Artificial intelligence driven smart agricultural applica

Agriculture serves as a cornerstone of the economy and a significant source of employment, particularly in developing countries like India. Within the realm of agriculture, there are three pivotal stages: pre-harvesting, harvesting, and post-harvesting. The emerging field of agrotechnology, often referred to as smart or digital agriculture, leverages data-intensive approaches to enhance agricultural productivity while minimizing its ecological footprint. Notably, the demand for artificial intelligence (AI) which includes machine learning (ML) and deep learning (DL) technologies has surged within the agrotechnology sector. This paper offers a comprehensive review of the latest applications of AI in agriculture, aiming to address challenges in the pre-harvesting, harvesting, and post-harvesting phases. It explores how AI and ML models can significantly assist farmers in decision-making processes across various domains and applications such as soil management, weed management, crop management, livestock management, water management etc. The findings of this study underscore the remarkable outcomes achieved by ML algorithms and models in resolving agricultural issues. In light of these findings, it is recommended that ML models be deployed in various real-time applications to provide valuable support to intended users, particularly farmers, in their day-to-day agricultural endeavours.