ORIGINAL RESEARCH article
Front. Sens.
Sec. Sensor Networks
Volume 6 - 2025 | doi: 10.3389/fsens.2025.1625488
This article is part of the Research TopicEnhancing Tea Sprout and Pest Detection through Machine Learning and Image ProcessingView all 4 articles
MobileNetV2-Based Classification of Premium Tea Leaves for Optimized Production
Provisionally accepted
Indrarini Dyah Irawati*
Anyelia Adianggiali- Telkom University, Bandung, Indonesia
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The agricultural sector in Indonesia is one of the sectors producing a variety of food crops including tea plants. Tea (Camellia sinensis) is one of the plants that is widely consumed by the world community. In particular, black tea is one type of tea that is in great demand in Indonesia. PT Perkebunan Nusantara (PTPN) VIII Kebun Rancabali is one of the companies that take part in producing black tea plants and produces around 30 tons of black tea per day. In its production, black tea plants go through various stages to be processed into quality tea powder. It is necessary to know in advance the quality of the black tea leaves themselves before entering the processing stage to produce quality tea products. Therefore, in this research, a system for quality classification on black tea plants using Convolutional Neural Network (CNN) based on MobileNetV2 architecture was created. Based on the test scenario, the use of Adam optimizer with learning rate 0.001 achieved the highest accuracy of 97% and RMSprop optimizer achieved 96% accuracy. This research uses a dataset of 2000 images, so the accuracy results obtained are expected to reflect more reliable model performance and better generalization capabilities.
Keywords: CNN, epoch, quality, Learning Rate, MobileNetV2, black tea Initiation Data Acquistion Prediction Output Performance Assesment Testing Model Training Model Dataset Partitioning Data Preparation
Received: 09 May 2025; Accepted: 28 Jul 2025.
Copyright: © 2025 Irawati and Adianggiali. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Indrarini Dyah Irawati, Telkom University, Bandung, Indonesia
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