Deteksi Tingkat Kematangan Buah Tomat dengan Metode Fuzzy Logic Menggunakan Modul Kamera Raspberry PI

Abstract

Intisari — Proses pemanenan buah tomat dapat dilakukan menggunakan metode visual dengan memperhatikan warna atau ukuran dari buah. Kemajuan teknologi menggunakan bantuan komputer membuat pemanenan dan pendeteksian kematangan buah tomat semakin mudah. Informasi kematangan buah tomat dapat diperoleh dengan cara pengolahan citra dengan bantuan fuzzy logic menggunakan metode Tsukamoto. Pada penelitian ini beberapa sampel buah tomat diambil nilai RGB melalui pengolahan citra sesuai dengan tingkat kematangannya, diantaranya mentah, setengah matang, dan buah matang. Setelah nilai RGB didapat maka akan diproses ke dalam fuzzy logic untuk mendapatkan informasi kematangan melalui aturan-aturan yang telah dibuat dan diintegrasikan menggunakan Raspberry Pi. Hasil penelitian ini menunjukan bahwa kondisi yang baik untuk melakukan pendeteksian buah tomat salah satunya yaitu pada kondisi indoor 100 Lux berlatar belakang gelap. Hal ini mungkin dikarenakan tidak terdapat pantulan cahaya berlebih sehingga citra yang diambil nilai RGBnya tidak terlalu tinggi dan mengurangi terjadinya kesalahan pembacaan informasi kematangan dalam proses fuzzy logic.Kata kunci — Image Processing, Fuzzy Logic, Modul Kamera Raspberry Pi.Abstract — The process of harvesting tomatoes is usually done by the visual method by looking at the color or the size of the fruit. Technological advancements by using computer assistance make harvesting and detecting the ripening process of tomatoes easier. The information of the process of Tomato’s ripening can be obtained by the image processing by using Tsukamoto Fuzzy-Logic. In this research some samples of tomatoes’ RGB values were taken through image processing in accordance with the ripening level, between unripe, under-ripe, and ripe fruit. After the RGB value is obtained, it will be processed into fuzzy logic to obtain the ripening level information through rules that have been created and integrated by using Raspberry-Pi. The result of this research indicates that a good condition for detecting tomatoes is in 100 Lux conditions with dark background. This might be because there is no excessive light reflection, so that the RGB value of the image is not too high and reduces the error of reading the ripening level in the fuzzy logic process.Keywords— Image Processing, Fuzzy Logic, Raspberry Pi Camera Module.

Identification of tomato ripeness levels (Lycopersicum esculentum Miil) using android-based digital image processing

“…The process of taking pictures to determine the maturity level of tomatoes using Android with a minimum pixel resolution of the camera used is 8 mp [2]. The maximum distance in taking photos is 25 cm [3]. Input the original image from the folder then cropping the original image on the tomato skin aims to reduce the size of the fruit, after cropping it will resize the cutting image to 32x32 pixels to reduce the computational process [4].…”

Application Fuzzy Mamdani to Determine the Ripeness Level of Crystal Guava Fruit

“…The results obtained indicate an accuracy rate of 86.67%. Another study was conducted by Muthiati et al [10], who conducted a study on the detection of tomato ripeness. The ripeness level of the fruit is classified using fuzzy logic.…”