Convolutional Neural Network Implementation Using the TensorFlow Library for Freshness Detection in Apples
Implementasi Convolutional Neural Network Menggunakan Library TensorFlow untuk Deteksi Kesegaran pada Apel
DOI:
https://doi.org/10.21070/ups.2354Keywords:
Android, Convolutional Neural Networks, LeNet-5, Artificial Intelligence, Fruit Freshness DetectionAbstract
Artificial intelligence products such as computer vision have begun to replace the role of humans in several fields of work. Based on BPS data, the percentage of Indonesia's fruit exports grew 10.36% in February 2022 with a value of US$ 340 million. This causes the need for time efficiency when carrying out the export process for determining the quality of the fruit. The amount of time, human resources, and the lack of understanding in determining the quality of fruit are things that need to be considered. Based on these problems and questions, this research was conducted to create a project that proves computer vision can also determine fruit’s quality by applying the Convolutional Neural Network algorithm with LeNet-5 architecture. This project is an android mobile-based application that can detect the condition of the fruit whether it is suitable (fresh) or not suitable (rotten) for consumption.
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