Application of a low-cost RGB sensor to detect basil (Ocimum basilicum L.) nutritional status at pilot scale level

Abstract

In this work, basil plants were fertilized with 0, 2.5 mM and 10 mM nitrogen (with different NO3−/NH4+ ratios), and then monitored using a low-power technique based on an optical leaf meter and a low-cost RGB sensor interfaced with an Arduino UNO board. The study aimed to investigate possible relationships between the concentration of some plant compounds (i.e., leaf chlorophyll and flavonoids content) and the nitrogen balance index, with the output data of a low-cost RGB sensor to indicate its capability in discriminating among different levels of nutrition. The data obtained underwent univariate and multivariate analysis. The univariate data analysis showed that the low-cost RGB sensor readings followed the development of the plants according to the varying applications of nitrogen. The multivariate analysis of the data showed that the indices related to plant metabolic efficiency and leaf colour were those most affected by the nitrogen levels of the solutions used. The comparison of the discrimination powers of the systems showed that both systems achieved comparable discrimination performances (85.0% and 89.4%) for plants supplied with 0 mM nitrogen solution. However, at increasing levels of nitrogen, the RGB sensor performed worse than the optical leaf meter (− 15.8% and − 8.6% for the 2.5 and 10 mM N treatments). The effect of the NO3−/NH4+ ratio could hardly be distinguished (except for the total chlorophyll resulting from the optical leaf meter readings). More data is, however, necessary to create a more robust model for future implementation of the application of such a sensor.