Early weed identification based on deep learning: A review

被引：27

作者：

Zhang, Yangkai ^{[1
,2
]}

Wang, Mengke ^{[2
]}

Zhao, Danlei ^{[3
]}

Liu, Chunye ^{[2
]}

Liu, Zhengguang ^{[2
,4
]}

机构：

[1] Northwest A&F Univ, Agr Water & Soil Engn, Yangling 712100, Shaanxi, Peoples R China

[2] Northwest A&F Univ, Key Lab Agr Soil & Water Engn Arid Semiarid Areas, Minist Educ, Xianyang 712100, Peoples R China

[3] Hebei Univ, Coll Qual & Tech Supervis, Baoding 071002, Peoples R China

[4] STS Technol Res & Dev Ctr, Chongqing 401122, Peoples R China

来源：

SMART AGRICULTURAL TECHNOLOGY | 2023年 / 3卷

关键词：

Transfer learning; Neural architecture search; Weed identification; Deep learning; Smart agriculture; CONVOLUTIONAL NEURAL-NETWORKS; CLASSIFICATION; MACHINE; CROPS;

D O I：

10.1016/j.atech.2022.100123

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

Weeds were one of the most destructive constraints on crop production and posed a significant threat to agricultural productivity. The increasing development of smart agriculture promoted the innovation and development of precise weed control techniques. With the application of deep learning in agriculture, more and more emerging technologies have been applied to weed identification. This paper reviewed recent emerging technologies based on deep learning in weed detection. First, the definition, development, and application of technologies such as transfer learning, neural architecture search, domain adaptation, knowledge distillation, and generative adjunctive neural networks are effectively summarized and explained. Next, specific cases of new technologies to solve the challenges in the field of early weed identification are provided. Finally, the technical challenges and possible future roadmap for emerging technologies are discussed and proposed. Collaboration between emerging technologies would become a more attractive development direction than upgrading single technologies in this roadmap. This review would provide a timely field survey and attract more researchers to address this interdisciplinary research issue.

引用

页数：11