4,531
Views
8
CrossRef citations to date
0
Altmetric
COMPUTER SCIENCE

Classification of paddy crop and weeds using semantic segmentation

, ORCID Icon, & | (Reviewing editor)
Article: 2018791 | Received 11 Sep 2021, Accepted 14 Nov 2021, Published online: 07 Feb 2022

References

  • Ahmed, F., Al-Mamun, H. A., Hossain Bari, A. S. M., Hossain, E., & Kwan, P. (2012). Classification of crops and weeds from digital images: A support vector machine approach. Crop Protection, 40, 0 98–18. https://doi.org/10.1016/j.cropro.2012.04.024
  • Andrea, C.-C., Barreno Mauricio Daniel, B., & Barrionuevo José Misael, J. Precise weed and maize classification through convolutional neuronal networks. In 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), 1–6. IEEE, 2017.
  • Ashok Kumar, D., & Prema, P. (2016). A novel approach for weed classification using curvelet transform and Tamura texture feature (ctttf) with rvm classification. International Journal of Applied Engineering Research, 110(3), 0 1841–1848 http://www.scopus.com/inward/record.url?eid=2-s2.0-84991310024&partnerID=MN8TOARS.
  • Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 390(12), 0 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615
  • Bakhshipour, A., & Jafari, A. (2018). Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Computers and Electronics in Agriculture, 145, 0 153–160. https://doi.org/10.1016/j.compag.2017.12.032
  • Bengio, Y., LeCun, Y., Nohl, C., & Burges, C. (1995). Lerec: A nn/hmm hybrid for on-line handwriting recognition. Neural Computation, 70(6), 0 1289–1303. https://doi.org/10.1162/neco.1995.7.6.1289
  • Chechlinski, L., Siemitkowska, B., & Majewski, M. (2019). A system for weeds and crops identification—reaching over 10 fps on raspberry pi with the usage of mobilenets, densenet and custom modifications. Sensors, 190(17), 0 3787. https://doi.org/10.3390/s19173787
  • Fawakherji, M., Youssef, A., Bloisi, D., Pretto, A., & Nardi, D. Crop and weeds classification for precision agriculture using context-independent pixel-wise segmentation. In 2019 Third IEEE International Conference on Robotic Computing (IRC) Naples, Italy, 146–152. IEEE, 2019.
  • Gao, J., Nuyttens, D., Lootens, P., Yong, H., & Pieters, J. G. (2018). Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosystems Engineering, 170, 0 39–50. https://doi.org/10.1016/j.biosystemseng.2018.03.006
  • Gharde, Y., Singh, P. K., Dubey, R. P., & Gupta, P. K. (2018). Assessment of yield and economic losses in agriculture due to weeds in India. Crop Protection, 107, 0 12–18. https://doi.org/10.1016/j.cropro.2018.01.007
  • Hamza Asad, M., & Bais, A. (2019). Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture 7 (4) doi:10.1016/j.inpa.2019.12.002 .
  • Inkyu, S., Chen, Z., Popović, M., Khanna, R., Liebisch, F., Nieto, J., & Siegwart, R. (2017). weednet: Dense semantic weed classification using multispectral images and mav for smart farming. IEEE Robotics and Automation Letters, 30(1), 0 588–595 doi:10.1109/LRA.2017.2774979.
  • Jialin, Y., Schumann, A. W., Cao, Z., Sharpe, S. M., & Boyd, N. S. (2019). Weed detection in perennial ryegrass with deep learning convolutional neural network. Frontiers in Plant Science, 10 1422–1429 . https://doi.org/10.3389/fpls.2019.01422
  • Kaiming, H., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition Las Vegas, NV, USA, 770–778, 2016.
  • Kamath, R., Balachandra, M., & Prabhu, S. (2019). Raspberry pi as visual sensor nodes in precision agriculture: A study. IEEE Access, 7, 0 45110–45122. https://doi.org/10.1109/ACCESS.2019.2908846
  • Kamath, R., Balachandra, M., & Prabhu, S. (2020). Paddy crop and weed discrimination: A multiple classifier system approach. International Journal of Agronomy, 2020, 1–14. https://doi.org/10.1155/2020/6474536
  • Kamath, R., Balachanra, M., & Prabhu, S. (2018). Paddy crop and weed classification using color features for computer vision based precision agriculture. International Journal of Engineering and Technology (UAE), 70(4), 0 2909–2916 doi:10.14419/ijet.v7i4.21511.
  • Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 0 70–90. https://doi.org/10.1016/j.compag.2018.02.016
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 0 1097–1105 https://dl.acm.org/doi/10.5555/2999134.2999257.
  • Liu, B., & Bruch, R. (2020). Weed detection for selective spraying: A review. Current Robotics Reports 1 (1) , 1–8 doi:10.1007/s43154-020-00001-w.
  • Long, J., Shelhamer, E., & Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition Boston, MA, USA, 3431–3440, 2015.
  • Ma, X., Deng, X., Long, Q., Jiang, Y., Hongwei, L., Wang, Y., & Xing, X. (2019). Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields. PloS One, 140(4 1–13 doi:10.1371/journal.pone.0215676).
  • Milioto, A., Lottes, P., & Stachniss, C. Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns. In 2018 IEEE International Conference on Robotics and Automation (ICRA) Brisbane, Australia, 2229–2235. IEEE, 2018.
  • Naidu, V. S. G. R. Hand book on weed identification, 2012.
  • Nistrup Jørgensen, L., Noe, E., Langvad, A.-M., Jensen, J. E., Erik Ørum, J., & Rydahl, P. (2007). Decision support systems: Barriers and farmers’ need for support. EPPO Bulletin, 370(2), 0 374–377. https://doi.org/10.1111/j.1365-2338.2007.01145.x
  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 90(1), 0 62–66. https://doi.org/10.1109/TSMC.1979.4310076
  • Parameswari, Y. S., & Srinivas, A. (2017). Weed management in rice—a review. International Journal of Applied and Pure Science and Agriculture, 3 (1) https://ijapsa.com/published-papers/volume-3/issue-1/weed-management-in-rice-a-review.pdf .
  • Redmon, J., & Farhadi, A. Yolo9000: Better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition Honolulu, USA, 7263–7271, 2017.
  • Santiago, W. E., Leite, N. J., Teruel, B. J., Karkee, M., Azania, C. A. M. et al. (2019). Evaluation of bag-of-features (bof) technique for weed management in sugarcane production. Australian Journal of Crop Science, 130(11), 0 1819. https://doi.org/10.21475/ajcs.19.13.11.p1838
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 1 85–117 doi:10.1016/j.neunet.2014.09.003.
  • Simonyan, K., & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  • Subudhi, H. N., Panda, S. P., Behera, P. K., & Patnaik, C. (2015). A check list of weeds in rice fields of coastal Orissa, India. Journal of Agricultural Science, 70(6), 0 207 doi:10.5539/JAS.V7N6P207.
  • Sureshkumar, R., Ashoka Reddy, Y., & Ravichandran, S. (2016). Effect of weeds and their management in transplanted rice–a review. International Journal of Research in Applied, Natural and Social Sciences, 40(11), 0 165–180 https://www.researchgate.net/publication/310774176_EFFECT_OF_WEEDS_AND_THEIR_MANAGEMENT_IN_TRANSPLANTED_RICE_-_A_REVIEW.
  • Xiaomeng, L., Chen, H., Xiaojuan, Q., Dou, Q., Chi-Wing, F., & Heng, P.-A. (2018). H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Transactions on Medical Imaging, 370(12), 0 2663–2674 doi:10.1109/TMI.2018.2845918.
  • Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. Scene parsing through ade20k dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition Honolulu, HI, USA, 633–641, 2017.