Plant Leaf Disease Detection using AI-Machine Learning computer vision :Project Outline
The primary occupation in India is agriculture. India ranks second in the agricultural output worldwide. Here in India, farmers cultivate a great diversity of crops. Various factors such as climatic conditions, soil conditions, various disease, etc. affect the production of the crops. The existing method for plants disease detection is simply naked eye observation which requires more man labor, properly equipped laboratories, expensive devices, etc. And improper disease detection may led to inexperienced pesticide usage that can cause development of long term resistance of the pathogens, reducing the ability of the crop to fight back.
The proposed system has concentrated on recognizing the green leaf diseases which assists the farmers to take a proper measurement and increases the production of plants. In this paper, an automated system is proposed for diagnosis three common green leaf diseases (Brown spot,Leaf blast, and Bacterial blight) and pesticides and/or fertilizers are advised according to the severity of the diseases. The type of green leaf diseases is recognized by CNN. After recognition, the predictive remedy is suggested that can help agriculture related people and organizations to take appropriate actions against these diseases.
The goal of this project is to develop a machine learning (ML) model to detect leaf diseases on plants. The ML model will use images of leaves to identify disease symptoms and classify them into different classes of diseases. The data used for training the model will be a combination of labeled images of leaves with different diseases, and unlabeled images of leaves from healthy plants.
The ML model will be trained on a dataset of labeled images of leaves with different diseases, and then tested on a separate dataset of unlabeled images of leaves from healthy plants.
The model will be evaluated based on its accuracy in correctly classifying the different types of leaf diseases. The model can then be deployed for live applications, such as for automated disease detection in agriculture.
Applications
- Early disease detection: Plant leaf disease detection can help identify diseases in their early stages, which can help farmers and growers take preventative measures to stop the spread of the disease and limit crop loss.
- Disease management: Plant leaf disease detection can help farmers and growers manage plant diseases more effectively by identifying the specific type of disease and providing recommendations on treatment options.
- Precision agriculture: Plant leaf disease detection can be used in precision agriculture to target specific areas of a field that may be affected by disease, reducing the amount of resources needed to manage the disease.
- Crop monitoring: Plant leaf disease detection can be used for crop monitoring, helping farmers and growers track the health of their crops over time and identify trends and patterns in disease outbreaks.
- Research: Plant leaf disease detection can be used in research to better understand the mechanisms behind plant diseases and develop new methods for disease prevention and treatment.
Requirements
Hardware Requirements:
- A PC or laptop with at least 4GB RAM
- A camera or a laptop camera
- A high-speed internet connection
Software Requirements:
- Python 3 or higher
- Scikit-learn library
- TensorFlow library
- OpenCV library
- Image processing library
- Any Machine Learning Framework
Tool:
IDLE is an integrated development environment for Python, which has been bundled with the default implementation of the language since 1.5.2b1. It is packaged as an optional part of the Python packaging with many Linux distributions. It is completely written in Python and the Tkinter GUI toolkit.
Technology:
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
What you’ll Learn after doing this project?
- How to pre-process images for machine learning
- How to extract features from images using Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP)
- How to train a machine learning model such as a Support Vector Machine (SVM) or a Convolutional Neural Network (CNN) for image classification
- How to evaluate the accuracy of a machine learning model
- How to deploy a machine learning model for real-world applications
- Understand the strengths and limitations of different machine learning and deep learning algorithms, and be able to choose appropriate algorithms for a given task.
- Understand how to use various tools and libraries (such as Tensor Flow) to build and train machine learning and deep learning models.
- Understand the importance of pre-processing and cleaning data before training a model. This could include techniques such as scaling, normalization, and handling missing or incorrect values.
- Develop an understanding of how to optimize model hyper parameters in order to improve performance. This could include techniques such as grid search, random search, or Bayesian optimization.
- Understand the concept of overfitting and be able to use techniques such as regularization and early stopping to prevent overfitting in their models.
