You will get AI Powered Crop Disease Detection System using YOLOv8 and Streamlit
Project details
Transform your agricultural monitoring with AgriGuard, a professional-grade computer vision solution designed to diagnose crop diseases instantly. This project offers a robust, English-version source code package, perfect for developers, researchers, or agricultural tech startups looking for a ready-to-deploy AI dashboard.
Key Features & Deliverables:
Triple Detection Modes: High-precision analysis for Images, Video files, and Real-time Camera feeds.
Expert AI Core: Powered by YOLOv8 for industry-leading speed and accuracy in object detection.
Localized Interface: Fully translated English UI with professional medical-grade disease labels and prevention advice.
Comprehensive Crop Support: Detects critical diseases in Apple, Grape, Potato, Tomato, Corn, Pepper, and Strawberry.
Developer-Ready: Clean Python code with detailed docstrings, pinned requirements, and a professional HTML setup guide.
Technical Stack: Python 3.8+, Ultralytics YOLOv8, Streamlit, PyTorch (CUDA support), OpenCV.
Whether you need a foundation for a commercial product or a high-end academic tool, AgriGuard provides the stability and performance required for modern AI applications.
Key Features & Deliverables:
Triple Detection Modes: High-precision analysis for Images, Video files, and Real-time Camera feeds.
Expert AI Core: Powered by YOLOv8 for industry-leading speed and accuracy in object detection.
Localized Interface: Fully translated English UI with professional medical-grade disease labels and prevention advice.
Comprehensive Crop Support: Detects critical diseases in Apple, Grape, Potato, Tomato, Corn, Pepper, and Strawberry.
Developer-Ready: Clean Python code with detailed docstrings, pinned requirements, and a professional HTML setup guide.
Technical Stack: Python 3.8+, Ultralytics YOLOv8, Streamlit, PyTorch (CUDA support), OpenCV.
Whether you need a foundation for a commercial product or a high-end academic tool, AgriGuard provides the stability and performance required for modern AI applications.
AI Development Type
Deep Learning, Knowledge Representation, Model TuningAI Tools
OpenCV, PyTorchAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$99
|
Standard
$199
|
Advanced
$499
|
|---|---|---|---|
| Delivery Time | 1 day | 3 days | 5 days |
Number of Revisions | 3 | 9 | 15 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | - | - | |
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
About Cheng
AI Engineer | Computer Vision & Streamlit Web Applications
Singapore, Singapore - 4:46 am local time
Unlike developers who only deliver scripts, I provide ready-to-use business solutions. I have a proven track record of publishing professional AI software on international marketplaces (like Codester), covering everything from custom dataset curation to internationalized UI deployment.
What I can do for you:
Custom Object Detection: Training YOLOv8/v10 models for specific use cases (Agriculture, Retail, Medical, etc.).
Interactive Web Dashboards: Building professional AI interfaces with Streamlit for real-time inference and data visualization.
End-to-End Deployment: From raw image labeling to cloud-hosted production environments.
Legacy Code Optimization: Converting your local Python scripts into polished, user-friendly web apps.
Technical Stack:
Models: YOLOv8, YOLOv10,Resent,PyTorch, OpenCV and more
Frontend/Deployment: Streamlit, Streamlit Cloud, GitHub Actions.
Languages: Python (Advanced), SQL.
I am passionate about turning complex AI research into practical tools that solve real-world problems. Let’s discuss how I can bring your AI vision to life!
Steps for completing your project
After purchasing the project, send requirements so Cheng can start the project.
Delivery time starts when Cheng receives requirements from you.
Cheng works on your project following the steps below.
Revisions may occur after the delivery date.
Source Code Delivery and Initial Setup
I will provide the full English-version source code, including pre-trained YOLOv8 models, the Streamlit web app, and the necessary requirements.txt file for environment setup.
Environment Configuration Support
I will provide a professional HTML installation guide to help you configure the Python environment and resolve common issues during the initial deployment.