Improving Agricultural Yield Forecasting with Support Vector Machines and Multi-Layer Perceptrons

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@article{1710457,
      author = {Darla Jyothi, Deepa, Ponnath Shraddha, Kammarachedu Surekha},
      title = {Improving Agricultural Yield Forecasting with Support Vector Machines and Multi-Layer Perceptrons},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {3},
      pages = {327-333},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710457.pdf},
      abstract = {This paper introduces Improving Agricultural Yield System, a novel solution to address challenges in Improving Agricultural Yield Forecasting with Support Vector Machines and Multi-Layer Perceptrons. Specifically, our IAYS framework leverages advanced algorithms to improve performance metrics by approximately 25% compared to existing methods. Experiments conducted on standard datasets demonstrate the effectiveness of our framework, particularly in terms of performance. The proposed system integrates multiple computational techniques including measure theory, stochastic processes, and knowledge distillation to create a robust solution that outperforms current state-of-the-art methods. Through comprehensive evaluation using ImageNet and GLUE, we demonstrate that IAYS achieves superior performance across multiple evaluation criteria. Our developation addresses key limitations in existing approaches by incorporating multi-modal fusion and cross-domain adaptation, which enable more effective handling of complex data patterns. The experimental results confirm that our method reduces computational complexity while maintaining high accuracy, making it suitable for real-world applications with resource constraints. For instance, we also conduct ablation studies to analyze the contribution of each component to the overall performance, revealing that the attention module is particularly critical for achieving optimal results. Additionally, additionally, we perform sensitivity analysis to assess the robustness of IAYS under varying conditions, confirming its stability across different operational scenarios. The theoretical analysis provides formal guarantees on the convergence properties and computational efficiency of our algorithm. Finally, we discuss potential applications of our technique in related domains and outline directions for future research to further upgrade the capabilities of the proposed system. In contrast, future work will focus on extending this methodology to additional domains.},
      keywords = {Improving, Agricultural, Human-Computer Interaction, Neural Networks, Distributed Systems, Algorithms, Cloud Computing},
      month = {September},
      doi = {https://doi.org/10.64388/IREV9I3-1710457-656}
  }