SADE: Android spectral reflectance estimator application using Wiener estimation to estimate sambiloto leaf's age

Abstract

This research proposes an Android application to estimate sambiloto (Andrographis paniculata) leaf's age from its estimated spectral reflectance using Wiener estimation. Sambiloto is one of Indonesia's popular medicinal plant. In order to use quality plants, a quality control method, such as lab tests, must be conducted. These lab tests require the destruction of leaf samples. One promising alternative is by using image processing using Wiener estimation. Wiener estimation is a conventional method to estimate high-dimensional data from low-dimensional data, for example in this case, three-channel image (RGB) to spectral reflectance. We can quantify the sambiloto leaf's quality through its spectral data in the form of its age. This research also proposes an improvement in dataset acquisition for the Wiener estimation. In the experiment we used datasets consisting of 97 standard colors, 15 samboloto leaves, and their combination. The results shows that the 15 sambiloto leaves dataset and second polynomial order gives the best reconstructed spectral reflectance. The RMSE and GFC of this dataset are 3.57 and 0.99, which is better than several previous researches. We use Probabilistic Neural Network for classifying the leaf's age from its reconstructed spectral reflectance. The accuracy for the age identification using PNN is 65%.