, Vol. 3 ›› Issue (3): 304-311.doi: 10.11959/j.issn.2096-6652.202131

• Special Issue: Intelligent Object Detection and Recognition • Previous Articles     Next Articles

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

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

  1. 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)

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: 

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