All Issue

2024 Vol.33, Issue 1 Preview Page

Original Articles

31 January 2024. pp. 63-70
Arakeri M.P. 2016, Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry. Procedia Comput Sci 79:426-433. doi:10.1016/j.procs.2016.03.055 10.1016/j.procs.2016.03.055
Barnes M., T. Duckett, G. Cielniak, G. Stroud, and G. Harper 2010, Visual detection of blemishes in potatoes using minimalist boosted classifiers. J Food Eng 98:339-346. doi:10.1016/j.jfoodeng.2010.01.010 10.1016/j.jfoodeng.2010.01.010
Bernotas G., L.C. Scorza, M.F. Hansen, I.J. Hales, K.J. Halliday, L.N. Smith, M.L. Smith, and A.J. McCormick 2019, A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth. GigaScience 8(5):giz056. doi:10.1093/gigascience/giz056 10.1093/gigascience/giz05631127811PMC6534809
Bhargava A., and A. Bansal 2021, Fruits and vegetables quality evaluation using computer vision: A review. J King Saud Univ Comput Inf 33(3):243-257. doi:10.1016/j.jksuci.2018.06.002 10.1016/j.jksuci.2018.06.002
Cubero S., N. Aleixos, E. Moltó, J. Gómez-Sanchis, and J. Blasco 2011, Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioproc Technol 4:487-504. doi:10.1007/s11947-010-0411-8 10.1007/s11947-010-0411-8
Fracarolli J.A., F.F.A. Pavarin, W. Castro, and J. Blasco 2021, Computer vision applied to food and agricultural products. Rev Cienc Agron 51:1-20. doi:10.5935/1806-6690.20200087 10.5935/1806-6690.20200087
Gomes J.F.S., and F.R. Leta 2012, Applications of computer vision techniques in the agriculture and food industry: a review. Eur Food Res Technol 235:989-1000. doi:10.1007/s00217-012-1844-2 10.1007/s00217-012-1844-2
Hameed K., D. Chai, and A. Rassau 2018, A comprehensive review of fruit and vegetable classification techniques. Image Vis Comput 80:24-44. doi:10.1016/j.imavis.2018.09.016 10.1016/j.imavis.2018.09.016
Howse J. 2013, OpenCV computer vision with python. Packt Publishing, Birmingham, England, UK, p 27.
Kirillov A., E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, and R. Girshick 2023, Segment anything. arXiv preprint arXiv:2304.02643. doi:10.1109/ICCV51070.2023.00371 10.1109/ICCV51070.2023
Kumar S.D., S. Esakkirajan, S. Bama, S, and B. Keerthiveena 2020, A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier. Microprocess Microsyst 76:103090. doi:10.1016/j.micpro.2020.103090 10.1016/j.micpro.2020.103090
Lonsbary S.K., J. O'Sullivan, and C.J. Swanton 2004, Reduced tillage alternatives for machine-harvested cucumbers. HortScience 39:991-995. doi:10.21273/HORTSCI.39.5.991 10.21273/HORTSCI.39.5.991
Mahendran R., G.C. Jayashree, and K. Alagusundaram 2012, Application of computer vision technique on sorting and grading of fruits and vegetables. J Food Process Technol 10:2157-7110.
Moreira G., S.A. Magalhaes, T. Pinho, F.N. dos Santos, and M. Cunha 2022, Benchmark of deep learning and a proposed HSV colour space models for the detection and classification of greenhouse tomato. Agronomy 12(2):356. doi:10.3390/agronomy12020356 10.3390/agronomy12020356
Nandi C.S., B. Tudu, and C. Koley 2012, An automated machine vision based system for fruit sorting and grading. In 2012 Sixth International Conference on Sensing Technology (ICST), IEEE, pp 195-200. doi:10.1109/ICSensT.2012.6461669 10.1109/ICSensT.2012.6461669
Paris M.K., J.E. Zalapa, J.D. McCreight, and J.E. Staub 2008, Genetic dissection of fruit quality components in melon (Cucumis melo L.) using a RIL population derived from exotic× elite US Western Shipping germplasm. Mol Breed 22:405-419. doi:10.1007/s11032-008-9185-3 10.1007/s11032-008-9185-3
Prasanna R.D., P. Neelamegam, S. Sriram, and N. Raju 2012, Enhancement of vein patterns in hand image for biometric and biomedical application using various image enhancement techniques. Procedia Eng 38:1174-1185. doi:10.1016/j.proeng.2012.06.149 10.1016/j.proeng.2012.06.149
Saldaña E., R. Siche, M. Luján, and R. Quevedo 2013, Computer vision applied to the inspection and quality control of fruits and vegetables. Braz J Food Technol 16:254-272. doi:10.1590/S1981-67232013005000031 10.1590/S1981-67232013005000031
Solem J.E. 2012, Programming Computer Vision with Python: Tools and algorithms for analyzing images. O'Reilly Media, Inc., Sebastopol, CA, USA.
Tian H., T. Wang, Y. Liu, X. Qiao, and Y. Li 2020, Computer vision technology in agricultural automation-A review. Inf Process Agric 7(1):1-19. doi:10.1016/j.inpa.2019.09.006 10.1016/j.inpa.2019.09.006
Wan P., A. Toudeshki, H. Tan, and R. Ehsani 2018, A methodology for fresh tomato maturity detection using computer vision. Comput Electron Agric 146:43-50. doi:10.1016/j.compag.2018.01.011 10.1016/j.compag.2018.01.011
Xu X., H. Wang, M. Miao, W. Zhang, Y. Zhang, H. Dai, Z. Zheng, and X. Zhang 2023, Cucumber flower detection based on YOLOv5s-SE7 within greenhouse environments. IEEE 11:64358-64369. doi:10.1109/ACCESS.2023.3286545 10.1109/ACCESS.2023.3286545
Zhang B., W. Huang, J. Li, C. Zhao, S. Fan, J. Wu, and C. Liu 2014, Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Res Int J 62:326-343. doi:10.1016/j.foodres.2014.03.012 10.1016/j.foodres.2014.03.012
Zhou Y., L. Hu, J. Song, L. Jiang, and S. Liu 2019, Isolation and characterization of a MADS-box gene in cucumber (Cucumis sativus L.) that affects flowering time and leaf morphology in transgenic Arabidopsis. Biotechnol Biotechnol Equip 33:54-63. doi:10.1080/13102818.2018.1534556 10.1080/13102818.2018.1534556
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Journal of Bio-Environment Control
  • Journal Title(Ko) :생물환경조절학회지
  • Volume : 33
  • No :1
  • Pages :63-70
  • Received Date : 2023-10-05
  • Revised Date : 2024-01-29
  • Accepted Date : 2024-01-29