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Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

文献类型： 外文期刊

作者： Zhang, Jinmeng 1 ; Yu, Feng 1 ; Zhang, Qian 1 ; Wang, Ming 1 ; Yu, Jinying 1 ; Tan, Yarong 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Inst Data Sci & Agr Econ, Beijing 100097, Peoples R China

2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China

关键词： unmanned aerial vehicle; convolutional neural network; deep learning; weed detection; weed management

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2024 年 14 卷 3 期

页码：

收录情况： SCI

摘要： With the continuous growth of the global population and the increasing demand for crop yield, enhancing crop productivity has emerged as a crucial research objective on a global scale. Weeds, being one of the primary abiotic factors impacting crop yield, contribute to approximately 13.2% of annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology has developed rapidly and its maturity has led to widespread utilization in improving crop productivity and reducing management costs. Concurrently, deep learning technology has become a prominent tool in image recognition. Convolutional Neural Networks (CNNs) has achieved remarkable outcomes in various domains, including agriculture, such as weed detection, pest identification, plant/fruit counting, maturity grading, etc. This study provides an overview of the development of UAV platforms, the classification of UAV platforms and their advantages and disadvantages, as well as the types and characteristics of data collected by common vision sensors used in agriculture, and discusses the application of deep learning technology in weed detection. The manuscript presents current advancements in UAV technology and CNNs in weed management tasks while emphasizing the existing limitations and future trends in its development process to assist researchers working on applying deep learning techniques to weed management.

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