A derivative approach for efficient hydroponic vertical farm monitoring using hyperspectral vision

Abstract

Vertical indoor hydroponic farms offer sustainable solutions in land scarce countries to foster agriculture productivity for addressing growing demand. Such farms require extensive controllability of the growing conditions to ensure year round-cultivation of diverse crops within the space available. Continuous monitoring of the crops and early remedial measures are essential to ensure non-compromised, high-quality yield from these farms. Currently, most farms rely on human vision based monitoring, which is quite subjective and time-consuming and could be ineffective in identifying crop stresses at early stages. Hence, efficient management of these farms requires advanced automated systems to monitor crop health, including possible stress factors such as nutrient, water, and light deficiencies at early stages to enable timely intervention. This research, in this context, explores innovative strategies using assessment parameters such as spectral ratios and derivative reflectance derived from hyperspectral images for crop monitoring. Customized spectral index for nutrient deficiency detection and approaches for quantification of derivative spectra for stress detection are developed. These strategies can be used to rapidly detect the stresses at the early stages non-destructively (within hours in case of light and water deficiencies) and could promptly guide in timely remedial actions. The proposed method offers automation possibilities for non-invasive monitoring systems utilizing hyperspectral vision. This non-invasive imaging system integrated on a robotic platform is envisaged to revolutionize the development of unmanned indoor hydroponic farms for a sustainable future.

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