Identifikasi penyakit tanaman menggunakan fast fourier transform dan local binary pattern dengan probabilistic neural network (studi kasus tanaman Anthurium dan padi)

Abstract

Plant diseases can result in death and decreased quality and quantity of agricultural products that are economically significant to cause losses for farmers. This research proposes a new system to identify plant disease automatically based on plant leaf image. Fast Fourier Transform (FFT) and Local Binary Patterns (LBP) are used to extract the features. There are nine features had been resulted by FFT i.e. mean, variance, different value of maximum and minimum levels, different value of maximum and mean levels, standard deviation, skewness, kurtosis, entropy, and the highest pixel value. Then these features are combined and classified using Probabilistic Neural Network. The result shows us that these features can be used to identify plants disease, best accuracy to indentify plants disease is 85.56%. The proposed system is promising since it is capable to identify disease plants species efficiently and accurately.