Open Access
Issue
E3S Web of Conf.
Volume 507, 2024
International Conference on Futuristic Trends in Engineering, Science & Technology (ICFTEST-2024)
Article Number 01078
Number of page(s) 9
DOI https://doi.org/10.1051/e3sconf/202450701078
Published online 29 March 2024
  1. Pawlak, Karolina, and Małgorzata Kołodziejczak. “The role of agriculture in ensuring food security in developing countries: Considerations in the context of the problem of sustainable food production.” Sustainability 12.13 (2020): 5488. [CrossRef] [Google Scholar]
  2. Norton, George W., and Jeffrey Alwang. “Changes in agricultural extension and implications for farmer adoption of new practices.” Applied Economic Perspectives and Policy 42.1 (2020): 8-20. [CrossRef] [Google Scholar]
  3. Khan, Nawab, et al. “Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture.” Sustainability 13.9 (2021): 4883. [CrossRef] [Google Scholar]
  4. Chen, Yuanzhe, et al. “AgriKG: an agricultural knowledge graph and its applications.” Database Systems for Advanced Applications: DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Chiang Mai, Thailand, April 22– 25, 2019, Proceedings 24. Springer International Publishing, 2019. [Google Scholar]
  5. Rajamanickam, Jayalakshmi, and Savitha Devi Mani. “Kullback chi square and Gustafson Kessel probabilistic neural network based soil fertility prediction.” Concurrency and Computation: Practice and Experience 33.24 (2021): e6460. [CrossRef] [Google Scholar]
  6. Katarya, Rahul, et al. “Impact of machine learning techniques in precision agriculture.” 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE). IEEE, 2020. [Google Scholar]
  7. Klerkx, Laurens, Emma Jakku, and Pierre Labarthe. “A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda.” NJAS-Wageningen journal of life sciences 90 (2019): 100315. [Google Scholar]
  8. Shadrin, Dmitrii, et al. “Enabling precision agriculture through embedded sensing with artificial intelligence.” IEEE Transactions on Instrumentation and Measurement 69.7 (2019): 4103-4113. [Google Scholar]
  9. Anushabai, D., et al. “Insights Into Yield Estimation Of Horticultural Crops Using Machine Learning Algorithms.” [Google Scholar]
  10. Suchithra, M.S.; Pai, M.L. Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Inf. Process. Agric. 2020, 7, 72–82. [CrossRef] [Google Scholar]
  11. Chambers, O. Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method. Sensors 2021, 21, 4208. [CrossRef] [CrossRef] [PubMed] [Google Scholar]
  12. Wu, C.; Chen, Y.; Hong, X.; Liu, Z.; Peng, C. Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques. For. Ecosyst. 2020, 7, 30. [CrossRef] [CrossRef] [Google Scholar]
  13. Rose, S.; Nickolas, S.; Sangeetha, S. Machine Learning and Statistical Approaches used in Estimating Parameters that Affect the Soil Fertility Status: A Survey. In Proceedings of the 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), Karnataka, India, 16–18 August 2018; IEEE: New York, NY, USA, 2018; pp. 381–385. [Google Scholar]
  14. Rajamanickam, J. Predictive model construction for prediction of soil fertility using decision tree machine learning algorithm. Infocomp J. Comput. Sci. 2021, 20, 49–55. [Google Scholar]
  15. Bang, Shivam, et al. “Fuzzy Logic based Crop Yield Prediction using Temperature and Rainfall parameters predicted through ARMA, SARIMA, and ARMAX models.” 2019 Twelfth international conference on contemporary computing (IC3). IEEE, 2019. [Google Scholar]
  16. Veenadhari, S., Bharat Misra, and C. D. Singh. “Machine learning approach for forecasting crop yield based on climatic parameters.” 2014 International Conference on Computer Communication and Informatics. IEEE, 2014. [Google Scholar]
  17. Rehman, A.; Liu, J.; Li, K.; Mateen, A.; Yasin, Q. Machine Learning Prediction Analysis using IoT for Smart Farming. Int. J. Emerg. Trends Eng. Res. 2020, 8, 1–30. [Google Scholar]
  18. Priya, P.K.; Yuvaraj, N. An IoT Based Gradient Descent Approach for Precision Crop Suggestion using MLP. J. Phys. Conf. Ser. 2019, 1362, 012038. [CrossRef] [CrossRef] [Google Scholar]
  19. Biradar, H.B.; Shabadi, L. Review on IOT based multidisciplinary models for smart farming. In Proceedings of the 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 19–20 May 2017; pp. 1923–1926. [Google Scholar]

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