Deep-Learning-based Plant Anomaly Detection using a Drone

드론을 이용한 딥러닝 기반 식물 이상 탐지 시스템

  • 이정민 (상명대학교 소프트웨어학과) ;
  • 이영훈 (상명대학교 소프트웨어학과) ;
  • 최남기 (상명대학교 소프트웨어학과) ;
  • 박희민 (상명대학교 소프트웨어학과) ;
  • 김현철 (상명대학교 소프트웨어학과)
  • Received : 2021.03.12
  • Accepted : 2021.03.17
  • Published : 2021.03.31

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.

References

  1. Lee, S., "US agricultural situation and agricultural policy," WORLD AGRICULTURE, vol. 207, pp.69-89, 2017.
  2. Protecting ROI: Farmers factor fungicide needs into crop costs. [online]. Available: https://www.agupdate.com/iowafarmertoday/news/crop/protecting-roi-farmersfactor-fungicide-needs-into-crop-costs/article_7910cb94-03c8-11e9-aae2-e7ee98c7df72.html.
  3. Aerial Crop Disease Drone Project Receives Gates Foundation Grant. [online]. Available: https://cfaes.osu.edu/news/articles/aerial-crop-disease-drone-projectreceives-gates-foundation-grant.
  4. Farmers are using unmanned aerial vehicles to assess plant health. [online]. Available: https://cultivateconnections.org/dekalb-county-ag-drones/.
  5. OpenCV. [online]. Available: https://opencv.org//
  6. Redmon, J., and Farhadi, A., "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
  7. Leaflet. [online]. Available: https://leafletjs.com.
  8. PlantVillage-Dataset. [online]. Available: https://github.com/spMohanty/PlantVillageDataset/tree/master/raw/color.
  9. Redmon, J., and Farhadi, A., "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
  10. AlexeyAB, Yolo_mark. [online]. Available: https://github.com/AlexeyAB/Yolo_mark.
  11. GPS Accuracy. [online]. Available: https://www.gps.gov/systems/gps/performance/accuracy/.