All Issue

2023 Vol.32, Issue 4 Preview Page

Original Articles

31 October 2023. pp. 434-441
Boyacı S., and H. Küçükönder 2022, A research on non-destructive leaf area estimation modeling for some apple cultivars. Erwerbs-Obstbau 64:1-7. doi:10.1007/s10341-021-00619-w 10.1007/s10341-021-00619-w
Commercialization Promotion Agency for R&D Outcome (COMPA) 2019, S&T Market Report, Vol. 69. COMPA, Seoul, Korea. (in Korean)
De Lucena L.R.R., M.L.D.M.V. Leite, C.B. da Cruz Junior, J.D. Carvalho, E.R. dos Santos, and A.D.M. de Oliveira 2019, Estimation of cladode area of Nopalea cochenillifera using digital images. J Prof Assoc Cactus Dev 21:32-42. doi:10.56890/jpacd.v21i.4 10.56890/jpacd.v21i.4
Deng Y., K. Yu, X. Yao, Q. Xie, Y. Hsieh, and J. Liu 2019, Estimation of Pinus massoniana leaf area using terrestrial laser scanning. Forests 10:660. doi:10.3390/f10080660 10.3390/f10080660
Fakir M.S.A., M.A.B. Siddique, A. Islam, M.R. Ismail, and M.K. Uddin 2013, Leaf area estimation by linear regression models in pigeonpea (Cajanus cajan (L.) Millsp.). J Food Agric Environ 11:312-316.
Gang M.S., H.J. Kim, and D.W. Kim 2022, Estimation of greenhouse lettuce growth indices based on a two-stage CNN using RGB-D images. Sensors 22:5499. (in Korean) doi:10.3390/s22155499 10.3390/s2215549935898004PMC9331482
Hajjdiab H., and A. Obaid 2010, A vision-based approach for nondestructive leaf area estimation. In 2010 The 2nd Conference on Environmental Science and Information Application Technology IEEE, pp 53-56. doi:10.1109/ESIAT.2010.5568973 10.1109/ESIAT.2010.5568973
He K., X. Zhang, S. Ren, and J. Sun 2016, Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770-778. doi:10.1109/CVPR.2016.90 10.1109/CVPR.2016.9026180094
Kim S.K., Lee S.K., Lee H.J., and Lee J.K. 2017, Horticultural crop growth models for smart farms: utilization of descriptive, explanatory, and structural growth models. Magazine Korean Soc Agric Engin 59:28-37. (in Korean)
Korea Rural Economic Institute (KREI) 2006, Agriculture and Rural Economy Trends Spring 2006. KREI, Naju, Korea. (in Korean)
Korea Rural Economic Institute (KREI) 2016, A study on analyzing the realities of smart farm operations and researching development direction. KREI, Naju, Korea. (in Korean)
Launay M., and M. Guérif 2003, Ability for a model to predict crop production variability at the regional scale: an evaluation for sugar beet. Agronomie 23:135-146. doi:10.1051/agro:2002078 10.1051/agro:2002078
Mack L., F. Capezzone, S. Munz, H.P. Piepho, W. Claupein, T. Phillips, and S. Graeff‐Hönninger 2017, Nondestructive leaf area estimation for chia. Agron J 109:1960-1969. doi:10.2134/agronj2017.03.0149 10.2134/agronj2017.03.0149
Nasiri A., A. Taheri-Garavand, D. Fanourakis, Y.D. Zhang, and N. Nikoloudakis 2021, Automated grapevine cultivar identification via leaf imaging and deep convolutional neural networks: a proof-of-concept study employing primary iranian varieties. Plants 10:1628. doi:10.3390/plants10081628 10.3390/plants1008162834451673PMC8399703
National Assembly Budget Office (NABO) 2022, Current status and improvement tasks of the smart agriculture fostering project. NABO, Seoul, Korea. (in Korean)
National Information Society Agency (NIA) 2019, AI INsight Report Vol. 01. NIA, Daegu, Korea. (in Korean)
NICE Information Service Co., Ltd. (NICE) 2020, GREENPLUS Technical Analysis Report. NICE, Seoul, Korea. (in Korean)
Peksen E. 2007, Non-destructive leaf area estimation model for faba bean (Vicia faba L.). Sci Hortic 113:322-328. doi:10.1016/j.scienta.2007.04.003 10.1016/j.scienta.2007.04.003
Simonyan K., and A. Zisserman 2014, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Souza M.C., and G. Habermann 2014, Non-destructive equations to estimate the leaf area of Styrax pohlii and Styrax ferrugineus. Braz J Biol 74:222-225. doi:10.1590/1519-6984.17012 10.1590/1519-6984.1701225055106
Zhang L., Z. Xu, D. Xu, J. Ma, Y. Chen, and Z. Fu 2020, Growth monitoring of greenhouse lettuce based on a convolutional neural network. Hortic Res 7:124. doi:10.1038/s41438-020-00345-6 10.1038/s41438-020-00345-632821407PMC7395764
Zhang W. 2020, Digital image processing method for estimating leaf length and width tested using kiwifruit leaves (Actinidia chinensis Planch). PloS ONE 15:e0235499. doi:10.1371/journal.pone.0235499 10.1371/journal.pone.023549932628694PMC7337316
Zoph B., and Q.V. Le 2016, Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578.
Zoph B., V. Vasudevan, J. Shlens, and Q.V. Le 2018, Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8697-8710. doi:10.1109/CVPR.2018.00907 10.1109/CVPR.2018.00907
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Journal of Bio-Environment Control
  • Journal Title(Ko) :생물환경조절학회지
  • Volume : 32
  • No :4
  • Pages :434-441
  • Received Date : 2023-08-31
  • Revised Date : 2023-10-23
  • Accepted Date : 2023-10-26