GREENHOUSE PRODUCTIVITY ESTIMATION BASED ON THE OPTIMIZED YOLOV5 MODEL

Admin bo'lib kirish (www.admin.slib.uz)

Tizim sinov (TEST) rejimida ishlamoqda! Murojat uchun @slib_support

Eski talqinga o'tish link

17 июл 2024

Asosiy til:Ingliz

GREENHOUSE PRODUCTIVITY ESTIMATION BASED ON THE OPTIMIZED YOLOV5 MODEL

Fan yo'nalishi:

669756b62611e.pdf

PDF

Raqamli iqtisodiyot

Maqola chop etish see more

MAQOLA ANNOTATSIYASI

Zamonaviy qishloq xo'jaligida hosildorlikni maksimal darajada oshirish uchun aniq monitoring va resurslarni samarali boshqarish muhim ahamiyatga ega hisoblanadi. Ushbu tadqiqot pomidor o'simligi ma'lumotlar to'plami uchun moslashtirilgan va optimallashtirilgan zamonaviy YOLOv5 ob'ektni aniqlash modelidan foydalangan holda issiqxona mahsuldorligini baholashga yangi yondashuvni taqdim etadi. Tadqiqot pomidorlarni uch toifaga - yashil, pushti va qizil rangga aniqlash va tasniflashga qaratilgan - real vaqt rejimida pishib yetilish jarayonini har tomonlama tushunishni ta'minlaydi. Optimallashtirilgan YOLOv5 modeli boshqa standard versiyali moddellarga nisbatan yuqori unumdorlikni namoyish etib, pomidorni aniqlashda yaxshilangan yuqori aniqlikni namoyish etadi. O'tkazilgan tajribada Model tartibga solingan yetti kamerali tizim bilan jihozlangan haqiqiy issiqxonada joylashtirilib har bir kamera orqali bitta qatordagi pomidor o'simliklarini suratga olib, sinchkovlik bilan kuztuv olib borildi. Bitta qatordan olingan natijalarni butun issiqxonaga (sakkiz qatordan iborat) ekstrapolyatsiya qilish orqali umumiy hosildorlikni aniq baholashga erishildi. Pomidor o'simliklari holati va asosiy statistik ma'lumotlarning real vaqt rejimida monitoringini osonlashtirish uchun veb-ilova ishlab chiqilgan bo’lib, Ilova yashil, pushti va qizil pomidorlarning foizlari haqida ma'lumot beradi. Bu issiqxona operatorlariga resurslarni taqsimlash va boshqarish bo'yicha ongli qarorlar qabul qilish imkonini beradi. Taklif etilayotgan metodologiya issiqxona mahsuldorligini baholash uchun keng ko'lamli va amaliy yechim taklif etadi. Olingan natijalar qishloq xo'jaligida kompyuter orqali boshqarish dasturlarini rivojlantirishga, issiqxonalar mahsuldorligini oshirishga, issiqxonalarda barqaror va samarali amaliyotlarni rivojlantirishga yordam beradi

MUALIFLAR

Teglar

# greenhouse Productivity# YOLOv5 Optimization# Tomato Detection# Precision Agriculture# issiqxona mahsuldorligi# YOLOv5 optimallashtirish# pomidorni aniqlash# aniq qishloq xo'jaligi# продуктивность теплицы# оптимизация YOLOv5# обнаружение помидоров# точное земледелие

Maqolani baholang

0

0 ta

Maqola idintifikatorlari

ROI:

https://eroi.uz/11.0142/A0724-0008

DOI:

Mavjud emas

Foydalanilgan adabiyotlar

T. Dewi, P. Risma, and Y. Oktarina, “Fruit sorting robot based on color and size for an agricultural product packaging system,” Bull. Electr. Eng. Informatics, vol. 9, no. 4, pp. 1438–1445, 2020, doi: 10.11591/eei.v9i4.2353

Z. Tian, W. Ma, Q. Yang, and F. Duan, “Application status and challenges of machine vision in plant factory—A review,” Inf. Process. Agric., vol. 9, no. 2, pp. 195–211, 2022, doi: 10.1016/j.inpa.2021.06.003

N. Schor, S. Berman, A. Dombrovsky, Y. Elad, T. Ignat, and A. Bechar, “Development of a robotic detection system for greenhouse pepper plant diseases,” Precis. Agric., vol. 18, no. 3, pp. 394–409, 2017, doi: 10.1007/s11119-017-9503-z

I. Bechar, S. Moisan, E. P. I. Pulsar, and I. S. Antipolis-mediterranee, “Online counting of pests in a greenhouse using computer vision,” VAIB 2010 - Vis. Obs. Anal. Anim. Insect Behav., no. August, pp. 1–4, 2010

M. V. Giuffrida, “Leaf counting from uncontrolled acquired images from greenhouse workers”.

S. Yang, L. Huang, and X. Zhang, “Research and application of machine vision in monitoring the growth of facility seedling crops,” Jiangsu Agric. Sci, vol. 47, pp. 179–187, 2019.

Y. Ren, “Development of transplanting robot in facility agriculture based on machine vision,” Dissertation, Zhejiang University, 2007.

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, pp. 1–19, 2020, doi: 10.1016/j.inpa.2019.09.006.

K. Lin, J. Chen, H. Si, and J. Wu, “A review on computer vision technologies applied in greenhouse plant stress detection,” Commun. Comput. Inf. Sci., vol. 363, pp. 192–200, 2013, doi: 10.1007/978-3-642-37149-3_23.

Z. Li et al., “A high-precision detection method of hydroponic lettuce seedlings status based on improved Faster RCNN,” Comput. Electron. Agric., vol. 182, no. October 2020, 2021, doi: 10.1016/j.compag.2021.106054.

Z. Wu, R. Yang, F. Gao, W. Wang, L. Fu, and R. Li, “Segmentation of abnormal leaves of hydroponic lettuce based on DeepLabV3+ for robotic sorting,” Comput. Electron. Agric., vol. 190, no. August, p. 106443, 2021, doi: 10.1016/j.compag.2021.106443.

L. Xi, M. Zhang, L. Zhang, T. T. S. Lew, and Y. M. Lam, “Novel Materials for Urban Farming,” Adv. Mater., vol. 34, no. 25, pp. 1–28, 2022, doi: 10.1002/adma.202105009.

G. Ares, B. Ha, and S. R. Jaeger, “Consumer attitudes to vertical farming (indoor plant factory with artificial lighting) in China, Singapore, UK, and USA: A multi-method study,” Food Res. Int., vol. 150, no. November, 2021, doi: 10.1016/j.foodres.2021.110811.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 580–587, 2014, doi: 10.1109/CVPR.2014.81.

T. Li et al., “Tomato recognition and location algorithm based on improved YOLOv5,” Comput. Electron. Agric., vol. 208, no. March, p. 107759, 2023, doi: 10.1016/j.compag.2023.107759.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, 2017, doi: 10.1109/TPAMI.2016.2577031.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.

J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” 2018, [Online]. Available: http://arxiv.org/abs/1804.02767

A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” 2020, [Online]. Available: http://arxiv.org/abs/2004.10934

Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “YOLOX: Exceeding YOLO Series in 2021,” pp. 1–7, 2021, [Online]. Available: http://arxiv.org/abs/2107.08430

J. Redmon and A. Farhadi, “YOLO9000: Better, faster, stronger,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 6517–6525, 2017, doi: 10.1109/CVPR.2017.690.

C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors,” pp. 7464–7475, 2023, doi: 10.1109/cvpr52729.2023.00721.

W. Liu et al., “SSD: Single Shot MultiBox Detector BT - Computer Vision – ECCV 2016,” B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., Cham: Springer International Publishing, 2016, pp. 21–37.

M. P. Mathew and T. Y. Mahesh, “Leaf-based disease detection in bell pepper plant using YOLO v5,” Signal, Image Video Process., vol. 16, no. 3, pp. 841– 847, 2022, doi: 10.1007/s11760-021-02024-y.

D. G. Lowe, “Object recognition from local scale-invariant features,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2, pp. 1150–1157, 1999, doi: 10.1109/iccv.1999.790410.

C. P. Papageorgiou, M. Oren, and T. Poggio, “General framework for object detection,” Proc. IEEE Int. Conf. Comput. Vis., pp. 555–562, 1998, doi: 10.1109/iccv.1998.710772.

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proc. - 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, CVPR 2005, vol. I, pp. 886–893, 2005, doi: 10.1109/CVPR.2005.177.

J. Donahue et al., “DeCAF: A deep convolutional activation feature for generic visual recognition,” 31st Int. Conf. Mach. Learn. ICML 2014, vol. 2, pp. 988– 996, 2014.

G. Yang et al., “Face Mask Recognition System with YOLOV5 Based on Image Recognition,” 2020 IEEE 6th Int. Conf. Comput. Commun. ICCC 2020, vol. 1, no. January 2020, pp. 1398–1404, 2020, doi: 10.1109/ICCC51575.2020.9345042.

T. Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 936–944, 2017, doi: 10.1109/CVPR.2017.106.

S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path Aggregation Network for Instance Segmentation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 8759–8768, 2018, doi: 10.1109/CVPR.2018.00913.

H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized intersection over union: A metric and a loss for bounding box regression,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2019- June, pp. 658–666, 2019, doi: 10.1109/CVPR.2019.00075.