ABSTRACT
Agro advisory systems play a crucial role in enhancing agricultural productivity and ensuring food security, particularly in developing nations where agriculture drives economic growth. The advancements in agricultural technology have resulted in the generation of substantial volumes of data, commonly known as big data. Utilizing big data analytics is the key to effectively storing and analyzing such extensive data to enhance agricultural practices and productivity. Agro advisory systems leverage weather data, soil information, crop models, and other pertinent data to offer guidance on crop management. This study proposes a Big Data framework that facilitates data acquisition from multiple sources, efficient storage, analysis, and crop recommendations. The suggested framework incorporates real-time soil and weather data and applies neural networks and decision tree algorithms to suggest suitable crop choices for specific locations. The objective is to provide farmers with effective recommendations that aid them in selecting the most suitable crops based on location-specific parameters
