Implementation of IoT and Data Analytics in Smart Agricultural System
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9442-7_33
Reference14 articles.
1. Gandhi N et al (2016) Rice crop yield forecasting of tropical wet and dry climatic zone of India using data mining techniques. In: IEEE international conference on advances in computer applications (ICACA)
2. Eswari KE, Vinitha L. Crop yield prediction in Tamil Nadu using Bayesian network. Int J Intell Adv Res Eng Comput 6(2). ISSN: 2348-2079
3. Mishra S, Paygude P, Chaudhary S, Idate S. Use of data mining in crop yield prediction. In: IEEE Xplore compliant-part number: CFP18J06-ART. ISBN: 978-1-5386-0807-4; DVD part number: CFP18J06DVD. ISBN: 978-1-5386-0806-7
4. Chlingaryana A, Sukkarieha S, Whelanb B (2018) Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput Electron Agric 151:61–69
5. Patil D et al (2017) Rice crop yield prediction using data mining techniques: an overview. Int J Adv Res Comput Sci Softw Eng 7(5)
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