Published Paper Details:

Agriculture continues to be the cornerstone of economic growth and food security across the globe, particularly in developing nations. With increasing population pressure, climate change, and the need for sustainable resource management, the integration of Artificial Intelligence (AI) and Machine Learning (ML) presents transformative opportunities for modernizing agriculture. AI and ML technologies enable data-driven decision-making by analyzing vast agricultural datasets to optimize crop production, manage resources efficiently, and predict potential threats such as pests, diseases, and extreme weather conditions. From precision irrigation and soil health monitoring to automated machinery and market forecasting, these technologies are reshaping the entire agricultural value chain. Despite their immense potential, the adoption of AI and ML in agriculture faces significant challenges. High implementation costs, inadequate digital infrastructure in rural areas, and limited technical knowledge among farmers hinder large-scale deployment. Issues related to data privacy, inconsistent datasets for ML model training, and ethical concerns about automation and employment further complicate the adoption process. To bridge these gaps, a multi-stakeholder approach involving governments, research institutions, agritech startups, and farmer communities is essential. This paper explores the applications, opportunities, and challenges of AI and ML in advancing agriculture. It highlights how these technologies can enhance productivity, promote sustainability, and empower smallholder farmers through accessible, low-cost innovations. Ultimately, AI and ML hold the promise of transforming agriculture into a smart, sustainable, and resilient sector, contributing significantly to global food security and rural development.