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1. Introduction

2. Tomato Leaves Disease Prediction: In today's world, agriculture plays a critical role in food production. Tomato crops are widely grown, but they are susceptible to diseases that can significantly impact yields. Detecting and addressing these diseases in a timely manner is crucial for sustainable agriculture and food security.

3. Problem Statement The problem we're addressing is the early detection of diseases in tomato leaves, which can lead to reduced crop quality and economic losses for farmers Undetected diseases can spread rapidly, requiring more pesticides and potentially affecting the overall crop yield. This not only impacts farmers' incomes but also the availability of tomatoes in the market, affecting food prices and accessibility."

4. Project Scope Our project focuses on detecting two common diseases in tomato leaves, 'Early Blight' and 'Late Blight.' However, we acknowledge that there are other tomato diseases not covered by our application." Our primary focus is on 'Early Blight' and 'Late Blight' because these diseases are prevalent and have a significant impact on tomato crops in our region

5. Project Type (Applied/Research) Our project is primarily applied, as our main goal is to provide a practical solution to a real-world problem faced by farmers in the field. Our objectives include developing a user-friendly mobile application that can accurately identify tomato leaf diseases using image analysis techniques. We aim to provide a tool that empowers farmers to make informed decisions about disease management."

6. Functional Requirements Image capture: The app allows users to take pictures of tomato leaves in the field.""Disease detection algorithms: It employs machine learning algorithms to analyze leaf images and identify diseases.""Real-time feedback: Users receive instant disease diagnosis results." The image capture function enables farmers to quickly capture leaf images directly from their mobile devices, facilitating rapid disease diagnosis. The disease detection algorithms analyze these images to accurately identify the presence and severity of diseases."

7. Tools/Domain Programming Languages: Python for machine learning and image processing.""Libraries: OpenCV for image analysis, TensorFlow for machine learning models." We selected Python and OpenCV for their robust image processing capabilities, which are essential for accurate disease detection. TensorFlow was chosen due to its efficiency in training deep learning models for image classification, making it suitable for our domain."

8. Methodology diagram Use a flowchart, diagram, or schematic to illustrate the step-by-step process involved in your disease detection system. For example, you can create a flowchart that shows the following steps:Image Capture -> Preprocessing -> Feature Extraction -> Disease Classification -> Result PresentationShow the flow of data and processesClearly depict how data flows from image capture through preprocessing, feature extraction, and finally to disease classification. For example, arrows and boxes can represent the flow of data between these steps. Explain each step's significance in the context of disease detection.

9. Business model While our primary focus is on solving the problem of tomato leaf disease detection, there are potential business opportunities associated with our project.