You will get Potato Disease Detection Model
Project details
My project develops an accurate disease detection model for potatoes. It achieves 98% accuracy, surpassing conventional methods. The solution is customizable, tailored to specific disease patterns and customer requirements. It offers comprehensive disease management features, including customizable thresholds, detailed reports, analytics, and integration with farm management systems. Ongoing support and collaboration ensure sustained performance and customer satisfaction. I aim to empower farmers with optimal disease control and crop health.
Machine Learning Tools
Keras, Python, Python Scikit-Learn, scikit-learnWhat's included $3,500
These options are included with the project scope.
$3,500
- Delivery Time 5 days
- Number of Revisions Unlimited
- Number of Model Variations 1
- Number of Scenarios 2
- Number of Graphs/Charts 1
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Fast 2 Days Delivery
+$3,700
Additional Model Variation
(+ 7 Days)
+$4,000
Additional Scenario
(+ 3 Days)
+$4,300
Additional Graph/Chart
(+ 3 Days)
+$4,550Frequently asked questions
About Moaaz
Data Analysis ,Data Sciense & Visualization | API, API Development
Kigali, Rwanda - 6:30 am local time
Steps for completing your project
After purchasing the project, send requirements so Moaaz can start the project.
Delivery time starts when Moaaz receives requirements from you.
Moaaz works on your project following the steps below.
Revisions may occur after the delivery date.
Project Initiation
The client purchases the project and sends their specific requirements, objectives, and timeline. Regularly updated, you can ensure transparency, maintain a clear record of progress, and provide your client with visibility into the project's status.
Model Evaluation and Refinement
Evaluate the trained model using the validation dataset. Fine-tune the model by adjusting parameters and conducting iterative experiments. Optimize the model for accuracy, precision, recall, and other relevant metrics.