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首页> 外文会议>East Indonesia Conference on Computer and Information Technology >Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network
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Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network

机译：基于卷积神经网络深入学习的香蕉成熟分类

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Fruit ripeness is an important thing in agriculture because it determines the fruit’s quality. Determining the ripeness of the fruit that was done manually poses several weaknesses, such as takes a relatively long time, requires a lot of labor, and can cause inconsistencies. The agricultural sector is one of the essential sectors of the economy in Indonesia. However, sometimes the process of determining fruit ripeness is still done by using the manual method. The development of computer vision and machine learning technologies can be used to classify fruit ripeness automatically. This study applies the Convolutional Neural Network to classify the ripeness of the banana. The banana’s ripeness is divided into four classes: unripe/green, yellowish-green, mid-ripen, and overripe. Two pre-trained models are used, which are MobileNet V2 and NASNetMobile. The experiment was conducted using Google Colab and several libraries such as OpenCV, Tensorflow, and scikit-learn. The result shows that MobileNet V2 achieves higher accuracy and faster execution time than the NASNetMobile. The highest accuracy achieved is 96.18%.
机译：果实成熟是农业的重要事项，因为它决定了水果的质量。确定完成的水果的成熟，手动摆在几个弱点，例如需要相对较长的时间，需要大量的劳动力，并且可能导致不一致。农业部门是印度尼西亚经济的基本领域之一。然而，有时通过使用手动方法来完成确定果实成熟的过程。计算机视觉和机器学习技术的开发可用于自动对果实成熟进行分类。本研究适用于卷积神经网络来分类香蕉的成熟。香蕉的成熟分为四类：未成熟/绿色，黄绿色，中成熟和覆盖物。使用两个预先接受的模型，它是MobileNet v2和NasnetMobile。使用谷歌COLAB和几个库进行实验，例如OpenCV，Tensorflow和Scikit-reash。结果表明，MobileNet V2实现比NASNetMobile更高的准确性和更快的执行时间。实现的最高精度为96.18％。

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