Data-driven Comprehensive Predictive Model Leveraging Market Insights and Topographical Factors for Crop Productivity and Profitability
Keywords:
crop productivity, prediction model, profit maximization, market trends, topographical factorsAbstract
For global food security and economic expansion, the agricultural industry is essential. However, farmers face numerous challenges such as unpredictable weather patterns, soil degradation, and market volatility, which affect crop productivity and profitability. This article proposes a prediction model that leverages market trends and topographical factors to optimize crop productivity and maximize profits. The model is designed using machine learning techniques and takes into account variables such as soil type, temperature, precipitation, and market prices. The results of the study demonstrate that the proposed model outperforms traditional crop management methods in terms of crop yield and profitability. The findings suggest that the model could be a useful tool for farmers and agricultural policymakers to make informed decisions regarding crop management and planning. The study also identifies the need for further research to validate and refine the proposed model, as well as to address potential challenges in its implementation, such as data availability and accessibility.
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