Published Paper Details:

The increasing complexity of global agricultural systems, coupled with the challenges of population expansion, climate variability, and diminishing natural resources, necessitates the adoption of advanced technological interventions. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools in fostering smart and sustainable agricultural practices. This paper critically examines the role of AI and ML in optimizing various dimensions of farming, including soil fertility assessment, precision irrigation, crop health monitoring, pest and disease detection, and yield forecasting. Through the integration of IoT-enabled sensors, unmanned aerial vehicles (UAVs), and remote sensing data, AI-driven analytics facilitate real-time decision-making and automation, thereby enhancing both efficiency and productivity. The study further explores how intelligent systems contribute to environmental sustainability by minimizing excessive input usage, mitigating greenhouse gas emissions, and promoting adaptive responses to climatic fluctuations. Economic implications such as cost reduction, risk mitigation, and improved value-chain management are also addressed. Despite their potential, the diffusion of AI and ML technologies remains constrained by factors including data scarcity, inadequate digital infrastructure, high deployment costs, and limited technical literacy among smallholders--particularly in developing economies such as India. The paper concludes that the successful realization of AI-enabled sustainable agriculture requires a multi-stakeholder framework encompassing policy support, capacity building, open-data ecosystems, and context-specific algorithmic design. Future research should emphasize the development of explainable, inclusive, and resource-efficient AI systems that align technological innovation with the imperatives of ecological balance and food security.