Unmanned Aerial Systems in Precision Agriculture

Technological Progresses and Applications
1. Auflage 2022
ISBN: 978-981-19-2029-5
Verlag: Springer Nature Singapore

Buch, Englisch, Band 2, 136 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 230 g

Reihe: Smart Agriculture

This book, consisting of 8 chapters, describes the state-of-the-art technological progress and applications of unmanned aerial vehicles (UAVs) in precision agriculture. It focuses on the UAV application in agriculture, such as crop disease detection, mid-season yield estimation, crop nutrient status, and high-throughput phenotyping. Different from individual papers focusing on a specific application, this book provides a holistic view for readers with a wide range of subjects. In addition to researchers in the areas of plant science, plant pathology, breeding, engineering, it is also intended for undergraduates and graduates who are interested in imaging processing, artificial intelligence in agriculture, precision agriculture, agricultural automation, and robotics.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Zhao

Liu, Hu

Jiang, Yu

Yang, Ce

Ampatzidis, Yiannis

Zhou, Jianfeng

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Naturwissenschaften Biowissenschaften Botanik
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften

Inhaltsverzeichnis

Applications of UAVs and machine learning in agriculture.- Robot Operating System Powered Data Acquisition for Unmanned Aircraft Systems in Digital Agriculture.- Unmanned aerial vehicle (UAV) applications in cotton production.- Time effect after initial wheat lodging on plot lodging ratio detection using UAV imagery and deep learning.- UAV mission height effects on wheat lodging ratio detection.- Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on An Optimized Hybrid Task Cascade Model.- UAV multispectral remote sensing for yellow rust mapping: opportunities and challenges.- Corn Goss's Wilt disease assessment based on UAV imagery.

Über Autor(innen)

Dr. Zhao Zhang earned his Ph.D. from the Department of Agricultural and Biological Engineering, The Pennsylvania State University. After conducting research as a PostDoc in USDA-ARS, he joined the Department of Agricultural and Biosystems Engineering, North Dakota State University, as Research Assistant Professor. He is now a professor with College of Information and Electrical Engineering, China Agricultural University, working in the area of smart agriculture”.

Dr. Hu Liu earned his Ph.D. from Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, and then joined the same institute as a researcher. Dr. Liu’s research topic is using remote sensing technology in agriculture.
Dr. Ce Yang earned her Ph.D. from University of Florida, and then joined the University of Minnesota at Twin Cities as a faculty member. Dr. Yang has conducted numerous research in using unmanned aerials vehicles in agriculture, which includes, but it not limited to, using drone images to monitor wheat disease and maize nitrogen status.
Dr. Yiannis Ampatzidis is a renowned professor with the Department of Agricultural and Biological Engineering, University of Florida. Dr. Ampatzidis is also a key member of the Southwest Florida Research & Education Center, located in Immokalee, FL. Dr. Ampatzidis focuses on using innovative technology on orchard management. 
Dr. Jianfeng Zhou earned his Ph.D. from Washington State University, and he is now an assistant professor with University of Missouri. Dr. Zhou has conducted a number of studies on using drone technology for both row and specialty crop management, such as apples, wheat, and cotton.
Dr. Yu Jiang earned his Ph.D. in Agricultural and Biological Engineering from University of Georgia, after which he joined the Cornell University as a faculty member. His research interests include multimodal sensing, agricultural robotics, and artificial intelligence in agriculture. He has conducted multiple projects to develop plant phenomics tools for crops such as cotton, blueberries, grapes, and apples.