The global agricultural landscape is facing unprecedented challenges, with farmers grappling to maintain optimal crop yields, indiscriminate usage of herbicides with prolonged repeat cycles creates health risks for humans, use of chemical herbicides to control weeds and contaminate soil, water sources, and even the harvested crops, leading to a range of health and ecological issues. Interfering in the ecosystem. This project aims to use artificial intelligence for image processing to categorize weeds and limit the use of chemicals for weed control. A custom data set annotating various weed category images was created to train the neural network. Users must feed the field area image to the trained model as input. This image passes through a neural network to detect weeds or crops in the specified image. Users get a weed category or healthy crop image as an output. Based on the detected weed timely intervention by the user to remove the weed can be initiated. This image can be a satellite image at a fixed duration from the field to send an alert message to the landowner for weed detection in the crop life cycle. Timely intervention of pest infestation etc. can also be detected. Removing weeds or weed plants will reduce the use of blanket chemical herbicides. This will not only reduce the use of chemicals but also improve the crop yield significantly.