FoliageWatch: Deep Learning-Driven Leaf Photo Analysis for Plant Disease Prediction

FoliageWatch is a sophisticated deep learning project that employs Convolutional Neural Networks (CNNs) to predict plant diseases by analyzing leaf photos. This repository contains the source code, data, and resources required to replicate and expand upon this innovative solution.

Table of Contents

• Introduction
• Features
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Dataset
• Model Training
• Evaluation
• Contributing
• License
• Acknowledgments

Introduction

FoliageWatch is a state-of-the-art plant disease prediction system that leverages deep learning techniques to assess the health of plants by analyzing images of their leaves. With the ability to identify various diseases early on, this project aims to empower farmers and gardeners to take proactive measures, reduce crop losses, and promote sustainable agriculture practices.

Features

• Utilizes Convolutional Neural Networks (CNNs) for image analysis.
• Predicts various plant diseases based on leaf photos.
• Easy-to-use interface for uploading and analyzing images.
• Scalable and adaptable for different plant species and diseases.
• Provides detailed disease diagnosis and recommendations for treatment.

Getting Started

Prerequisites

Before running the project, ensure you have the following prerequisites installed:

• Python
• TensorFlow
• NumPy
• OpenCV
• django

Installation

1. Clone this repository:

   git clone https://github.com/your-username/foliagewatch.git