The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Forecasting yield by integrating agrarian factors and machine learning models: A survey
Abstract
Abstract The advancement in science and technology has led to a substantial amount of data from various fields of agriculture to be incremented in the public domain. Hence a desideratum arises from the investigation of the available data and integrating them with a process like a crop improvement, yield prediction, crop disease analysis, identifying water stress, and so on. Computing techniques like Machine learning is a new advent for the analysis and resoluteness of these intricate issues. Various analytical models like Decision Trees, Random Forests, Support Vector Machines, Bayesian Networks, and Artificial Neural Networks, and so on, have been utilized for engendering the models and analyze the results. These methods enable to analyze soil, climate, and water regime which are significantly involved in crop growth and precision farming. This survey incorporates an overview of some of the existing supervised and unsupervised machine learning models associated with the crop yield in literature. Moreover, this survey compares one approach with other using various error measures like Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2).
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Cites
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Cited by
An overview of smart irrigation systems using IoT
Obaideen, K., Yousef, B. A. A., AlMallahi, M. N., Tan, Y. C., Mahmoud, M., Jaber, H., & Ramadan, M. (2022) · Energy Nexus · doi:10.1016/j.nexus.2022.100124 · Cited by 448
Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021) · IEEE Access · doi:10.1109/access.2021.3075159 · Cited by 369
Condran, S., Bewong, M., Islam, M. Z., Maphosa, L., & Zheng, L. (2022) · IEEE Access · doi:10.1109/access.2022.3188649 · Cited by 99
Metadata
- Authors
- Elavarasan, D., Vincent, D. R., Sharma, V., Zomaya, A. Y., & Srinivasan, K.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 155
- Pages
- 257-282
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2898543370
- Cited by
- 346
- References
- 79
- FWCI
- 16.79
- Type
- journal article
- Language
- en
- Open access
- closed
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- Elavarasan, D., Vincent, D. R., Sharma, V., Zomaya, A. Y., & Srinivasan, K. (2018). Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.10.024
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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