The purpose of this study is to create a Smart Agriculture system which is built by advanced systems like the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Unmanned Aerial Vehicles (UAV) to improve the precision of crop management practices and predict yields. The system aims to change the farming practices by improving the identification of plant diseases using the image processing and machine learning techniques, monitoring the health of the plants using the IoT sensors, and performing irrigation using the irrigation system automated through sensors and weather prediction. Also, solar energy is utilized by the system, which helps in crossing the barrier of environmental sustainability. A notable feature of this system is deployment of Unmanned Aerial Vehicles (UAVs) with multispectral imagers for capturing aerial images and facilitating analysis of crop condition, including insufficiency in nutrients or proliferation of pests. This information allows for the focused use of fertilizers and pesticides, which minimizes waste and lowers the environmental impact. Also, due to the ability to efficiently field scouted and monitored crops, most problems have been solved in time before corrective actions are taken with a quasi-expert system approach. The system predicts optimal yields by algorithmically relating historical data, real time sensor data and weather data.