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. [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. [Google Scholar]
  3. Khan, Nawab, et al. “Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture.” Sustainability 13.9 (2021): 4883. [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. [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] [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] [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] [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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.