한국농공학회지 (Magazine of the Korean Society of Agricultural Engineers)

한국농공학회 (The Korean Society of Agricultural Engineers)
권호 표지

수직농장을 위한 저비용 다중 카메라 네트워크 기반 작물 표현형 분석

  • 양명균 (전북대학교 생물산업기계공학과) ;
  • 이성환 (전북대학교 농업기계공학과) ;
  • 김영진 (전북대학교 생물산업기계공학과) ;
  • 정도균 (전북대학교 생물산업기계공학과)
  • 발행 : 2024.05.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

키워드

참고문헌

  1. ‌Cho, W. J., & Yang, M. (2023). High-Throughput Plant Phenotyping System Using a Low-Cost Camera Network for Plant Factory. Agriculture, 13(10), 1874. 
  2. Li, L., Zhang, Q., & Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors, 14(11), 20078-20111. 
  3. Chen, D., Neumann, K., Friedel, S., Kilian, B., Chen, M., Altmann, T., & Klukas, C. (2014). Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. The plant cell, 26(12), 4636-4655. 
  4. ‌Chen, W. T., Yeh, Y. H. F., Liu, T. Y., & Lin, T. T. (2016). An automated and continuous plant weight measurement system for plant factory. Frontiers in plant Science, 7, 135125. 
  5. Fahlgren, N., Gehan, M. A., & Baxter, I. (2015). Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Current opinion in plant biology, 24, 93-99. 
  6. Kacira, M., & Ling, P. P. (2001). Design and development of an automated and Non-contact sensing system for continuous monitoring of plant health and growth. Transactions of the ASAE, 44(4), 989. 
  7. ‌Sonnentag, O., Hufkens, K., Teshera-Sterne, C., Young, A. M., Friedl, M., Braswell, B. H., ... & Richardson, A. D. (2012). Digital repeat photography for phenological research in forest ecosystems. Agricultural and Forest Meteorology, 152, 159-177. 
  8. Jiang, Y., Li, C., & Paterson, A. H. (2016). High throughput phenotyping of cotton plant height using depth images under field conditions. Computers and Electronics in Agriculture, 130, 57-68. 
  9. ‌Barker III, J., Zhang, N., Sharon, J., Steeves, R., Wang, X., Wei, Y., & Poland, J. (2016). Development of a field-based high-throughput mobile phenotyping platform. Computers and Electronics in Agriculture, 122, 74-85. 
  10. ‌Ge, Y., Bai, G., Stoerger, V., & Schnable, J. C. (2016). Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Computers and Electronics in Agriculture, 127, 625-632. 
  11. ‌Naik, H. S., Zhang, J., Lofquist, A., Assefa, T., Sarkar, S., Ackerman, D., ... & Ganapathysubramanian, B. (2017). A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant methods, 13, 1-12. 
  12. Granier, C., Aguirrezabal, L., Chenu, K., Cookson, S. J., Dauzat, M., Hamard, P., ... & Tardieu, F. (2006). PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New phytologist, 169(3), 623-635. 
  13. Hartmann, A., Czauderna, T., Hoffmann, R., Stein, N., & Schreiber, F. (2011). HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC bioinformatics, 12, 1-9. 
  14. Tisne, S., Serrand, Y., Bach, L., Gilbault, E., Ben Ameur, R., Balasse, H., ... & Loudet, O. (2013). Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. The Plant Journal, 74(3), 534-544.