Plant Disease Prediction using Deep Learning CNN with ADAM Optimizer

The model begins with data preparation, where 87,000 RGB images of healthy and diseased plant leaves are collected from a Kaggle dataset and categorized into 38 classes. The dataset is divided into training, validation, and test sets to ensure reliable model evaluation. Image preprocessing, including resizing to 128x128 pixels and augmentation techniques like rotation and flipping, enhances dataset diversity and helps the model generalize better. A Convolutional Neural Network (CNN) serves as the backbone of the model, extracting critical features through convolution, ReLU activation functions and max-pooling layers to enhance feature extraction and reduce dimensionality. Following this, fully connected layers process the extracted features, while a softmax output layer assigns probabilities to classify the images into specific disease categories. The model is trained using TensorFlow and Keras frameworks, employing techniques like categorical cross-entropy, Adam optimizer, early stopping, and model checkpointing to refine performance iteratively. The output is presented through a Streamlit web application, enabling users to upload leaf images for instant disease identification. The application provides detailed predictions, including the name of the disease, confidence scores, and actionable recommendations for treatment. Additionally, GTTS generates audio descriptions, while Translation APIs offer the results in multiple languages, ensuring a comprehensive and accessible user experience.