Agriculture not only provides food for humans, but it is also a major source of revenue for any nation. Millions of dollars are spent every year to protect rice crops from insects and pests that cause damage during harvest and storage. Early pest detection, which allows the crop to be protected from pest attack, is one form of crop protection. The best way to learn about the health of a crop is to examine it regularly. If pests are discovered, adequate steps may be taken to prevent the crop from suffering a major loss of yield. Early detection will help to reduce the use of pesticides and direct the pesticide selection process. It has grown into a large field of science, with a lot of work being done around the world to detect pests automatically. The typical way of inspecting the fields is with the naked eye. A farmer must manually search and assess over a vast landscape of fields, risking overlooking different affected areas and conducting thorough research across large lots. To analyse the entire area, several human experts are needed, which is both costly and time-consuming. This proposed system is mainly intended to develop an Intelligent IT-driven system using various Artificial Intelligence and Computer Vision Algorithms for precision farming, enabling the delivery of information directly to the farmer's phone, providing the details of damage localization, crop health, and needs for fertilizer and pesticide application.