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

• Published in: Agronomy (Basel) Vol. 14; no. 3; p. 494
• Main Authors: Zhang, Jinmeng, Yu, Feng, Zhang, Qian, Wang, Ming, Yu, Jinying, Tan, Yarong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2024
• Subjects: Abiotic factors; Agricultural land; Agricultural production; Agriculture; Artificial intelligence; Artificial neural networks; Computational linguistics; convolutional neural network; Crop production; Crop yield; Crop yields; Crops; Deep learning; Drone aircraft; Ecosystem components; Language processing; Machine learning; Natural language interfaces; Neural networks; Platforms; Productivity; Sensors; unmanned aerial vehicle; Unmanned aerial vehicles; Weed control; weed detection; weed management; Weeds; China
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14030494

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.