ANOTASI CITRA BERBASIS PYTHON UNTUK RANCANG BANGUN PERANGKAT LUNAK DETEKSI OBJEK PADA TANDAN BUAH SEGAR KELAPA SAWIT CACAT

Minarni Shiddiq, Muhammad Ikhsan Hamid, Vicky Vernando Dasta, Yohanes Dwi Saputra, Dewi Anjarwati Mahmudah, Dinda Kamia Evkha Putri, Annisya Madani, Ihsan Okta Harmailil

Abstract


Object detection can determine the existence of an object, scope and image. Object detection begins with the introduction of an object. This method can be used to automate the process of sorting and grading oil palm fresh fruit bunches (FFB) at palm oil mills, which are still done manually. Image annotations are needed in building the software so that the software can identify object features in an image, especially imager in video frames. This study aims to annotate images of oil palm FFB into 2 categories, namely normal palm and abnormal palm. This category is the standard regulation of the Minister of Agriculture No. 14 of 2013. Image acquisition is carried out by varying the position of each oil palm FFB with the top and bottom position of the fruit which is then augmented 4 times which function to multiply the image data model to be annotated. Annotation is done using the python program application, namely Labelimg. The amount of image data that has been annotated is 200 images consisting of 100 normal palm images and 100 abnormal palm images.

Keywords


Computer Vision; Annotation; Oil Palm; Python

References


1. Rachmat, E., & Cahyanti, M. (2015). Algoritma Transformasi Ruang Warna. Indie Publising: Depok.

2. Kodagali, J. A., & Balaji, S. (2012). Computer vision and image analysis based techniques for automatic characterization of fruits-a review. International Journal of Computer Applications, 50(6), 6–12.

3. Yani, R. A., Minarni, M., Saktioto, S., & Husein, I. R. (2020). Volumetric prediction of symmetrical-shaped fruits by computer vision. Science, Technology and Communication Journal, 1(1), 20–26.

4. Rohcastu, T. K., & Rahmad, C. (2019). Object Detection System Sebagai Alat Bantu Mendeteksi Objek Sekitar untuk Penyandang Tunanetra. Seminar Informatika Aplikatif Polinema, 81–88.

5. Jalled, F., & Voronkov, I. (2016). Object detection using image processing. arXiv preprint arXiv:1611.07791.

6. Defrianto, D., Shiddiq, M., Malik, U., Asyana, V., & Soerbakti, Y. (2022). Fluorescence spectrum analysis on leaf and fruit using the ImageJ software application. Science, Technology & Communication Journal, 3(1), 1–6.

7. Ningsih, S. A., Shiddiq, M., Arief, D. S., & Husein, I. R. (2020). Pengunaan Pencitraan Multispektral pada Panjang Gelombang 520 nm dan 800 nm untuk Mengevaluasi Tingkat Kematangan TBS Kelapa Sawit. Komunikasi Fisika Indonesia, 17(3), 144–149.

8. Sudrajat. (2019). KELAPA SAWIT: Prospek Pengembangan dan Peningkatan Produktivitas. IPB Press: Bogor.

9. Putri, J. L. E., Minarni, M., Candra, F., & Herman, H. (2019). Analisa Citra Hiperspektral Daun dari Tanaman Kelapa Sawit yang Mengalami Kekurangan Air Menggunakan Program Matlab. Komunikasi Fisika Indonesia, 16(2), 143–148.

10. Nugroho, A. (2019). Buku Teknologi Agroindustri Kelapa Sawit. Lambung Mengkurat Universitas Press: Banjarmasin.




DOI: http://dx.doi.org/10.31258/jkfi.20.2.135-140

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Indexing by:

  

 

Image