An Efficient RSM-Based Algorithm for Measuring Chlorophyll on Orchid Leaves with a Microspectrometer

As the manufacturing and the computing power of mobile devices improve, micro-instruments have more and more applications nowadays. In the field of agricultural science, a spectrometer provides a non-destructive way to extract the inner growth feature of plants, which helps to keep track of the health of crops, and helps to determine the required treatment. In this paper, we propose an algorithm that predicts the concentration of chlorophyll on orchid leaves, by analyzing the spectral data collected from leaves with a micro spectrometer. The proposed algorithm utilizes the response surface methodology for building a non-linear prediction model. For verifying our algorithm, we collect 400 spectral data from four different species of orchids, where each individual species contains 100 samples. The experimental results show that our prediction model using 8 different wavelengths achieves 0.842 and 9.14 in R² and RMSECV, respectively, which is competitive to the result of traditional approach using 43 different wavelengths.