Empirical Analysis of Crop Yield Prediction and Disease Detection Systems: A Statistical Perspective-Reference-Cited by-同舟云学术

Empirical Analysis of Crop Yield Prediction and Disease Detection Systems: A Statistical Perspective

Published:2022-11-08 Issue: Volume: Page:49-57
ISSN:2367-3370
Container-title:ICT Infrastructure and Computing
language:
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Author:

Lohi Snehal A.,Bhatt Chinmay

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-5331-6_6

Reference11 articles.

1. Bhojani SH, Bhatt N (2020) Wheat crop yield prediction using new activation functions in neural network. Neural Comput Appl 32:13941–13951. https://doi.org/10.1007/s00521-020-04797-8

2. Prasad NR, Patel NR, Danodia A (2021) Crop yield prediction in cotton for regional level using random forest approach. Spat Inf Res 29:195–206. https://doi.org/10.1007/s41324-020-00346-6

3. Elavarasan D, Vincent PMD (2020) Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE Access 8:86886–86901. https://doi.org/10.1109/ACCESS.2020.2992480

4. Anderson SL et al, Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems. Plant Phenome J 2(1). https://doi.org/10.2135/tppj2019.02.0004

5. Tiwari P, Shukla P (2018) Crop yield prediction by modified convolutional neural network and geographical indexes. Int J Comput Sci Eng 6(8):503–513

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