Leaf Disease Detection Using Deep Learning
Detecting diseases in plants early is crucial for ensuring healthy crops and maximizing agricultural yields. Our project, "Leaf Disease Detection Using DeepLearning," focuses on harnessing the power of Deeplearning to identify and classify diseases in plant leaves from images. By utilizing a robust dataset of leaf images with various diseases, we train a Deeplearning model to accurately diagnose the condition of the plants. This project aims to assist farmers and agriculturists in timely detection and treatment of plant diseases, thereby promoting healthier crops and reducing losses.
Our approach involves using convolutional neural networks (CNNs), which are highly effective in image recognition tasks. We preprocess the images to enhance quality and apply data augmentation techniques to improve the model's generalization. The resulting model is capable of detecting multiple diseases with high accuracy, providing a valuable tool for maintaining crop health. This project exemplifies the significant impact that Deeplearning can have on agriculture, offering a practical solution to a common problem.
Explanation All Code
Step 1:
Import and install the necessary packages.
Mounting Google Drive
We mount Google Drive to access our dataset stored in the cloud.
Importing Libraries
We import essential libraries for data processing, model building, evaluation, and visualization.
Data collection and preparation:
We utilized the Glaucoma Dataset for DeepLearning Classification. We collected a Glaucoma dataset consisting of 10400 images.Then, divide the dataset into 80% for training and 20% for validation.
Load Datasets
We set the paths for training and validation datasets stored in Google Drive.
Listing Categories
We list the categories (labels) in the training dataset directory.
Step 2:
Data Processing
We define a function to read and resize images, storing them with their respective labels. We also count the number of images per class.
Processing Training Data
We process the training data and print the total number of training samples.
Plotting Class Distributions
We plot the distribution of classes in the training dataset.
Processing Validation Data
We process the validation data and print the total number of validation samples.
Plotting Validation Data Distribution
We plot the distribution of classes in the validation dataset.
Step 3:
Preparing Data for Model
We convert image data and labels to numpy arrays and normalize the images.
Visualizing Random Training Images
We display random images from the training dataset for visualization.
Step 4:
Leaf Disease Detection Using vgg16 custom model, modified vgg19 model and efficientnet_b4 Model.
- VGG16 Custom Model: This model is based on the original VGG16 but customized to better suit the specific requirements of leaf disease detection. Its 16-layer architecture helps in accurately identifying disease patterns in leaf images.
- Modified VGG19 Model: This model is an enhanced version of the VGG19, modified to improve its performance in detecting leaf diseases. The additional layers provide more depth and precision in image analysis.
- EfficientNet_B4 Model: Known for its balance of efficiency and accuracy, this state-of-the-art model is designed to perform well with fewer resources while maintaining high accuracy. It is particularly effective for complex image classification tasks like identifying leaf diseases.
By using these models, my project aims to accurately detect diseases in leaves from images, leveraging the strengths of each model to improve overall performance.
Building a VGG16 Model
We define and compile a VGG16 Custom Model.
Training the VGG16 Custom Model.
We train the model and save the best weights.
Plotting Training History
We plot the accuracy and loss curves of the model.
Evaluating Model Performance
Evaluating the Model
Plotting Confusion Matrix
We plot the confusion matrix and print the classification report.
Step 5:
Building a Modified vgg19 Model
We define and compile a modified vgg19 model.
Training the modified vgg19 model
We train the model and save the best weights.
Plotting Training History
We plot the accuracy and loss curves of the model.
Evaluating Model Performance
Evaluating the Model
We load the best model and evaluate it on the test data. There is the accuracy of 92.31%.
Plotting Confusion Matrix
Classification Report
We plot the confusion matrix and print the classification report.
Step 6:
Building a efficientnet_b4 Model
We define and compile a efficientnet_b4 model.
Training the efficientnet_b4 model
We train the model and save the best weights.
Plotting Training History
We plot the accuracy and loss curves of the model.
Evaluating Model Performance
Evaluating the Model
We load the best model and evaluate it on the test data. There is the accuracy of 99.42%.
Save the model
Plotting Confusion Matrix
Classification Report
We plot the confusion matrix and print the classification report.
Step 7:
Prediction
Conclusion
The "Leaf Disease Detection Using DeepLearning" project successfully demonstrates the application of advanced technology to solve agricultural challenges. Our model can accurately identify various diseases in plant leaves, providing a crucial tool for farmers and agriculturists to ensure crop health. By enabling early detection and diagnosis, this technology helps in taking timely measures to treat affected plants, ultimately leading to better crop yields and reduced losses.
Through rigorous testing and validation, our model has proven to be reliable and effective in real-world scenarios. The success of this project highlights the potential of integrating Deeplearning into agricultural practices, offering a scalable and efficient solution to disease management. Moving forward, we aim to further enhance the model's accuracy, expand its capabilities to detect more diseases, and collaborate with agricultural experts to bring this technology to the field. This project represents a significant advancement in agricultural technology, promoting sustainable farming and food security.