Monitoring agricultural ecosystems

Kasper Johansen, Antonino Maltese, Matthew McCabe

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Unmanned aerial system (UAS)-based sensing technologies are considered an invaluable component of digital agriculture through delivery of timely and accurate information that can contribute to food and water security and sustainable food production. Precision agriculture, which focuses on optimization procedures to increase food production with fewer inputs, is already benefiting from regular UAS-derived information on crop function, performance, and condition. UAS can also benefit yield prediction-related studies, further facilitating effective management and planning aspects of the production chain. While red–green–blue and multispectral sensors provide a means for many operational crop mapping and monitoring solutions, detection of plant disease and assessment of evapotranspiration of crops generally require more exploratory analysis of hyperspectral and thermal infrared data, respectively. This chapter provides an overview of UAS-based crop monitoring applications suitable for precision agriculture and demonstrates, through two separate case studies focusing on macadamia and orange trees, the UAS data processing chain and workflows required for individual tree crown delineation, crown area and tree height measurements, tree condition assessment and identification of evaporative fractions, canopy temperature, and associated water stress. This chapter concludes with a discussion of best practices for UAS-based data collection and processing, as well as an exploration of challenges and prospects for delivering valuable agricultural insights to meet the future demands for increasing food production.
Original languageEnglish (US)
Title of host publicationUnmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments
PublisherElsevier
Pages125-151
Number of pages27
ISBN (Print)9780323852838
DOIs
StatePublished - Feb 17 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-05-29

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