A Strategic Analytics Using Convolutional Neural Networks for Weed Identification in Sugar Beet Fields

Brahim JabirNoureddine FalihAsmaa SarihAdil Tannouche

DOI 10.7160/aol.2021.130104
No 1/2021, March
pp. 49-57

Jabir, B., Falih, N. Sarih, A. and Tannouche, A. (2021) “A Strategic Analytics Using Convolutional Neural Networks for Weed Identification in Sugar Beet Fields", AGRIS on-line Papers in Economics and Informatics, Vol. 13, No. 1, pp. 49-57. ISSN 1804-1930. DOI 10.7160/aol.2021.130104.

Abstract

Researchers in precision agriculture regularly use deep learning that will help growers and farmers control and monitor crops during the growing season; these tools help to extract meaningful information from large-scale aerial images received from the field using several techniques in order to create a strategic analytics for making a decision. The information result of the operation could be exploited for many reasons, such as sub-plot specific weed control. Our focus in this paper is on weed identification and control in sugar beet fields, particularly the creation and optimization of a Convolutional Neural Networks model and train it according to our data set to predict and identify the most popular weed strains in the region of Beni Mellal, Morocco. All that could help select herbicides that work on the identified weeds, we explore the way of transfer learning approach to design the networks, and the famous library Tensorflow for deep learning models, and Keras which is a high-level API built on Tensorflow.

Keywords

Deep learning, CNN, precision agriculture, decision making, strategic analytics.

References

  1. Abd El-Rahiem, B., Ahmed, M. A. O., Reyad, O., Abd El-Rahaman, H., Amin, M. and Abd El-Samie, F. (2019) “An efficient deep convolutional neural network for visual image classification”, In International Conference on Advanced Machine Learning Technologies and Applications, Springer, Cham, pp. 23-31. DOI 10.1007/978-3-030-14118-9_3.
  2. Agarap, A. F. (2018) “Deep learning using rectified linear units (relu)”, arXiv preprint arXiv:1803.08375.
  3. Albawi, S., Mohammed, T. A. and Al-Zawi, S. (2017) “Understanding of a convolutional neural network”, In 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, IEEE, pp. 1-6. DOI 10.1109/ICEngTechnol.2017.8308186.
  4. Analide, C. and Kim, P. (2017) “Experiential learning in data science: from the dataset repository to the platform of experiences”, In Intelligent Environments 2017: Workshop Proceedings of the 13th International Conference on Intelligent Environments, IOS Press, Vol. 22, 122 p.
  5. Basha, S. S., Dubey, S. R., Pulabaigari, V. and Mukherjee, S. (2020) “Impact of fully connected layers on performance of convolutional neural networks for image classification”, Neurocomputing, Vol. 378, pp. 112-119. ISSN 0925-2312. DOI 10.1016/j.neucom.2019.10.008.
  6. Bolo, B., Mpoeleng, D. and Zlotnikova, I. (2019) “Development of Methods Acquiring Real Time Very High Resolution Agricultural Spatial Information Using Unmanned Aerial Vehicle”, AGRIS on-line Papers in Economics and Informatics, Vol. 11, No. 2, pp. 21-29. ISSN 1804-1930. DOI 10.7160/aol.2019.110203.
  7. El Bouzaidi, H., Hafiane, F. Z. and Fekhaoui, M. (2020) “Inventory of Pesticides and their impact on the environment by calculating the frequency of treatment indicator in the Gharb plain (Morocco)”, Mediterranean Journal of Chemistry, Vol. 4, No. 10. ISSN 2028-3997.
  8. El Housni, Z., Ezrari, S., Tahiri, A. and Ouijja, A. (2020) “Resistance of Cercospora beticola Sacc isolates to thiophanate methyl (benzimidazole), demethylation inhibitors and quinone outside inhibitors in Morocco”, EPPO Bulletin, Vol. 50, No. 2, pp. 350-357. E-ISSN 1365-2338. DOI 10.1111/epp.12673.
  9. Fountas, S., Wulfsohn, D., Blackmore, B. S., Jacobsen, H. L. and Pedersen, S. M. (2006) “A model of decision-making and information flows for information-intensive agriculture”, Agricultural Systems, Vol. 87, No. 2, pp. 192-210. ISSN 0308-521X. DOI 10.1016/j.agsy.2004.12.003.
  10. Goldsborough, P. (2016) “A tour of tensorflow”, arXiv preprint arXiv:1610.01178.
  11. Grattarola, D. and Alippi, C. (2020) “Graph neural networks in tensorflow and keras with spectral”, arXiv preprint arXiv:2006.12138.
  12. Han, D., Liu, Q. and Fan, W. (2018) “A new image classification method using CNN transfer learning and web data augmentation”, Expert Systems with Applications, Vol. 95, pp. 43-56. ISSN 0957-4174. DOI 10.1016/j.eswa.2017.11.028.
  13. Hin, D. (2020) “StackOverflow vs Kaggle: A Study of Developer Discussions About Data Science”, arXiv preprint arXiv:2006.08334.
  14. Chen, Y., Jiang, H., Li, C., Jia, X. and Ghamisi, P. (2016) “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 10, pp. 6232-6251. ISSN 0196-2892. DOI 10.1109/TGRS.2016.2584107.
  15. Chollet, F. (2018) “Keras: The python deep learning library”, ascl, ascl-1806.
  16. Chollet, F. and Allaire, J. J. (2018) “Deep Learning mit R und Keras: Das Praxis-Handbuch von den Entwicklern von Keras und RStudio”, MITP-Verlags GmbH & Co. KG. ISBN-10: 3958458939, ISBN-13: 978-3958458932.
  17. Jabir, B. and Falih, N. (2020) “Digital agriculture in Morocco, opportunities and challenges”, In 2020 IEEE 6th International Conference on Optimization and Applications (ICOA), IEEE, pp. 1-5. IEEE. DOI 10.1109/ICOA49421.2020.9094450.
  18. Jarolímek, J., Pavlík, J., Kholova, J. and Ronanki, S. (2019) “Data Pre-processing for Agricultural Simulations”, Agris on-line Papers in Economics and Informatics, Vol. 11, No. 1, pp. 49-53. ISSN 1804-1930. DOI 10.7160/aol.2019.110105.
  19. Jiang, H., Zhang, C., Qiao, Y., Zhang, Z., Zhang, W. and Song, C. (2020) “CNN feature based graph convolutional network for weed and crop recognition in smart farming”, Computers and Electronics in Agriculture, Vol. 174, ISSN 0168-1699. DOI 10.1016/j.compag.2020.105450.
  20. Jursík, M., Soukup, J. and Kolářová, M. (2020) “Sugar beet varieties tolerant to ALS-inhibiting herbicides: A novel tool in weed management”, Crop Protection, Vol. 137, ISSN 0261-2194. DOI 10.1016/j.cropro.2020.105294.
  21. Kartal, S., Choudhary, S., Stočes, M., Šimek, P., Vokoun, T. and Novák, V. (2020) “Segmentation of Bean-Plants Using Clustering Algorithms”, AGRIS On-line Papers in Economics and Informatics, Vol. 12, No. 3, pp. 36-43. ISSN 1804-1930. DOI 10.7160/aol.2020.120304.
  22. Khaki, S., Wang, L. and Archontoulis, S. V. (2020) “A CNN-RNN Framework for Crop Yield Prediction”, Frontiers in Plant Science, Vol. 10. E-ISSN 1664-462X. DOI 10.3389/fpls.2019.01750.
  23. Kim, H. C. and Kang, M. J. (2020) “A comparison of methods to reduce overfitting in neural networks”, International Journal of Advanced Smart Convergence, Vol. 9, No. 2, pp. 173-178. E-ISSN 2288-2855. ISSN 2288-2847. DOI 10.7236/IJASC.2020.9.2.173.
  24. Luus, F., Khan, N. and Akhalwaya, I. (2019) “Active Learning with TensorBoard Projector”, arXiv preprint arXiv:1901.00675.
  25. Nagpal, A. and Gabrani, G. (2019) "Python for Data Analytics, Scientific and Technical Applications," Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates, 2019, pp. 140-145. DOI 10.1109/AICAI.2019.8701341.
  26. Nakazawa, T. and Kulkarni, D. V. (2018) “Wafer map defect pattern classification and image retrieval using convolutional neural network”, IEEE Transactions on Semiconductor Manufacturing, Vol. 31, No. 2, pp. 309-314. E-ISSN 1558-2345, ISSN 0894-6507. DOI 10.1109/TSM.2018.2795466.
  27. Taud, H. and Mas, J. (2018) "Multilayer Perceptron (MLP)", In: Camacho Olmedo M., Paegelow M., Mas JF., Escobar F. (eds) Geomatic Approaches for Modeling Land Change Scenarios. Lecture Notes in Geoinformation and Cartography. Springer, Cham. DOI 10.1007/978-3-319-60801-3_27.
  28. Xu, Q., Zhang, M., Gu, Z. and Pan, G. (2019) “Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs”, Neurocomputing, Vol. 328, pp. 69-74. ISSN 0925-2312. DOI 10.1016/j.neucom.2018.03.080.
  29. Zhang, S., Zhang, X., Chan, J. and Rosso, P. (2019) “Irony detection via sentiment-based transfer learning”, Information Processing & Management, Vol. 56, No. 5, pp. 1633-1644. ISSN 0306-4573. DOI 10.1016/j.ipm.2019.04.006.

Full paper

  Full paper (.pdf, 2.33 MB).