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