ESTIMATION OF CROP RECOMMENDATION USING GENERATIVE ADVERSARIAL NETWORK WITH OPTIMIZED MACHINE LEARNING MODEL
DOI:
https://doi.org/10.48047/gve32p11Keywords:
Yield Prediction, Agricultural Recommender System, Soil Parameters, Crop Selection, Fertilizer Recommendation, Rule-Based Machine Learning, Agroyield Predictor FrameworkAbstract
Agricultural productivity depends on soil characteristics, climate conditions, and appropriate farming practices. The proposed AgroYield Predictor Framework integrates crowd-sourced agronomic data, soil parameters, climatic conditions, and machine learning models to provide optimized crop and fertilizer recommendations. The system leverages Generative Adversarial Networks (GAN) to enhance microclimate data and improve predictive accuracy. A rule-based machine learning model is employed to process spatial and temporal datasets, utilizing farmer input parameters such as soil conditions, temperature, wind speed, and fertilizer application rates.
Downloads
References
. https://www.ibm.com/think/topics/recommendation-engine [2] Supriya, M., Tyagi, A. K., & Tiwari, S. (2024). Sensor-Based Intelligent Recommender Systems for Agricultural Activities. In AI Applications for Business, Medical, and Agricultural Sustainability (pp. 197-235). IGI Global
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
