Project Outline
This project will investigate the use of convolutional neural networks (CNNs) for automated leaf disease classification. We will use a dataset of images of three different species of plants with known disease labels.
The goal of this project is to build a convolutional neural network model that can accurately classify leaf images into healthy and diseased categories.
- Collect and label the dataset of images for the three different species of plants. We will then preprocess the images to ensure that all the images are of the same size and resolution.
- Then, we will create the convolutional neural network model and train it using the labelled dataset.
- Finally, will evaluate the performance of the model by testing it on unseen data.
This project will enable us to gain a better understanding of how well convolutional neural networks can be used for automated leaf disease classification. It will also provide us with insights into how different parameters can affect the model’s performance.
Software/Hardware requirement:
Hardware requirement
- Graphics Processing Unit (GPU)
- A minimum of 8GB RAM
- Hard disk with at least 200GB of storage
Software requirement
- Python programming language
- TensorFlow or Keras deep learning frameworks
- OpenCV computer vision library
Tool:
IDLE is Python’s Integrated Development and Learning Environment. It allows programmers to easily write Python code. Just like Python Shell, IDLE can be used to execute a single statement and create, modify, and execute Python scripts.
Technology:
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
What you will learn?
- Build, train, deploy AI models.
- Develop Deep Learning model to automate and optimize the leaf disease detection.
- Creation of a graph showing model accuracy for both training and testing
- Matrix of plot confusion for several classes
- Shows the performance metrics Specificity, Sensitivity, Accuracy, respectively.
- Create a web-based or Windows application an user interface.
