Grading Oil Palm Fruit Bunch using Convolution Neural Network

Abstract

Elaeis guineensis is a typical oil palm fresh fruit bunches (FFB) in Malaysia that must be harvested at the optimum ripeness. In order to effectively assess the quality of oil palm fruit (FFB), non-contact image sensing technology can provide automatic and non -destructive detection fruit itself. Oil palm fruit is harvested in conditions that FFB oil palm should not gather due to the raw fruit that looks ripe due to an error from the human vision to recognize the best state for ripe fruit. The expected FFB are ripe bunches with a yellowish and reddish outer layer and a yellow-colored mesocarp. Thus, the proposed system can determine the quality directly via an Android smartphone to speed up the recognition of oil palm FFB. Convolution Neural Network (CNN) system will use to classify types of ripeness grading of the oil palm fruit via Android smartphone. The overall results for 8458 total images for four classes of oil palm fruit bunch FFB (Overripe, Ripe, Underripe, and Unripe) is 93.19% accuracy using Anaconda Jupyter Notebook and Google Colab Pro platform. The model is deployed into the Android Studio successfully by using TensorFlow Lite to build an Android application. The android application detection accuracy was 88.79% for the Samsung Galaxy S22 (SM-S901E) model with 1050ms inference time and 91% for the Samsung Galaxy A30 (SM-A305F) model with 1140ms inference time.  This model approach can assist workers to determine the maturity level of oil palm fruit bunch before making a decision.

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