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

Abstract

Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining solar energy capture via photovoltaic panels with crop production. However, in-depth information on plant growth patterns within the spatially heterogenous microclimate created by the intermittent shading of APVs is largely missing. In the present study, we implement a customized robot-mounted 3D-multispectral imaging system to closely monitor 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 plant height, 3D leaf area, light penetration, and canopy 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 enable better crop management within such non-homogenous cultivation systems.

Plant phenotyping relevance

ロボット搭載の3D・マルチスペクトル画像システムを構築・適用し、作物の形態形質とストレス状態を高精度・高スループットに取得しており、表現型取得手法が研究の中心である。

Code and data availability

The supplied blocks describe the FieldDeer robot, PlantEye F600 scans, Hortcontrol preprocessing, and R-based robust linear mixed-effects analysis, but contain no data availability statement, public repository deposit, or author-provided URL for the phenotype datasets, 3D point clouds, or analysis code. The only URL in

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