An Intelligent Framework For Agricultural Growth Forecasting Using Weather-Based Deep Learning Models
DOI:
https://doi.org/10.29284/pz5hp896Keywords:
Agricultural growth prediction, Weather forecasting, Deep learning, Machine learning, Smart agricultureAbstract
Accurate forecasting of agricultural growth is essential for improving crop productivity, food security, and sustainable farming practices. Agricultural growth is strongly influenced by weather conditions such as temperature, rainfall, humidity, and soil moisture, which vary dynamically over time. Traditional statistical and machine learning models often struggle to capture complex; non-linear relationships present in large-scale agricultural and climatic datasets. Recent advances in deep learning provide powerful tools for modelling such complex patterns and improving prediction accuracy. This research proposes an intelligent framework for agricultural growth forecasting using weather-based deep learning models. The framework integrates historical weather data and crop-related features to predict agricultural growth more accurately. Deep learning models are developed and evaluated, and their performance is compared with traditional machine learning approaches. Experimental results demonstrate that deep learning models achieve higher accuracy and lower prediction error due to their ability to learn temporal and spatial dependencies from weather data. The proposed framework supports data-driven decision-making and can assist farmers and agricultural planners in improving crop management strategies.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
