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. 2025 Aug 12;16:1630162. doi: 10.3389/fpls.2025.1630162

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Copyright © 2025 Parker, Celebioglu, Watson and Gepts.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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A series of images and graphs related to agricultural data analysis. Top row: A) RGB image, B) NDVI, C) Mask, D) VARI, E) DSM, F) Thermal. Bottom row: G) Graph comparing Mean NDVI (Parrot Sequoia) and Mean NDVI (RedEdge-M) for combined, drought, and control treatments. H) Graph showing the relationship between Mean NDVI and Mean VARI. I) Graph illustrating Maximum canopy height against Mean NDVI. J) Graph depicting Mean NDVI versus Mean temperature, with linear regression equations and R-squared values for each dataset. — The methods described herein can be used to extract a variety of data types from many sensors, including multispectral, RGB, and thermal cameras. In this example, four cameras were flown over a terminal drought stress experiment in chickpea, with a drought stress treatment at north and well-watered control to the south in each map: (A) A true-color RGB orthomosaic (B) NDVI, (C) NDVI-based classification layer delineating living plant material from non-living material, (D) visible atmospherically resistant index (VARI) (E) a digital surface model (DSM) topographic map, (F) thermal map of canopy temperature. (G-I) Data comparisons derived from the raster files in the previous panels. Each point represents a plot of 120 plants, with drought plots in red and well-watered plots in blue. (G) Comparison of NDVI values calculated over the field from distinct cameras, flown on separate aircraft at different altitudes, with data extracted from independently generated plot grids. The extremely strong correlation (R² > 0.99) demonstrates the high precision and reliability of these data processing and extraction methods. (H) VARI from an inexpensive RGB stock camera is strongly correlated with NDVI from a multispectral Micasense RedEdge-M camera, (I) Correlation between canopy height and mean NDVI is comparatively low, indicating that the drought stress treatment strongly affects canopy NDVI but not canopy height, which was largely established before the onset of the terminal drought stress. (J) Canopy NDVI and temperature are strongly negatively correlated. Maximum canopy height in panel I is derived from the digital surface model (DSM) shown in panel E; mean temperature is derived from the thermal reflectance map shown in panel (F) All data processing conducted through methods described here and are previously unpublished. Field management by Kay Watt and Antonia Palkovic.