Plant image recognition with deep learning: A review

被引：50

作者：

Chen, Ying ^{[1
,2
]}

Huang, Yiqi ^{[2
]}

Zhang, Zizhao ^{[1
,2
]}

Wang, Zhen ^{[1
,2
]}

Liu, Bo ^{[1
]}

Liu, Conghui ^{[1
]}

Huang, Cong ^{[1
]}

Dong, Shuangyu ^{[1
]}

Pu, Xuejiao ^{[1
]}

Wan, Fanghao ^{[1
]}

Qiao, Xi ^{[1
,2
]}

Qian, Wanqiang ^{[1
]}

机构：

[1] Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Shenzhen Branch, Guangdong Lab Lingnan Modern Agr,Genome Anal Lab,M, Shenzhen, Peoples R China

[2] Guangxi Univ, Coll Mech Engn, Nanning 530004, Peoples R China

来源：

COMPUTERS AND ELECTRONICS IN AGRICULTURE | 2023年 / 212卷

基金：

中国国家自然科学基金;

关键词：

Plant image recognition; Deep learning; Feature extraction; Data acquisition; CONVOLUTIONAL NEURAL-NETWORKS; GOOGLE STREET VIEW; DISEASE DETECTION; VISUAL DETECTION; ENSEMBLE MODEL; IDENTIFICATION; WEED; CLASSIFICATION; SEGMENTATION; PERFORMANCE;

D O I：

10.1016/j.compag.2023.108072

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

Significant advances in the field of digital image processing have been achieved in recent years using deep learning, which has significantly exceeded previous methods. Deep learning allows computers to automatically learn pattern features. Manual extraction of plant image features requires careful engineering and considerable domain expertise, so how to use deep learning technology for plant image identification studies has become a research hotspot. The following three elements are presented in this work: the various neural network structures in plant image recognition and recent research on neural network improvement methods; the way of plant image data collection and processing; three important future development directions. This review summarizes the methods used in the field of plant image recognition in the past five years, providing the latest and most practical ideas for solving problems for researchers in this field.

引用

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