Leaf Disease Classification using Deep Learning Models -- 2
Budget: ₹1,500 – ₹12,500 INR
I am looking for an experienced machine learning and deep learning expert to build a Leaf Disease Classification Model that can accurately identify and classify plant leaf diseases from images.
Dataset Details:
The dataset contains 87,000 images categorized into 38 different classes, such as Apple healthy, Apple scab, Apple black rot, Tomato botcera, etc.
The dataset is divided into a training and validation set in an 80:20 ratio.
Project Goal:
The objective is to develop a model that can:
1. Identify the correct class (out of 38) to which the leaf belongs.
2. Classify the leaf's health condition as one of the following:
Healthy
Initial stage of disease
Middle stage of disease
Severe damage
3. Analyze the percentage of leaf area affected for severity detection.
4. Provide key performance metrics such as:
Accuracy
Precision
Recall
F1 Score
5. Handle overfitting and underfitting issues explicitly and show how they impact model performance.
Technologies to Use:
Keras API
TensorFlow
Jupyter Notebook
Models to Implement:
1. SVM with HOG (Histogram of Oriented Gradients) for feature extraction
2. SVM with VGG16 (Pre-trained Model) for feature extraction
3. CNN with multiple convolutional layers to directly extract features
Workflow Pipeline:
1. Model Building
2. Training
3. Evaluation
4. Saving the Model
5. Testing and Performance Analysis (Accuracy, Precision, Recall, F1 Score)
Deliverables:
Complete code with proper documentation
Visualization graphs for accuracy and loss
Performance metrics report
Model file (.h5 or .pkl)
Requirements:
Experience in Deep Learning and Computer Vision
Strong understanding of CNN, SVM, and Transfer Learning
Proficiency in TensorFlow and Keras
Ability to handle large datasets
Dataset Details:
The dataset contains 87,000 images categorized into 38 different classes, such as Apple healthy, Apple scab, Apple black rot, Tomato botcera, etc.
The dataset is divided into a training and validation set in an 80:20 ratio.
Project Goal:
The objective is to develop a model that can:
1. Identify the correct class (out of 38) to which the leaf belongs.
2. Classify the leaf's health condition as one of the following:
Healthy
Initial stage of disease
Middle stage of disease
Severe damage
3. Analyze the percentage of leaf area affected for severity detection.
4. Provide key performance metrics such as:
Accuracy
Precision
Recall
F1 Score
5. Handle overfitting and underfitting issues explicitly and show how they impact model performance.
Technologies to Use:
Keras API
TensorFlow
Jupyter Notebook
Models to Implement:
1. SVM with HOG (Histogram of Oriented Gradients) for feature extraction
2. SVM with VGG16 (Pre-trained Model) for feature extraction
3. CNN with multiple convolutional layers to directly extract features
Workflow Pipeline:
1. Model Building
2. Training
3. Evaluation
4. Saving the Model
5. Testing and Performance Analysis (Accuracy, Precision, Recall, F1 Score)
Deliverables:
Complete code with proper documentation
Visualization graphs for accuracy and loss
Performance metrics report
Model file (.h5 or .pkl)
Requirements:
Experience in Deep Learning and Computer Vision
Strong understanding of CNN, SVM, and Transfer Learning
Proficiency in TensorFlow and Keras
Ability to handle large datasets