The aim of this study is to find out characteristics of Green Rapid lettuce with digital image processing using morphological technique on image and first order statistical feature extraction. In digital image processing, the original images were processed to be RGB image and then processed again to be grayscale one. The grayscale images were processed to be biner image so that the image can be changed into biner form (0 and 1). In morphological process, this study used edge detection, dilation, and erosion. The final stage of morphological process used feature extraction process to find out image morphological characteristics which can be described mathematically. The result of extraction showed its samples within range or average area about 9.593.622.205.915.820 pixels, perimeter about 8.427.724.410.694.360 pixels, length about 829.877.174.308.893 pixels, and width about 1.654.002.663.010.820 pixels
This study aims to determine the extraction of features contained in robusta and arabica coffee greenbeans to make objects detectable and can be drawn into mathematical numbers. The method used is the original image which is converted to RGB and then grayscaling is carried out followed by a binary process that aims to change the image into binary form (0 and 1) after the digital image processing is complete the process of extracting the characteristics of greenbean coffee based on width, height, perimeter, surface area, roundness percentage, and perimeter of each coffee greenbean so that it can be understood mathematically. The results of the binary image process carried out morphological operations, the morphological process there is an erosion and dilation process. The results of the erosion and dilation process are carried out feature extraction to get the length, height, circumference, roundness ratio and perimeter of an image image. Then the value is stored in the database as a feature extraction of each greenbean coffee.The result of feature extraction obtained from coffee greenbean samples with a mean width of 7.7 pixels, height 11, circumference 31.3, surface area 69.5, roundness percentage 89,559, and perimeter 1,674 of greenbean robusta coffee while for arabica greenbean can be width 13.2 pixels, height 18.8, circumference 51.2, surface area 199.9, percentage of roundness 94.548, and perimeter 1.6000038 with total data taken were 20 greenbean coffees.
Penelitian ini bertujuan untuk mengetahui hasil perbandingan tanaman selada menggunakan metode morfologi dan ekstrasi ciri dari proses kroping pada citra dan citra asli. Teknik kroping merupakan teknik memperkecil ukuran citra. Sedangkan citra asli merupakan citra hasil dari tangkapan alat citra digital. Pada pengolahan citra digital terdapat teknik atau metode morfologi pada citra. Sebelum tahap morfologi, citra akan di proses menggunakan 2 metode yaitu metode kroping pada citra dan citra asi atau tanpa kroping. Setelah proses kroping atau tanpa kroping citra di olah menjadi citra abu-abu (grayscale), selanjutnya citra akan di proses menggunakan deteksi tepi untuk memperoleh hasil bagian tepi pada citra. Kemudian citra diubah menjadi bentuk biner (0 dan 1) atau sering disebut citra biner. Setelah pengolahan citra digital selesai kemudian tahap morfologi, dimana terdapat proses dilasi dan erosi. Proses ini bertujuan untuk mempertebal dan menipiskan hasil citra atau untuk meningkatkan hasil pendeteksian pada citra atau objek. Tahap selanjutnya yaitu proses ekstrasi ciri, dimana proses ini bertujuan untuk memperoleh nilai matematis pada sebuah citra.Proses ekstrasi ciri pada citra dilakukan sebanyak dua kali yaitu ekstrasi ciri citra asli dan ekstrasi ciri citra kroping. Dari hasil ekstrasi dapat diperoleh nilai matematis citra dengan ketentuan luas, keliling, kebundaran, kerampingan, panjang dan lebar. Proses ekstrasi ciri yang pertama atau citra asli didapatkan nilai matematis dengan rentang atau rata-rata luas 51.667.147.953.650.600, keliling 58.594.529.654.404.700, kebundaran 1.000.149.515.265.920, kerampingan 30.741.557.873.547.900, panjang 3.496.560.371.093.740 dan lebar 8.489.952.148.437.490. Sedangkan proses ekstrasi ciri kroping pada citra didapat nilai matematis citra dengan rentang atau rata-rata luas 83.364.228.960.567.300, keliling 26.081.033.779.659.700, kebundara 10.002.171.123.342.300, kerampingan 41.652.085.165.818.800, panjang 27.072.842.968.749.900 dan lebar 7.598.551.957.031.240.
Temanggung Regency is the largest coffee producing area in Central Java. Robusta and Arabica are two types of coffee grown in this area. The manual method of sorting coffee beans is still used so the results are more subjective. Therefore, we require a system for categorizing coffee beans so that the results are more objective and reliable. This study uses K-NN classification and morphological features to recognize coffee beans based on the type and shape of the coffee bean defects. This aim of this study discovers which characteristics are better at classifying coffee beans into four categories (whole Robusta, whole Arabica, Robusta broken, and broken Arabica). A total of 110 coffee bean photos were used, with 80 images as training data, 40 images as test data, and a total of five morphological features.Our findings reveal that morphological traits may classify coffee beans into four categories with an accuracy of 62.5%, which is very good for detecting 100% whole Robusta and 90% Arabica but remains weak for recognizing broken coffee beans by type. Lean performs better in distinguishing coffee beans based on four classes, with a 70% accuracy. Morphological features outperform color features in distinguishing coffee beans based on shape defects, with an accuracy score of 83%.
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