Recognition of Weeds in Cornfields

• Autorzy: Hartyányi Gábor , Czuni Laszlo

Identyfikatory

DOI

10.15439/2023F2610

• Języki publikacji: EN

Abstrakty

EN

In terms of weed control, existing precision spraying solutions seek to reduce the unwanted impact of spraying by separate field scanning from mostly birds' eye view. In our study, we propose a hybrid approach in which the mechanical hoeing and the spraying is done simultaneously accomplished by weed recognition from a lower position where the plants' leaves do not cover weeds. We demonstrate the line and the weed recognition methods on a dataset collected from corn fields and compare different convolutional neural networks. We also investigate the feasibility on two widely known embedded platforms.

Słowa kluczowe

EN

location awareness; computer science; spraying; bird; agriculture; convolutional neural network; chemical

PL

lokalizacja; informatyka; opryskiwanie; ptak; rolnictwo; konwolucyjna sieć neuronowa; chemikalia

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 995--999
• Opis fizyczny: Bibliogr. 18 poz., fot., tab., wykr.

Twórcy

autor

Hartyányi Gábor

• Image Processing Research Laboratory University of Pannonia, 8200 Veszprém Egyetem u. 10, Hungary also with Axial Ltd, 6500 Baja

• https://orcid.org/009-0002-0524-0495

autor

Czuni Laszlo

• Image Processing Research Laboratory University of Pannonia, 8200 Veszprém Egyetem u. 10, Hungary

• https://orcid.org/0000-0001-7667-9513

Bibliografia

• 1. Constanze Fetting, “The European Green Deal," ESDN Report, December 2020, ESDN Office, Vienna.
• 2. Aichen Wang, Wen Zhang, Xinhua Wei, “A review on weed detection using ground-based machine vision and image processing techniques,” Computers and Electronics in Agriculture, vol. 158, 2019, pp. 226–240.
• 3. Food and Agriculture Organization of the United Nations, https://www.fao.org/faostat
• 4. Bakhshipour, A., Jafari, A., Nassiri, S.M., Zare, D., “Weed segmentation using texture features extracted from wavelet sub-images,” Biosyst. Eng., vol. 157, 2017, pp. 1-–12.
• 5. Kumar, D.A., Prema, P., “A novel wrapping curvelet transformation based angular texture pattern (WCTATP) extraction method for weed identification,” ICTACT J. on Image Video Process., vol. 6, no. 3, 2016
• 6. García-Santillán, I., Guerrero, J.M., Montalvo, M., Pajares, G., “Curved and straight crop row detection by accumulation of green pixels from images in maize fields.” Precis. Agric., vol. 19, 2018, pp. 18-–41.
• 7. Midtiby, H.S., Åstrand, B., Jørgensen, O., Jørgensen, R.N.,“Upper limit for context–based crop classification in robotic weeding applications.” Biosyst. Eng. vol. 146, 2016, pp. 183-–192.
• 8. Xu, K., Li, H., Cao, W., Zhu, Y., Chen, R., and Ni, J., “Recognition of weeds in wheat fields based on the fusion of RGB images and depth images.” IEEE Access, vol. 8, 2020, pp. 110362–110370.
• 9. Tang, J., Zhang, Z., Wang, D., Xin, J., He, L., “Research on weeds identification based on K-means feature learning.” Soft Comput. vol. 22, 2018, pp. 7649–7658.
• 10. Hall, D., Dayoub, F., Kulk, J., McCool, C., “Towards unsupervised weed scouting for agricultural robotics.” in Robotics and Automation (ICRA), 2017 IEEE International Conference On. IEEE, pp. 5223-–5230.
• 11. Szegedy, C, Liu W., Jia Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A., “Going Deeper with Convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1—9.
• 12. Zhang, R., Wang, C., Hu, X., Liu, Y., and Chen, S., “Weed location and recognition based on UAV imaging and deep learning.” International Journal of Precision Agricultural Aviation, 2020, vol. 3, no. 1, pp. 23–29.
• 13. Garibaldi-Márquez, F., Flores, G., Mercado-Ravell, D. A., RamírezPedraza, A., and Valentín-Coronado, L. M., “Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning, “ Sensors, vol. 22, no. 8, 3021.
• 14. Simonyan, K.; Zisserman, A., “Very deep convolutional networks for large-scale image recognition,” In Proceedings of the 3rd International Conference on Learning Representations, San Diego, USA, 7–9 May 2015.
• 15. Chollet, F. “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 21-26 July 2017, pp. 1800—1807.
• 16. Venkataraju, A., Arumugam, D., Stepan, C., Kiran, R., and Peters, T., “A Review of Machine Learning Techniques for Identifying Weeds in Corn,” Smart Agricultural Technology, 2022, 100102.
• 17. He, K., Gkioxari, G. , Dollár, P., and Girshick, R., “Mask R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2961-–2969.
• 18. Tan, M., and Le, Q., “EfficientNet: Rethinking model scaling for convolutional neural networks.” International Conference on Machine Learning, PMLR, 2019, pp. 6105–6114.

Uwagi

1. Thematic Tracks Short Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).