AI-Based Drought Prediction for Farmers: Analysis of Crop Yield and Its Relationship with Weather Variables

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@article{1717987,
      author = {Vijay R, Dr. Ganesh D},
      title = {AI-Based Drought Prediction for Farmers: Analysis of Crop Yield and Its Relationship with Weather Variables},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {2643-2651},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717987.pdf},
      abstract = {Drought is a severe natural event that affects agriculture greatly, particularly in regions where rain-fed agriculture is the main source of food. It has a major detrimental impact on society, the environment, and the lives of farmers. Drought prediction is an important component of early warning systems for drought management. Advances in machine learning (ML) and the Internet of Things  have demonstrated potential to improve drought prediction and provide sustainable farming solutions.This paper proposes a machine-learning-based AI solution to predict drought occurrences through the conduit of the processed climatic, hydrological, and soil data which are collected in various ways. The experimental model is incorporating the historical data of rainfall patterns, temperature fluctuations, soil moisture levels, and others into the predictions with the highest possible probability of drought conditions. To predict the occurrence of droughts and thus act, the proposed framework integrates data pre-processing techniques, such as feature selection and machine learning algorithms such as Neural Networks, Gradient Boosting, and Random Forest, with the help of which the proposed method is approximately as good as or even better than using traditional methods in the drought prediction in regions with different climates.},
      keywords = {An AI-Based Drought Prediction System for Farmers Integrates Multi-Source Data, Processes It Through Machine Learning Models, And Delivers Actionable Alerts. This Approach Enables Early Identification of Drought, Allowing for Proactive Water Management},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717987}
  }