Robot-based 3D-multispectral monitoring of soybean in a spatially heterogenous agrivoltaic environment

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ID: 321947
2026
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Abstract
Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining crop production with solar energy capture via photovoltaic panels. In-depth information on plant growth patterns within the spatially heterogenous microclimate created by APVs would enable better planning and management within such unconventional systems. Thus, the present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in canopy height, surface area, light penetration, and volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would help improve crop management within such non-homogenous cultivation systems.
Reference Key
openalex_W7169870013 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Avinash Agarwal, Christoph Jedmowski, Ilgaz Askin, Erekle Chakhvashvili, M. Meier, Joschka Neumann, Michael Quarten, Uwe Rascher, Angelina Steier, Onno Muller
Journal Journal of experimental botany
Year 2026
DOI
10.1093/jxb/erag356
URL
https://doi.org/10.1093/jxb/erag356
Keywords Keywords not found

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