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Deep-Learning-based Plant Anomaly Detection using a Drone  

Lee, Jeong-Min (Dept. of Software, Sangmyung University)
Lee, Yeong-Hun (Dept. of Software, Sangmyung University)
Choi, Nam-Ki (Dept. of Software, Sangmyung University)
Park, Heemin (Dept. of Software, Sangmyung University)
Kim, Hyun-Chul (Dept. of Software, Sangmyung University)
Publication Information
Journal of the Semiconductor & Display Technology / v.20, no.1, 2021 , pp. 94-98 More about this Journal
Abstract
As the world's population grows, the food industry becomes increasingly important. Among them, agriculture is an industry that produces stocks of people all over the world, which is very important food industry. Despite the growing importance of agriculture, however, a large number of crops are lost every year due to pests and malnutrition. So, we propose a plant anomaly detection system for managing crops incorporating deep learning and drones with various possibilities. In this paper, we develop a system that analyzes images taken by drones and GPS of the drone's movement path and visually displays them on a map. Our system detects plant anomalies with 97% accuracy. The system is expected to enable efficient crop management at low cost.
Keywords
Deep Learning; Machine Learning; Object Detection; Drone; Plant Anomaly;
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  • Reference
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