Tahun
2011
Nama Peneliti/Mahasiswa
BENI SAID ALFARISI (G64086023)
Judul
Identifikasi Penyakit Tanaman Anthurium dan Padi menggunakan Fast Fourier Transform dan Local
Binary Pattern dengan Probabilistic Neural Network
Judul (English)
Plant disease identification using Fast Fourier Transform and Local Binary Patterns with
Probabilistic Neural Network (case study Rice and Anthurium)
Pembimbing
Yeni Herdiyeni
Abstrak/Ringkasan
ABSTRACT BENI SAID ALFARISI. Plant disease identification using Fast Fourier Transform and
Local Binary Patterns with Probabilistic Neural Network (case study Rice and Anthurium).
Supervised by YENI HERDIYENI. 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.
Keywords: fast fourier transform, local binary patterns, probabilistic neural network.
http://alihjenis.cs.ipb.ac.id