DOI QR코드

DOI QR Code

Development of a real-time crop recognition system using a stereo camera

  • Baek, Seung-Min (Department of Biosystems Mechanical Engineering, Chungnam National University) ;
  • Kim, Wan-Soo (Department of Biosystems Mechanical Engineering, Chungnam National University) ;
  • Kim, Yong-Joo (Department of Biosystems Mechanical Engineering, Chungnam National University) ;
  • Chung, Sun-Ok (Department of Biosystems Mechanical Engineering, Chungnam National University) ;
  • Nam, Kyu-Chul (Certification, Warranty, Inspection & Standardization Team, Korea Agricultural Machinery Industry Cooperative) ;
  • Lee, Dae Hyun (Department of Biosystems Mechanical Engineering, Chungnam National University)
  • Received : 2020.02.06
  • Accepted : 2020.05.15
  • Published : 2020.06.01

Abstract

In this study, a real-time crop recognition system was developed for an unmanned farm machine for upland farming. The crop recognition system was developed based on a stereo camera, and an image processing framework was proposed that consists of disparity matching, localization of crop area, and estimation of crop height with coordinate transformations. The performance was evaluated by attaching the crop recognition system to a tractor for five representative crops (cabbage, potato, sesame, radish, and soybean). The test condition was set at 3 levels of distances to the crop (100, 150, and 200 cm) and 5 levels of camera height (42, 44, 46, 48, and 50 cm). The mean relative error (MRE) was used to compare the height between the measured and estimated results. As a result, the MRE of Chinese cabbage was the lowest at 1.70%, and the MRE of soybean was the highest at 4.97%. It is considered that the MRE of the crop which has more similar distribution lower. the results showed that all crop height was estimated with less than 5% MRE. The developed crop recognition system can be applied to various agricultural machinery which enhances the accuracy of crop detection and its performance in various illumination conditions.

Keywords

References

  1. Han JB, Yang KM, Sin HS, Lee JI, Lee SJ, Kim DH, Song SH, Seo KH. 2019. Simulation based on the multibody dynamics for verification of autonomous driving control of agricultural robot. Journal of Institute of Control, Robotics, and Systems 25:902-909. [in Korean] https://doi.org/10.5302/J.ICROS.2019.19.0064
  2. Jang HK. 2012. The more environmentally robust edge detection of moving objects using improved Canny edge detector and Freeman chain code. The Journal of Korean Institute of Communications and information Sciences 37:7-42. [in Korean]
  3. Jeong SH, Choi YW, Cho GS. 2018. Basic data investigation method of crop insurance using spatial information based on UAV. Journal of Korean Society for Geospatial Information System 26:61-68. [in Korean] https://doi.org/10.7319/kogsis.2018.26.3.061
  4. KAMICO (Korean Agricultural Machinery Industry Cooperative), KSAM (Korea Agricultural Machinery). 2018. Agricultural machinery yearbook Republic of Korea. KAMICO, Cheonan, KSAM, Jeonju, Korea. [in Korean]
  5. Kim DY. 2011. Design and development of mechatronics of robotic vehicle for agricultural assistance. Master dissertation, Mokpo National Univ., Mokpo, Korea. [in Korean]
  6. Kim WS, Baek SY, Kim TJ, Kim YS, Park SU, Choi CH, Hong SJ, Kim YJ. 2019. Work load analysis for determination of the reduction gear ratio for a 78 kW all wheel drive electric tractor design. Korean Journal of Agricultural Science 45:613-627. [in Korean]
  7. Lee CH, Kim WS, Choi CH, Noh HS, Hong SJ. 2018. Analysis of trends on patents for unmanned technology used in agriculture. Korean Journal of Agricultural Science 45:114-119. https://doi.org/10.7744/KJOAS.20170063
  8. Lee DH, Kim AK, Choi CH, Kim YJ. 2019a. Study on image-based flock density evaluation of broiler chicks. Journal of Korea Institute of Information, Electronics, and Communication Technology 12:373-379. [in Korean] https://doi.org/10.17661/JKIIECT.2019.12.4.373
  9. Lee DH, Lee SH, Cho BK, Wakholi C, Seo YW, Cho SH, Lee WH. 2019b. Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network. Asian-Australasian Journal of Animal Sciences. https://doi.org/10.5713/ajas.19.0748. 2019b.
  10. Lee DS, Lee DW, Kim SD, Kim TJ, Yoo JS. 2009. Real-time moving object tracking and distance measurement system using stereo camera. Journal of Broadcast Engineering 14:366-377. [in Korean] https://doi.org/10.5909/JBE.2009.14.3.366
  11. Lee EJ, Lee KI, Kim HS, Kang BS. 2010. Development of agriculture environment monitoring system using integrated sensor module. Journal of the Korea Contents Association 10:63-71. [in Korean]
  12. Lee JP. 2017. A study on calculation of damaged crop area by unmanned aerial vehicle. Journal of the Korean Cadastre Information Association 19:15-27. [in Korean]
  13. Lee YB, Noh HK. 2018. Study on mechanization rate of field farming. Journal of Agricultural Science Chungbuk University 34:169-178. [in Korean]
  14. Na SI, Park CW, So KH, Ahn HY, Lee DK. 2018. Development of biomass evaluation model of winter crop using RGB imagery based on unmanned aerial vehicle. Korean Journal of Remote Sensing 34:709-720. [in Korean] https://doi.org/10.7780/kjrs.2018.34.5.1
  15. Park CS, Yang SC, Lee GJ, Lee JT, Kim HM, Park SH, Kim DH, Jung AY, Hwang SW. 2006. Spatial variability of soil moisture content, soil penetration resistance and crop yield on the leveled upland in the reclaimed highland. Korean Journal of Soil Science and Fertilizer 39:123-135. [in Korean]
  16. Park JK, Park JH. 2015. Crops classification using imagery of unmanned aerial vehicle (UAV). Journal of the Korean Society of Agricultural Engineers 57:91-97. [in Korean] https://doi.org/10.5389/KSAE.2015.57.6.091
  17. Yoo LN, Hwang SC. 2018. A comparative study on the policy of Korea and Japan for improving upland farming mechanization. Journal of the Korean of Rural Planning 24:89-97. [in Korean]