Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

Abstract

Over the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies.

Deep Learning-Based High-Throughput Phenotyping Of Maize (Zea maysL.) Tasseling From Uas Imagery Across Environments

“…However, it is worth noting that UAS collection and analysis technologies will continue to improve in speed and accuracy for DTT, while DTA can not be further scaled or improved. More importantly many other phenotypes and predictions can simultaneously be made using the same UAS data (Gano et al 2024), such as plant height (Anderson et al 2019, Pugh et al 2018, Tirado et al 2020), yield predictions (Kumar et al 2023, Sunoj et al 2021, Barzin et al 2020), disease (Wu et al 2019, Chivasa et al 2021, De Salvio et al 2022), and further enhance our understanding of the phenome (Murray et al 2023). Nevertheless, a recent survey identified the “high cost of instruments/devices or software” and the “Lack of knowledge or trained personnel to analyze data” as important barriers to UAS adoption (Lachowiec et al 2024).…”