Нейросети, глубокое обучение, машинное зрение в сельском хозяйстве. Краткий обзор для 2021 года

Ildar  Rakhmatulin

ПРЕПРИНТ

Эта статья является препринтом и не была отрецензирована.
О результатах, изложенных в препринтах, не следует сообщать в СМИ как о проверенной информации.

Нейросети, глубокое обучение, машинное зрение в сельском хозяйстве. Краткий обзор для 2021 года

I. Rakhmatulin

2021-03-21

https://doi.org/10.24108/preprints-3112205

Беспрецедентный прогресс в области глубокого обучения повлиял на многие отрасли, в том числе и на сектор сельского хозяйства. Использование нейронных сетей в агропромышленной деятельности в задаче распознавания различных культур и сорняков - новое направление с историей менее 10 лет. Десятки новых нейронных сети появляются каждый год, но отсутствие каких-либо стандартов существенно затрудняет понимание реальной ситуации использования нейронной сети в аграрном секторе. В рукописи мы проанализировали исследования за последние 10 лет по использованию нейронных сетей для классификации и отслеживание посевов и сорняков в сельском хозяйстве. Мы представили анализ результатов использования различных нейронной сети для задач классификации и отслеживания. Наконец, мы сделали рекомендации по использованию нейронных сетей в задачах распознавания культивируемого объекта и сорняков.

Ссылка для цитирования:

Rakhmatulin I. 2021. Нейросети, глубокое обучение, машинное зрение в сельском хозяйстве. Краткий обзор для 2021 года. PREPRINTS.RU. https://doi.org/10.24108/preprints-3112205

Список литературы

1. Ip, P., & Ang, L. (2018). Big data and machine learning for crop protection. Computers and Electronics in Agriculture, 151, 376-383

2. Wang, A., Ang, W., & Seng, K. (2019). A review on weed detection using ground-based machine vision and image processing techniques. Computers and Electronics in Agriculture, 158, 226-240

3. Nejati, H., Azimifar, Z., & Destain, M. (2011). Weed detection in 3D images. Precision Agriculture 12(5), 607-622

4. Nejati, H., Azimifar, Z., & Zamani, M. (2008). Using Fast Fourier Transform for Weed Detection in Corn Fields. IEEE International Conference on Systems, Man and Cybernetics, 1215-1219

5. Rueda-Ayala, V., Gerhards, R., & Rasmussen, J. (2010). Mechanical weed control. Precision Crop Protection - the Challenge and Use of Heterogeneity, 45, 279-294

6. Utstumo, T. (2018). Robotic in-row weed control in vegetables. Computers and Electronics in Agriculture, 154, 36-45

7. Alba, O., Syrov, L., & Shirtliffe, S. (2019). Increased seeding rate and multiple methods of mechanical weed control reduce weed biomass in a poorly competitive organic crop. Field Crops Research,6, 245

8. Melander, B., Lattanzi, B., & Pannacci, E. (2015). Intelligent versus non-intelligent mechanical intra-row weed control in transplanted onion and cabbage. Crop Protection, 72, 1-8

9. Syrov, L. (2016). Effects of mechanical weed control or cover crop on the growth and economic viability of two short-rotation willow cultivars. Biomass and Bioenergy, 91, 296-305

10. Panqueba, B., & Medina, C. (2016). A computer vision application to detect unwanted weed in early stage crops, 4, 41-45

11. Reddy , R., Laasya,G. and Basha, M., 2017. Image Processing for Weed Detection, 5, Issue 4, 485-489

12. Vikhram, G., Agarwal, R., & Prasanth, V. (2018). Automatic Weed Detection and Smart Herbicide Sprayer Robot. International Journal of Engineering & Technology, 7, 115-118

13. Pasha, I., Reddy, A., & Mahima, P. (2018). DESIGN OF A WEED DETECTION SYSTEM FOR COTTON FIELD. International Journal of Pure and Applied Mathematics, 118, 1314-3395

14. García-Santillán, I., & Guerrero, J. (2018). Curved and straight crop row detection by accumulation of green pixels from images in maize fields. Precision Agriculture, 19, Issue 1, 18–41

15. Murawwat, S. (2018). Weed Detection Using SVMs. Engineering, Technology & Applied Science Research, 8, 2412-2416

16. Pulido-Rojas, C. (2016). Machine vision system for weed detection using image filtering in vegetables cropssa. Revista Facultad de Ingeniería, 56, 356-360

17. Bhongal, K., & Gore,S. (2017). USING IMAGE PROCESSING TECHNIQUES AND SMART HERBICIDE SPRAYER ROBOT, 7, 10-16

18. Michen, S., & Lawer, A. (2019). Deep learning n agriculture, 187, 278-291

19. Ambika, N., & Supriya, P. (2018). Detection of Vanilla Species by Employing Image Processing Approach. Procedia Computer Science, 143, 474-480

20. Ferreira, A., Freitas, D., Silva, G., Pistorib, H., & Folhesc, M. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture, 143, 314-324

21. Sabanci, K., & Aydin,C. (2016). Smart Robotic Weed Control System for Sugar Beet. J. Agr. Sci. Tech, 19, 73-83

22. Young, S. (2018). Future Directions for Automated Weed Management in Precision Agriculture. , Automation: The Future of Weed Control in Cropping Systems, 6, 249–259

23. Frasconi, C., & Fontanell, M. (2014). Design and full realization of physical weed control (PWC) automated machine within the RHEA project. Proceedings International Conference of Agricultural Engineering, 134-143

24. Kulkarni, V. (2013). Advanced Agriculture Robotic Weed Control System. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2, Issue 10, 5073-5081

25. Bakhshipour, A. (2017). Weed segmentation using texture features extracted from wavelet sub-images. Biosystems Engineering, 157, 1-12

26. Kargar, A. (2013). AUTOMATIC WEED DETECTION AND SMART HERBICIDE SPRAY ROBOT FOR CORN FIELDS. Robotics and Mechatronics (ICRoM), First RSI/ISM International Conference, 451-470

27. Potena, C. (2016). Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture. International Conference on Intelligent Autonomous Systems IAS. Intelligent Autonomous Systems, 14, 105-121

28. Slaughter, D., & Giles, D. (2018). Autonomous robotic weed control systems: A review. Computers and electronics in agriculture, 61, 63–78

29. Ospina, R. (2018). Smart Agricultural Vehicle by Integrating Motion Model with Machine Vision Data. Dissertation. Hokkaido University Collection of Scholarly and Academic Papers: HUSCAP

30. Dyrmann, M. (2015). Automatic Detection and Classification of Weed Seedlings under Natural Light Conditions. Thesis for: PhD. DOI: 10.13140/RG.2.2.29212.18560

31. Alba, O. (2019). Increased seeding rate and multiple methods of mechanical weed control reduce weed biomass in a poorly competitive organic crop. Field Crops Research, 245, 107-117

32. Bah, M. (2018). Deep Learning with unsupervised data labeling for weeds detection on UAV images. Remote Sensing 10(11), 15-17

33. Priya, S. (2019). Identification of Weeds using Hsv Color Spaces and Labelling with Machine Learning Algorithms. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, 8, Issue-1, 1781-1786

34. Potena, C. (2019). Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture. Intelligent Autonomous Systems 14: Proceedings of the 14th International Conference IAS-14,105-121

35. Ahmed, F. (2019). Machine Learning for Crop and Weed Classification. Performance Analysis of Support Vector Machine and Bayesian Classifier for Crop and Weed Classification from Digital Images. World Applied Sciences Journal, 12(4), 432-440

36. Sharma, P. (2019). Crops and weeds classification using Convolutional Neural Networks via optimization of transfer learning parameters. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958, 8, Issue-5, 2285-2294

37. Zhang, W. (2018). Broad-leaf weed detection in pasture. IEEE 3rd International Conference on Image, Vision and Computing, 15-23

38. Huang, H. (2016). A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery Huasheng. PLoS ONE, 450-470

39. Hameed, S. (2018). Detection of Weed and Wheat Using Image Processing. Conference: 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS), 34-39

40. Wang, S., & Liu, H. (2018). Low-Altitude Remote Sensing Based on Convolutional Neural Network for Weed Classification in Ecological Irrigation Area. IFAC-PapersOnLine, 51, Issue 17, 298-303

41. Huang, Y. (2019). UAV Low-Altitude Remote Sensing for Precision Weed Management. Weed Technology, 32, 1-5

ПРЕПРИНТ

ИНФОРМАЦИЯ