We have made the dataset publicly available, and it can be accessed through the following reference: Grbović Ž, Ivošević B, Buden M, Waqar R, Pajević N, Ljubičić N, et al. (2025) Integrating UAV multispectral imaging and proximal sensing for high-precision cereal crop monitoring [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15133473 .
Open resource ↗Zenodo · 10.5281/zenodo.15133473 · lines:281-306Unverified paper record
Integrating UAV multispectral imaging and proximal sensing for high-precision cereal crop monitoring.
PloS one · 22 May 2025 · 10.1371/journal.pone.0322712
Abstract
Multispectral optical data significantly enhances cereal crop monitoring by enabling precise tracking of growth stages, early detection of germination issues, and assessment of plant health. This study evaluates the potential of integrating UAV multispectral sensor with the handheld Plant-O-Meter device for high-precision crop monitoring. The aim was to determine the optimal UAV imaging timing that aligns with proximal sensor measurements to improve growth stage assessments. Experiments were conducted on 41 cereal genotypes, including ancient and modern varieties, under two nitrogen top-dress dosages across 130 plots. The top ten performing genotypes were analyzed to identify resilient varieties adaptable to climate change and evolving field conditions. Our results demonstrate that vegetation indices during booting and spike emergence stages consistently predict yield potential, offering a robust framework for early-stage yield estimation. Additionally, we provide a comparative analysis of UAV and handheld sensor data, highlighting their respective strengths and limitations. Three vegetation indices, GRDVI, NDVI and SAVI demonstrated a very strong average positive correlation: 0.957, 0.954 and 0.944 across the selected genotypes from different performance levels. The combined dataset supports improved fertilization strategies, optimized seeding cycles, and identification of genotypes with stable agronomic traits. This study underscores the synergistic potential of aerial and proximal sensing technologies for next-generation cereal crop management and precision agriculture.
Plant phenotyping relevance
UAVマルチスペクトル画像と近接センサーを統合し、画像取得時期、センサーデータの比較、植物生育段階・収量予測を評価しており、植物形質取得手法が研究の中心である。
abstractThis study evaluates the potential of integrating UAV multispectral sensor with the handheld Plant-O-Meter device for high-precision crop monitoring.
abstractAdditionally, we provide a comparative analysis of UAV and handheld sensor data, highlighting their respective strengths and limitations.
abstractvegetation indices during booting and spike emergence stages consistently predict yield potential
Code and data availability
The paper's Data Availability statement explicitly deposits the study's dataset (UAV multispectral and Plant-O-Meter phenotyping measurements) on Zenodo with a public DOI, matching an allowed URL. No separate analysis code repository is stated.
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