This project revolves around creating a web-based application for the automation of identifying particular species of crops via artificial intelligence and machine learning. This feature allows users to directly upload their images of crops and after employing certain sophisticated image preprocessing techniques and trained AI model, it predicts the crop species. The frontend is done on React.js which provides a smooth and user-friendly experience and the image uploads and model integration are done through a Flask backend. The system incorporates a large set of images of various crops to train a machine learning on crop species recognition. For obtaining all the steps such as model fitting and making predictions to perform accurately, critical changes like resizing, normalizing, and augmenting of images were carried out. It also ensures that the detailed output such as the name of the most likely species, and continent or country for growth of the crop is presented. This solution addresses the needs of farmers, agricultural experts, and researchers by facilitating the crop recognition process that would enhance agricultural practices and management of resources. In the future, this can be developed further by increasing the dataset to more species and adding field data so that real-time analysis of the crops can be incorporated.