Advanced Application Development in Agriculture—Issues and Challenges

Author:

Gandhi Purnima

Publisher

Springer Nature Singapore

Reference24 articles.

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2. Sahu S, Chawla M, Khare N (2017) An efficient analysis of crop yield prediction using Hadoop framework based on random forest approach. In: 2017 international conference on computing, communication and automation (ICCCA). IEEE, pp 53–57

3. Majumdar J, Naraseeyappa S, Ankalaki S (2017) Analysis of agriculture data using data mining techniques: application of big data. J Bigdata 4(1):20

4. Pantazi XE, Moshou D, Alexandridis T, Whetton RL, Mouazen AM (2016) Wheat yield prediction using machine learning and advanced sensing techniques. Comput Electron Agric 121:57–65

5. Singh RS, Gelmecha DJ, Ayane TH, Sinha DK (2022) Functional framework for IoT-based agricultural system. In: AI, Edge and IoT-based Smart Agriculture. Academic Press, pp 43–69

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