Farm weed detection and management model complete with deployment
Leaf Detection System using OpenCV
Computer Vision is a field of study that helps to develop techniques to recognize images and displays. It has different features like image recognition, object detection, image creation, etc.
We can detect objects present in an image like a human face, animal face, eyes, etc. We can use OpenCV to detect objects present in an image. OpenCV has many pre-trained models based on its features.
Our Leaf Detection System detects leaves from the image using Convolutional Neural Network (CNN) and OpenCV. It will also detect the type of leaf once it detects whether the image contains an image that is been provided.
In this system, the user will need to register first to log in to the system. With the credentials, they can log in to the system. The system will detect the leaf and the leaf type from an uploaded image. The user will just need to upload an image that contains a leaf in it.
This system will automatically detect the leaf from the image and once it finds the leaf it will further detect which type of leaf it is. The user will also be able to see the accuracy score.
The front end involves Html, CSS, and JavaScript and the back end involves Python. The framework used is Django and the database is MySQL. Here, we have implemented the OpenCV and CNN library. The dataset has been extracted from Kaggle.
The system comprises 1 major module with their sub-modules as follows:
The waterfall model is a classical model used in the system development life cycle to create a system with a linear and sequential approach. It is termed a waterfall because the model develops systematically from one phase to another in a downward fashion. The waterfall approach does not define the process to go back to the previous phase to handle changes in requirements. The waterfall approach is the earliest approach that was used for software development.
Requirement:
No previous knowledge required
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