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# Tomato Plant Disease Detection using TinyML
# Tomato Plant Disease Prediction using TinyML

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@AKSHITHA-CHILUKA

AKSHITHA-CHILUKA Jun 11, 2024

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Machine Learning Repositories

Welcome to Machine Learning Repositories! 🚀 This repository is a curated list of awesome machine learning frameworks, libraries, and software, organized by programming language.

This repository is designed to be beginner-friendly, and new contributors will be given priority. However, please note that repeated issue creation for more scores will be considered a flag. Let's maintain a healthy and collaborative environment for learning and contributing!

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  1. Fork the repository to your GitHub account.
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## Description
This project develops a lightweight machine learning model techniques to detect tomato plant diseases from leaf images in real-time. By leveraging TensorFlow, the model runs directly on edge devices, providing immediate insights for agricultural decision-making. With a dataset of 16,011 images across 10 disease categories, the model undergoes rigorous preprocessing, augmentation, and optimization. Real-world testing validates its effectiveness, offering farmers a convenient tool for on-site disease diagnosis, potentially revolutionizing agricultural practices.
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Plant Disease Detection using CNN

Introduction

This project aims to develop a convolutional neural network (CNN) model to accurately detect and classify diseases in plant leaves from images, helping farmers manage crop health more effectively. The CNN model is trained on a dataset containing images of healthy and diseased plant leaves across various categories of diseases.

Dataset

The dataset used for training and testing the CNN model is the PlantVillage Dataset, which contains images of various plant diseases and healthy plant leaves. The dataset includes images of several plant species, such as tomatoes, potatoes, apples, grapes, and more.

Model Architecture

The CNN model architecture used for this project consists of multiple convolutional layers followed by max-pooling layers, dropout layers for regularization, and fully connected layers. The final layer uses the softmax activation function to classify the input image into different disease categories.

Technology Stack

  • Python
  • TensorFlow
  • Keras
  • NumPy
  • Matplotlib

Directory Structure

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