Objective: Classify plant diseases using the PlantVillage dataset. Model Architecture: Input layer Flatten layer Three dense layers with ReLU activation Output layer with softmax activation Techniques: Data augmentation Adam and SGD optimizers Dropout regularization Tools and Technologies: TensorFlow Keras Outcome: Achieved high accuracy in disease classification.
This project focuses on the development of a deep neural network (DNN) for the classification of plant diseases using the PlantVillage dataset. The dataset contains over 54,000 images of healthy and unhealthy leaves from various plant species. The goal is to build a model that can accurately identify the type of disease affecting a plant based on an image of its leaf.
Flattening: In deep learning, flattening refers to converting a multi-dimensional array (e.g., an image) into a one-dimensional array. This step is essential before feeding the data into the fully connected (dense) layers of a neural network.
Layers:
Input Layer: The initial layer that receives the input data. Hidden Layers: Intermediate layers where computations are performed. These can include various types such as dense layers, convolutional layers, etc. Output Layer: The final layer that produces the output predictions. Dense Layer: Also known as a fully connected layer, it is where each neuron receives input from all neurons in the previous layer. This layer is crucial for learning complex patterns in the data.
Regularization: A technique used to prevent overfitting by adding a penalty to the loss function. Common types include L1, L2 regularization, and dropout.
Adam Optimizer: An optimization algorithm that combines the advantages of two other extensions of stochastic gradient descent. Adam is known for its efficiency and effectiveness in training deep learning models.
SGD (Stochastic Gradient Descent): An optimization method that updates the model parameters incrementally, which can lead to faster convergence compared to batch gradient descent.
Dropout: A regularization method where a fraction of neurons is randomly ignored during training, which helps prevent overfitting.
Data Acquisition and Preprocessing
Dataset: PlantVillage dataset, which includes 54,303 images categorized into 38 classes representing various plant diseases. Preprocessing: Images were resized, normalized, and augmented to enhance the model's ability to generalize.
Layers: Rescaling Layer: Normalizes pixel values to the range [0, 1]. Flatten Layer: Converts 2D images to 1D vectors. Dense Layers: Three dense layers with ReLU activations for hidden layers and softmax activation for the output layer. Units: The model includes 128 units in the first dense layer, 64 units in the second, and 38 units in the output layer corresponding to the number of classes. Training Configuration
Loss Function: Sparse Categorical Crossentropy. Metrics: Accuracy. Optimizers: Adam optimizer was used for initial training, followed by SGD for further refinement. Regularization: Dropout was incorporated to reduce overfitting.
The model was trained over 20 epochs, achieving significant improvements in accuracy and reductions in loss. Here are the key results:
Training Accuracy: Improved steadily, indicating the model's ability to learn from the training data. Validation Accuracy: Consistently improved, showing the model's ability to generalize to unseen data. Conclusion: The deep neural network successfully classified plant diseases with a high degree of accuracy. The choice of the Adam optimizer facilitated efficient training, while dropout regularization helped mitigate overfitting. This project demonstrates the effectiveness of deep learning techniques in tackling complex image classification tasks in the agricultural domain.