Agricultural Yield Forecasting with the Fusion of Transfer Learning and LSTM Approach
Boppudi Swanth, J Sheela · 2024
This study addresses the challenges inherent in crop yield prediction by proposing a novel approach that combines transfer learning and Long Short-Term Memory (LSTM) techniques. Existing methodologies face obstacles related to limited accuracy and adaptability, particularly in handling diverse agricultural datasets. Recent techniques, including transfer learning, have shown promise in leveraging knowledge from disparate domains, but challenges persist in effectively applying these techniques to agricultural data. The proposed approach aims to overcome these challenges by synergistically employing transfer learning and LSTM. Transfer learning enhances the model's analytical capabilities by leveraging knowledge from various domains, providing a broader foundation for agricultural dataset analysis. Additionally, LSTM, renowned for capturing temporal dependencies, further refines the model's predictive capacity, addressing challenges related to dynamic agricultural processes and environmental factors. The research methodology involves the implementation and fine-tuning of transfer learning and LSTM models on relevant agricultural datasets, followed by a rigorous evaluation of their performance. The study anticipates uncovering a novel and effective solution for optimizing crop yield predictions. By explicitly addressing challenges faced by current systems and integrating recent techniques, this research contributes to precision farming and sustainable agricultural practices. The anticipated findings hold the potential to empower farmers with valuable insights, facilitating improved decision-making and resource allocation in the realm of agriculture.