The goal of smart agriculture, a new sector driven by technological breakthroughs, is to transform conventional farming methods through the integration of many technologies, including drones, artificial intelligence (AI), Internet of Things (IoT), and data analytics. An overview of smart agriculture is given in this paper, along with how it might be used to solve some of the major issues confronting the agricultural industry, such as rising food prices, water scarcity, climate change, and labor shortages. Farmers may use IoT sensors to precisely plan irrigation and fertilization schedules by tracking crop health, temperature, and soil moisture levels in real-time which can be resolved by integrating edge-computing. Artificial intelligence (AI) algorithms examine enormous volumes of sensor and drone data to offer insights about agricultural illnesses, pest infestations, and the best times to grow. Farmers can identify crop stress and track growth patterns by using high-resolution photographs of fields taken by drones fitted with cameras and multispectral imaging sensors. With the least amount of negative environmental impact, farmers may improve crop yields, optimize resource allocation, and make data-driven decisions with the use of data analytics technologies. In order to ensure food security for future generations, smart agriculture has the potential to change the agricultural sector into one that is more productive, efficient, and sustainable. In this paper we proposed a classifier named as CUDA-EECA (Enhanced Ensembled Crop Recommendation Algorithm) Model for Crop Quality Estimation using Edge Computing. And compared with base classifiers like Decision Tree and SVM in which the proposed classifier gave the best accuracy when compared with the other 2 i.e., ~96%.