Agricultural price prediction through artificial intelligence

Agriculture is a critical sector in India, contributing significantly to the economy and supporting the livelihoods of millions. However, the sector faces challenges due to the unpredictability of agricultural prices, which can fluctuate due to various factors, including global market trends, weather conditions, and government policies. In recent years, artificial intelligence (AI) has emerged as a powerful tool to forecast agricultural prices, offering the potential to improve decision-making and mitigate risks for farmers, traders, and other stakeholders. This research paper provides a comprehensive review and analysis of AI-driven agricultural price prediction techniques. It examines traditional methods and their limitations, highlighting the need for AI-driven solutions. The paper discusses various AI models, including machine learning algorithms like linear regression, random forest, and LSTM, and their application in predicting agricultural prices. It also explores the role of AI in enhancing market transparency, optimizing resource allocation, and improving decision-making in agriculture. Furthermore, the paper discusses the real-world impacts and benefits of AI-driven price prediction for agricultural stakeholders. It highlights how AI can help farmers increase profitability, reduce risk, and optimize resource allocation. The paper also discusses the challenges and ethical considerations associated with AI in agriculture, emphasizing the importance of creating policies to address these issues. Overall, this paper demonstrates the potential of AI in revolutionizing agricultural price prediction and its impact on the agriculture sector in India. It concludes with recommendations for policymakers, stakeholders, and researchers to further develop and implement AI-driven solutions for forecasting crop prices, ultimately supporting the prosperity of Indian farmers and ensuring food security.