SISTEM PENGGUNAAN YOLO V5 UNTUK MENGKLASIFIKASI TINGKAT KEMATANGAN TANDAN BUAH SEGAR KELAPA SAWIT BERBASIS ANDROID SECARA REAL TIME

Abstract

Identifying the maturity level of Fresh Fruit Bunches is one of the steps in the oil palm harvesting process. The maturity level of Fresh Fruit Bunches is an important factor that determines the quality of palm oil production. In general, farmers can check the maturity of FFB manually by direct observation. Determining the maturity of FFB manually requires time and expertise of skilled farmers to ensure proper ripeness. YOLO v5 is a machine learning method that can be used to quickly and accurately detect oil palm FFB objects. With YOLO v5, the model can recognize the shape, color and edge of each FFB training data. The software used is google collaboratory, android studio, roboflow, tensorfow lite, and drawio. For UML system design using Drawio software using 8002 FFB image dataset consisting of 5600 training data, 1600 validation data and 799 test data. The model can identify six levels of oil palm maturity consisting of raw, ripe 1, ripe 2, ripe 3, over ripe, and empty bunches. The results of the training model have an accuracy of 82% with a precision, recall and an F1 score of more 94%.