Development of a Crop-Based Predictive System for Optimizing Crop Yield for Farmers

References

[1] S. Sikandar, R. Mahum, & S. Aladhadh, (2022). Automatic Crop Expert System using an Improved LSTM with Attention Block, CSSE, 2023, 47(2), 2017-2022. https://doi.org/10.32604/CSSE.2023.037723

[2] A. Islam, R.A. Ifty, M.N. Arefin, I. Khair, S. Hossain, & J.A. Muhammed, (2023). Ensemble Machine Learning Approach for agricultural crop selection, International Conference on Electrical, Computer and Communication Engineering. https://doi.org/10.1109/ECCE57851.2023.10101585

[3] S. Gowda, & S. Reddy, (2020) Design and implementation of crop yield prediction model in agriculture, International Journal of Scientific & Technology Research, 8(1), 545 – 547.

[4] V. Suraparaju, S. Ujjainia, & P. Gautam, (2021). A crop recommendation System to improve crop productivity using an ensemble technique, Internatonal Journal of Innovative Technology and Exploring Engineering, 10(4), 102-104. https://doi.org/10.35940/IJITEE.D8507.0210421

[5] S.P. Raja, B. Sawicka, Z. Stamenkovic and M. Ganesan, (2022). Crop prediction based on agricultural environmental characteristics using various feature selection techniques and classifiers, 10, 23630. https://doi.org/10.1109/ACCESS.2022.3154350

[6] D. Garg, M. Alam, (2023). An effective crop recommendation method using machine learning techniques. Internatonal Journal of Advanced Technology and Engineering Exploration, 10(102), 501 – 504. http://dx.doi.org/10.19101/IJATEE.2022.10100456

[7] H.M. Monisha, J. K. Dhanush, S.M. Adarsha, & M. Dalli,(2024). A web-based crop recommendation system using various machine learning algorithms. International Journal of Novel Research and Development, 9(2), C162 – C164. https://doi.org/IJNRD.2024.378469022

[8] M. Rashid, B.S. Bari, Y.B. Yusup, M.A. Kamaruddin, & N. Khan, (2021) A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction, IEEE Transactions and Journals, 4(1), 8-12. https://doi.org/10.1109/ACCESS.2021.3075159

[9] A. Ahmed, S.A. Adewunmi, V. Yemi-Peters, (2023). Crop yield prediction in Nigeria using machine learning techniques: (A case study of Southern part of Nigeria), UMYU Scientifica, 2(4), 31-38. https://doi.org/10.56919/usci.2324.004

[10] T.G. Liliane, & S.C. Mutengwa, (2020) Factors affecting yield of crops, Agronomy – Climate change

[11] and Food Security, pg.1. https://dx.doi.org/10.5772/intechopen.90672

[12] M. Harris, (2023, September 19). Understanding Random Forest Methods. Stats with R. Retrieved October 16, 2024, from https://www.statswithr.com

[13] A. Ahmed, S.A. Adewunmi, V. Yemi-Peters, (2023). Seasonal crop yield prediction using machine learning techniques (A case study of Northern Nigeria), FUW Trends in Science and Technology Journal, 8(2), 332-336. https://doi.org/10.48185/jaai.v4i1.728

@article{1706433,
      author = {Kinga Mary Temidayo, Olutayo Kehinde Boyinbode, Olawale Solomon Akintola},
      title = {Development of a Crop-Based Predictive System for Optimizing Crop Yield for Farmers},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {4},
      pages = {706-711},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706433.pdf},
      abstract = {Crop yield prediction is essential for helping farmers make informed decisions, optimize resource allocation, and enhance productivity. This study presents a machine learning-based predictive system that leverages an ensemble approach, combining Bagging Regressor, XGBoost, and Random Forest models to forecast crop yields. Utilizing a comprehensive dataset that includes vital crop parameters such as crop type, area, temperature, rainfall, and soil type, the system provides valuable insights into the factors influencing predicted yields. By identifying key determinants, the system enables farmers to optimize crop management strategies, leading to improved productivity, reduced uncertainty, and enhanced sustainability. The proposed system empowers farmers with data-driven decision-making tools, contributing to food security and promoting sustainable agricultural practices.},
      keywords = {Crop yield Prediction, Ensemble Algorithm, Farmers, Machine Learning, Productivity},
      month = {October},
  }