Realtime Object Detection Masa Siap Panen Tanaman Sayuran Berbasis Mobile Android Dengan Deep Learning

  • Andri Heru Saputra Universitas Islam Indonesia
  • Dhomas Hatta Fudholi Universitas Islam Indonesia
Keywords: real-time, object detection, vegetable, harvest, MobileNetV3

Abstract

Determining the harvesting period can be done visually, physically, computationally, and chemically. Since the harvesting process is crucial, late harvesting will affect post-harvest and production quality. Leafy vegetables have a relatively short ready-to-harvest period. Visual recognition of the harvesting period combined with image processing can recognize harvesting vegetables' visual characteristics. This study aims to build a deep learning-based mobile model to detect real-time vegetable plant objects such as bok choy, spinach, kale, and curly kale to determine whether these vegetables are ready for harvest. Mobile-based architecture is chosen due to latency, privacy, connectivity, and power consumption reason since there is no round-trip communication to the server. In this research, we use MobileNetV3 as the base architecture. To find the best model, we experiment using different image input size. We have obtained a maximum MAP score of 0. 705510 using a 36,000 image dataset. Furthermore, after implementing the model into the Android mobile application, we analyze the best practice in using the application to capture distance. In real-time detection usage, the detection can be done with an ideal distance of 5 cm and 10 cm.

 

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Published
2021-08-20
How to Cite
Andri Heru Saputra, & Dhomas Hatta Fudholi. (2021). Realtime Object Detection Masa Siap Panen Tanaman Sayuran Berbasis Mobile Android Dengan Deep Learning. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 647 - 655. https://doi.org/10.29207/resti.v5i4.3190
Section
Artikel Rekayasa Sistem Informasi