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Paper Title : Weed Identification using Deep Learning and Image Processing in Vegetable Plantation

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.7294955

Authors: B. Haritha Lakshmi, Ch.Ruchitha, B.Gayathri, G.Kavya

         


Abstract
Vegetable plantations have irregular plant spacing, making weed identification more difficult than in crop plantations. Finding weeds in vegetable or plant fields is a topic that hasn't received much attention. The main objective of earlier approaches to crop weed identification was identification. Despite the fact that there are many different weed species, directly. In contrast, this essay proposes a revolutionary method that combines deep learning and image processing technologies. First, veggies were identified and bounding boxes were created around them using a trained Center Net model. The remaining green things that continued to fall out of the surrounding boxes were then classified as weeds. By concentrating just on detecting vegetables in this way, the model may avoid dealing with different weed species.

Reference
1. T. W. Berge, A. H. Aastveit, and H. Fykse, ‘‘Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals,’’ Precis. Agricult., vol. 9, no. 6, pp. 391–405, Dec. 2008 2. E. Hamuda, M. Glavin, and E. Jones, ‘‘A survey of image processing techniques for plant extraction and segmentation in the field,’’ Comput. Electron. Agricult., vol. 125, pp. 184–199, Jul. 2016 3. H. Mennan, K. Jabran, B. H. Zandstra, and F. Pala, ‘‘Non-chemical weed management in vegetables by using cover crops: A review,’’ Agronomy, vol. 10, no. 2, p. 257, Feb. 2020. 4. X. Dai, Y. Xu, J. Zheng, and H. Song, ‘‘Analysis of the variability of pesticide concentration downstream of inline mixers for direct nozzle injection systems,’’ Biosyst. Eng., vol. 180, pp. 59–69, Apr. 2019. 5. D. C. Slaughter, D. K. Giles, and D. Downey, ‘‘Autonomous robotic weed control systems: A review,’’ Comput. Electron. Agricult., vol. 61, no. 1, pp. 63–78, Apr. 2008. 6. K. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, ‘‘Machine learning in agriculture: A review,’’ Sensors, vol. 18, no. 8, p. 2674, Aug. 2018. 7. A. Wang, W. Zhang, and X. Wei, ‘‘A review on weed detection using ground-based machine vision and image processing techniques,’’ Comput. Electron. Agricult., vol. 158, pp. 226–240, Mar. 2019. 8. F. Ahmed, H. A. Al-Mamun, A. S. M. H. Bari, E. Hossain, and P. Kwan, ‘‘Classification of crops and weeds from digital images: A support vector machine approach,’’ Crop Protection, vol. 40, pp. 98–104, Oct. 2012. 9. P. Herrera, J. Dorado, and Á. Ribeiro, ‘‘A novel approach for weed type classification based on shape descriptors and a fuzzy decision-making method,’’ Sensors, vol. 14, no. 8, pp. 15304–15324, Aug. 2014. 10. Y. Chen, X. Jin, L. Tang, J. Che, Y. Sun, and J. Chen, ‘‘Intra-row weed recognition using plant spacing information in stereo images,’’ presented at the Kansas City, Missouri, St. Joseph, MI, USA, 2013. [Online]. Available: http://elibrary.asabe.org/abstract.asp?aid=43357&t=5 11. G. Hinton, L. Deng, D. Yu, G. Dahl, A.-R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, ‘‘Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,’’ IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82–97, Nov. 2012. 12. Y. LeCun, Y. Bengio, and G. Hinton, ‘‘Deep learning,’’ Nature, vol. 521, no. 7553, pp. 436–444, May 2015. 13. J. Schmidhuber, ‘‘Deep learning in neural networks: An overview,’’ Neural Netw., vol. 61, pp. 85–117, Jan. 2015. 14. J. Long, E. Shelhamer, and T. Darrell, ‘‘Fully convolutional networks for semantic segmentation,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2015, pp. 3431–3440, doi: 10.1109/CVPR. 2015.7298965. 15. A. Olsen, D. A. Konovalov, B. Philippa, P. Ridd, J. C. Wood, J. Johns, W. Banks, B. Girgenti, O. Kenny, J. Whinney, B. Calvert, M. R. Azghadi, and R. D. White, ‘‘DeepWeeds: A multiclass weed species image dataset for deep learning,’’ Sci. Rep., vol. 9, no. 1, p. 2058, Feb. 2019.
Keywords
— DL , GA, image processing , Weed Identification.