The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Development of AI and IoT-based smart farm pest prediction system: Research on application of YOLOv5 and Isolation Forest models

AI 및 IoT 기반 스마트팜 병충해 예측시스템 개발: YOLOv5 및 Isolation Forest 모델 적용 연구

  • Mi-Kyoung Park ;
  • Hyun Sim (Dept. Smart Agriculture, Sunchon National University)
  • 박미경 (순천대학교 스마트농업전공) ;
  • 심현 (국립순천대학교 스마트농업전공)
  • Received : 2024.06.30
  • Accepted : 2024.07.25
  • Published : 2024.08.31
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Abstract

In this study, we implemented a real-time pest detection and prediction system for a strawberry farm using a computer vision model based on the YOLOv5 architecture and an Isolation Forest Classifier. The model performance evaluation showed that the YOLOv5 model achieved a mean average precision (mAP 0.5) of 78.7%, an accuracy of 92.8%, a recall of 90.0%, and an F1-score of 76%, indicating high predictive performance. This system was designed to be applicable not only to strawberry farms but also to other crops and various environments. Based on data collected from a tomato farm, a new AI model was trained, resulting in a prediction accuracy of over 85% for major diseases such as late blight and yellow leaf curl virus. Compared to the previous model, this represented an improvement of more than 10% in prediction accuracy.

본 연구에서는 딸기 농장을 대상으로 YOLOv5 아키텍처를 기반으로 한 컴퓨터 비전 모델과 Isolation Forest Classifier를 적용하여 병충해를 실시간으로 감지 및 예측하는 시스템을 개발하였다. 모델 성능 평가 결과, YOLOv5 모델은 평균 정밀도(mAP 0.5) 78.7%, 정확도 92.8%, 재현율 90.0%, F1 점수 76%로 높은 예측 성능을 나타냈다. 본 시스템은 딸기 농장뿐만 아니라 다른 작물과 다양한 환경에도 적용할 수 있도록 설계되었다. 토마토 농장에서 수집된 데이터를 기반으로 새로운 AI 모델을 학습한 결과, 주요 병충해인 역병과 황화병에 대한 예측 정확도가 85% 이상으로 나타났으며, 기존 모델보다 예측 정확도가 10% 이상 향상되었다.

Keywords

Acknowledgement

본 논문은 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 지역지능화혁신인재양성사업임(IITP-2024-2020-0-01489).

References

  1. H. Sim, S. Choi, and H. Kim, "Algorithm Improvement Through AI-Based Casting Process Parameter Optimization," Journal of the Korea Institute of Electronic Communication Sciences, vol. 18, no. 3, June 2023, pp. 441-448. http://dx.doi.org/10.13067/JKIECS.2023.18.6.1321 
  2. H. Sim and H. Kim, "Development of AI-based Smart Agriculture Early Warning System," Journal of the Korea Society of Computer and Information, vol. 28, no. 12, Dec. 2023, pp. 67-77. 
  3. L. Garcia, L. Parra, J. M. Jimenez, J. Lloret, and P. Lorenz, "IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture," Sensors, vol. 20, no. 4, Feb. 2020, pp. 1042. 
  4. R. Dagar, S. Som, and S. K. Khatri, "Smart Farming-IoT in Agriculture," in Proc. 2018 Int. Conf. Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2018, pp. 1052-1056. 
  5. R. K. Jha, S. Kumar, K. Joshi, and R. Pandey, "Field monitoring using IoT in agriculture," in Proc. 2017 Int. Conf. Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India, 2017, pp. 1417-1420. 
  6. D. Davcev, K. Mitreski, S. Trajkovic, V. Nikolovski, and N. Koteli, "IoT agriculture system based on LoRaWAN," in Proc. 2018 14th IEEE Int. Workshop Factory Communication Systems (WFCS), Imperia, Italy, 2018, pp. 1-4. 
  7. I. Mat, M. R. M. Kassim, A. N. Harun, and I. M. Yusoff, "IoT in Precision Agriculture applications using Wireless Moisture Sensor Network," in Proc. 2016 IEEE Conf. Open Systems (ICOS), Langkawi, Malaysia, 2016, pp. 24-29. 
  8. T. Baranwal, Nitika, and P. K. Pateriya, "Development of IoT based smart security and monitoring devices for agriculture," in Proc. 2016 6th Int. Conf. Cloud System and Big Data Engineering (Confluence), Noida, India, 2016, pp. 597-602. 
  9. A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, "Deep Learning for Computer Vision: A Brief Review," Comput. Intell. Neurosci., vol. 2018, June 2018, pp. 7068349. 
  10. H. Tian, T. Wang, Y. Liu, X. Qiao, and Y. Li, "Computer vision technology in agricultural automation -A review," Inf. Process. Agric., vol. 7, no. 1, Mar. 2020, pp. 1-19. 
  11. D. I. Patricio and R. Rieder, "Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review," Comput. Electron. Agric., vol. 153, Nov. 2018, pp. 69-81. 
  12. P. Kaur, S. Harnal, R. Tiwari, S. Upadhyay, S. Bhatia, A. Mashat, and A. Alabdali, "Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction," Sensors, vol. 22, no. 2, Jan. 2022, pp. 575. 
  13. S. S. Patil and S. A. Thorat, "Early detection of grapes diseases using machine learning and IoT," in Proc. 2016 Second Int. Conf. Cognitive Computing and Information Processing (CCIP), Mysuru, India, 2016, pp. 604-609. 
  14. P. Karczmarek, A. Kiersztyn, and W. Pedrycz, "Fuzzy Set-Based Isolation Forest," in Proc. 2020 IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, 2020, pp. 1-6.