Retrospection On the Applications of Machine Learning Techniques in The Digital Agricultural Management
Keywords:
Machine learning (ML), Deep Learning, digital agriculture managementAbstract
Agriculture is currently experiencing a period of transition. Farmers are faced with the challenges of increasing food production despite a changing climate and an expanding population, all while adjusting to new technologies that have irrevocably changed agriculture. These challenges are compounded by the fact that agriculture has been forever transformed by technological advancements. A new era of digital agriculture management has been ushered in as a result of the application of machine learning to numerous facets of agriculture, such as the scheduling of irrigation and the control of insects. This article explores agricultural application cases for machine learning and deep learning, which may assist farmers in confronting these challenges head-on. Depending on the specific needs of each agricultural application, a variety of machine learning methods could be developed. It will be beneficial for data scientists to have a high level of understanding about use cases and the machine learning technologies that are linked with them.
References
[1] T. Li, K. Johansen, and M. F. McCabe, "A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data," ISPRS J. Photogramm. Remote Sens., vol. 186, no. February, pp. 83-101, 2022, https://doi.org/10.1016/j.isprsjprs.2022.02.002
[2] R. Aworka et al., "Agricultural decision system based on advanced machine learning models for yield prediction: Case of East African countries," Smart Agric. Technol., vol. 2, no. March, p. 100048, 2022, https://doi.org/10.1016/j.atech.2022.100048
[3] J. True, R. Bassiouny, N. Razfar, V. Venkatesh, and R. Kashef, "Weed detection in soybean crops using custom lightweight deep learning models," J. Agric. Food Res., vol. 8, no. April, p. 100308, 2022, https://doi.org/10.1016/j.jafr.2022.100308
[4] J. Pant, R. P. Pant, M. Kumar Singh, D. Pratap Singh, and H. Pant, "Analysis of agricultural crop yield prediction using statistical techniques of machine learning," Mater. Today Proc., vol. 46, no. xxxx, pp. 10922-10926, 2021, https://doi.org/10.1016/j.matpr.2021.01.948
[5] T. Kasinathan, D. Singaraju, and S. R. Uyyala, "Insect classification and detection in field crops using modern machine learning techniques," Inf. Process. Agric., vol. 8, no. 3, pp. 446-457, 2021, https://doi.org/10.1016/j.inpa.2020.09.006
[6] R. Sujatha, J. M. Chatterjee, N. Z. Jhanjhi, and S. N. Brohi, "Performance of deep learning vs machine learning in plant leaf disease detection," Microprocess. Microsyst., vol. 80, no. November 2020, p. 103615, 2021, https://doi.org/10.1016/j.micpro.2020.103615
[7] A. Hamrani, A. Akbarzadeh, and C. A. Madramootoo, "Machine learning for predicting greenhouse gas emissions from agricultural soils," Sci. Total Environ., vol. 741, p. 140338, 2020, https://doi.org/10.1016/j.scitotenv.2020.140338
[8] T. U. Rehman, M. S. Mahmud, Y. K. Chang, J. Jin, and J. Shin, "Current and future applications of statistical machine learning algorithms for agricultural machine vision systems," Comput. Electron. Agric., vol. 156, no. December 2018, pp. 585-605, 2019,https://doi.org/10.1016/j.compag.2018.12.006
[9] G. F. H. van G. Bekker, M. Addison, P. Addison, and A. van Niekerk, "Using machine learning to identify the geographical drivers of Ceratitis capitata trap catch in an agricultural landscape," Comput. Electron. Agric., vol. 162, no. August 2018, pp. 582-592, 2019,https://doi.org/10.1016/j.compag.2019.05.008
[10] B. Espejo-Garcia, J. Martinez-Guanter, M. Pérez-Ruiz, F. J. Lopez-Pellicer, and F. Javier Zarazaga-Soria, "Machine learning for automatic rule classification of agricultural regulations: A case study in Spain," Comput. Electron. Agric., vol. 150, no. May, pp. 343-352, 2018, https://doi.org/10.1016/j.compag.2018.05.007
[11] A. Chlingaryan, S. Sukkarieh, and B. Whelan, "Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review," Comput. Electron. Agric., vol. 151, no. May, pp. 61-69, 2018, https://doi.org/10.1016/j.compag.2018.05.012
[12] E. J. Coopersmith, B. S. Minsker, C. E. Wenzel, and B. J. Gilmore, "Machine learning assessments of soil drying for agricultural planning," Comput. Electron. Agric., vol. 104, pp. 93-104, 2014,https://doi.org/10.1016/j.compag.2014.04.004
