Published Paper Details:

Agriculture and plant sciences face unprecedented challenges due to climate change, population growth, and resource scarcity. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies, enabling precision farming, early disease detection, yield prediction, and enhanced plant phenotyping. This review synthesizes recent advancements, drawing from diverse applications such as convolutional neural networks (CNNs) for image-based disease identification, predictive analytics for crop optimization, and IoT-integrated systems for real-time monitoring. Key benefits include reduced crop losses, optimized resource use, and improved food security. However, challenges like data privacy, model interpretability, and integration barriers persist. By examining literature from 2023-2025, this paper highlights AI's role in sustainable agriculture, including genomics, stress detection, and robotics. Future directions emphasize interdisciplinary collaboration and robust AI frameworks to address global agricultural demands.