Detection System of Strawberry Ripeness Using K-Means

Indra, Dolly; Satra, Ramdan; Azis, Huzain; Manga, Abdul Rachman; L, Harlinda

doi:10.33096/ilkom.v14i1.1054.25-31

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Perkembangan teknologi komputer saat ini telah membawa perubahan yang signifikan dalam cara kita bekerja, belajar, berkomunikasi, dan bersosialisasi. Dengan perkembangannya yang sangat pesat ini maka sangat membantu manusia dalam menyelesaikan pekerjaan manusia dalam berbagai bidang [2] misalnya untuk melakukan pengenalan wajah [3], sistem pendeteksi kematangan buah strawberry [4], sistem pengendali pemadam kebakaran pada rumah [5], sistem pengenalan nomor plat kendaran [6]. Salah satu penerapan teknologi komputer yaitu pada bidang computer vision yang mempunyai peranan yang sangat penting untuk melakukan pengenalan objek [7].…”

Section: Pendahuluanunclassified

Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision

Indra¹,

Herman²,

Budi³

2023

Komputika

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The development of computer technology today is very helpful for humans in completing their work in various fields. One application of computer technology i.e., in the field of computer vision which has a very important role for object recognition. In this study, we designed a computer vision-based automatic vehicle counting system. The system that we created uses the MobileNetV2 Single Shot Multibox Detector (SSD) which is placed on the Raspberry Pi 4 to carry out the process of classifying cars and motorcycles and the raspberry pi 4 also functions as a system controller. This automatic vehicle counter system has been integrated between Raspberry Pi 4 and a mobile application on a smartphone where the smartphone functions to display information such as day, date, month, year and together with the number of cars and motorcycles. We tested this automatic vehicle counting system on steam services (car and motorcycle washing) for 3 days where 10 vehicles were collected every day. The test results show that the system is capable of detecting cars and motorcyles with an average accuracy rate of 46.6%.

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Section: Pendahuluanunclassified

Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision

Indra¹,

Herman²,

Budi³

2023

Komputika

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“…A common challenge lies in the need for big and complex data to solve problems effectively with deep learning. Deep learning has become a very important part of image processing [9] with many methods such as Fourier descriptor, [10], K-Means [11], chain code contour [12], Euclidean distance [13] and GLCM features [14] all used to detect objects. Object detection systems are designed to detect real-world objects using object models.…”

Section: Introductionmentioning

confidence: 99%

Classifying BISINDO Alphabet using TensorFlow Object Detection API

Hayati,

Handayani,

Irianto

et al. 2023

Ilk. J. Ilm.

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Indonesian Sign Language (BISINDO) is one of the sign languages used in Indonesia. The process of classifying BISINDO can be done by utilizing advances in computer technology such as deep learning. The use of the BISINDO letter classification system with the application of the MobileNet V2 FPNLite SSD model using the TensorFlow object detection API. The purpose of this study is to classify BISINDO letters A-Z and measure the accuracy, precision, recall, and cross-validation performance of the model. The dataset used was 4054 images with a size of consisting of 26 letter classes, which were taken by researchers by applying several research scenarios and limitations. The steps carried out are: dividing the ratio of the simulation dataset 80:20, and applying cross-validation (k-fold = 5). In this study, a real time testing using 2 scenarios was conducted, namely testing with bright light conditions of 500 lux and dim light of 50 lux with an average processing time of 30 frames per second (fps). With a simulation data set ratio of 80:20, 5 iterations were performed, the first iteration yielded a precision result of 0.758 and a recall result of 0.790, and the second iteration yielded a precision result of 0.635 and a recall result of 0.77, then obtained an accuracy score of 0.712, the third iteration provides a recall score of 0.746, the fourth iteration obtains a precision score of 0.713 and a recall score of 0.751, the fifth iteration gives a precision score of 0.742 for a fit score case and the recall score is 0.773. So, the overall average precision score is 0.712 and the overall average recall score is 0.747, indicating that the model built performs very well.

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Recent Advances in Intelligent Harvesting Robots

Yun,

An,

et al. 2024

Smart Agriculture

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Detection System of Strawberry Ripeness Using K-Means

Cited by 3 publications

References 16 publications

Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision

Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision

Classifying BISINDO Alphabet using TensorFlow Object Detection API

Recent Advances in Intelligent Harvesting Robots

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