By integrating hyperspectral imaging, RGB cameras, and nighttime blue light-induced chlorophyll fluorescence, PhenoGazer captures plant growth dynamics and stress responses with high precision.
Plant phenotyping—the measurement of plant traits such as growth, vigor, and photosynthesis—remains one of agriculture’s major bottlenecks. Traditional methods are labor-intensive, time-consuming, and often miss dynamic physiological processes. Over the past decade, automated imaging systems such as PlantScreen™ and LemnaTec have improved throughput, but their reliance on large infrastructure and focus on daytime measurements limits applicability. Chlorophyll fluorescence, a proxy for photosynthetic activity, is especially valuable for detecting stress. While sun-induced fluorescence has been studied extensively, nighttime LED-induced fluorescence offers unique advantages by capturing plants’ dark-acclimated states. Based on these challenges, researchers sought to design a flexible, cost-effective system capable of continuous crop monitoring under diverse conditions.
A study (DOI: 10.1016/j.plaphe.2025.100047) published in Plant Phenomics on 28 April 2025 by Christine Yao-Yun Chang’s team, U.S. Department of Agriculture, Agricultural Research Service, Adaptive Cropping Systems Laboratory, marks a significant advancement in phenotyping technology, offering researchers and breeders a powerful tool to accelerate crop improvement and strengthen agricultural resilience.
To validate the performance of the PhenoGazer system, the research team first developed and applied several methodological evaluations before moving to application trials. The initial step involved chamber light condition evaluation, where researchers identified that even in controlled growth chambers, inconsistencies in LED illumination—caused by reflections, shadows, and angles—could bias spectral responses. To address this, a calibration routine was created to generate correction factors adaptable to various deployment environments, ensuring reliable data acquisition. Next, system reliability was tested by examining the repeatability of the movement module and spectral measurements. Using positional imaging, they confirmed that well-functioning sensors achieved motor path errors of less than 0.25 cm, while malfunctioning sensors triggered automatic corrections, highlighting the system’s ability to maintain precision during long-term monitoring. A further assessment of blue light impact radius established that chlorophyll fluorescence was only activated within a 10 cm range of the fiber optics, with no effect observed beyond 12 cm, leading to a recommended minimum spacing of 12 cm between target canopies to avoid unintended stimulation during nighttime routines. Building on these methods, the team conducted an application demonstration on soybean plants under fully watered, drought, and diseased conditions. Vegetation indices including modified NDVI, CIgreen, and PSRI were calculated across different times of day, revealing clear diurnal variations and distinct declines in canopy greenness and photosynthetic efficiency in stressed plants, particularly evident at midday. Complementary nighttime measurements of blue light-induced chlorophyll fluorescence confirmed reduced emission intensity and altered red-to-far-red ratios in droughted and diseased plants, indicating impaired photosynthetic capacity. Finally, comparisons with the commercial TraitFinder system showed strong correlations for key traits such as NDVI and PRI, validating PhenoGazer’s accuracy. Collectively, these results demonstrated the robustness, precision, and sensitivity of PhenoGazer for continuous monitoring of plant stress responses under controlled conditions.
PhenoGazer represents a significant step forward in crop research by providing day-night, automated phenotyping at a fraction of the cost of commercial systems. Its portability and modular design make it adaptable to controlled environments and potentially scalable to field trials. By capturing subtle changes in vegetation indices and fluorescence, the system allows breeders and researchers to monitor stress responses more precisely, accelerating the development of drought- and disease-resistant cultivars. Beyond research, PhenoGazer could inform digital agriculture applications, offering real-time crop health diagnostics to farmers.
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References
DOI
10.1016/j.plaphe.2025.100047
Original URL
https://doi.org/10.1016/j.plaphe.2025.100047
Funding information
MAH was supported in part by an appointment to the Agricultural Research Service (ARS) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA). CYC acknowledges support from the U.S. Department of Agriculture, Agricultural Research Service, CRIS #8042-21660-001-00D. This work was supported by a Cooperative Research And Development Agreement with JB Hyperspectral Devices, GmbH (CRADA No. 58-8042-2-029F).
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.