Tomato Leaf Disease Classification

Prediction Models/Tomato_Leaf_Disease/Readme.md

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+# Tomato Leaf Disease Classification
+
+## Project Description
+This project focuses on classifying tomato leaf diseases using a Convolutional Neural Network (CNN) model. The goal is to accurately detect and classify common diseases affecting tomato plants, such as bacterial spot, early blight, late blight, leaf mold, and more. Leveraging a deep learning approach, this project achieves a classification accuracy of approximately 94%, making it a valuable tool for precision agriculture and plant disease management.
+
+### Technology Stack
+- **Python**: For data processing, training, and testing the model.
+- **TensorFlow/Keras**: Used for building and training the CNN model.
+- **OpenCV**: For image processing tasks, if applicable.
+- **NumPy, Pandas**: For data manipulation and handling.
+- **Matplotlib/Seaborn**: For data visualization.
+
+## Problem Statement
+Tomato plants are highly susceptible to a variety of diseases that can affect both yield and quality. Traditional methods of diagnosing plant diseases are often time-consuming, prone to error, and reliant on expert knowledge. An automated approach to identifying diseases can improve diagnostic accuracy and enable timely intervention, thereby reducing crop losses and increasing agricultural productivity. This project addresses the challenge by implementing a deep learning-based model capable of classifying multiple tomato leaf diseases with high accuracy.
+
+## Dataset
+The dataset used consists of labeled images of tomato leaves, with each image categorized by the type of disease present. The dataset is split into training, validation, and test sets to evaluate model performance effectively.
+
+