Sold by: Persistent SystemsÂ
Overview
Usually, significant amount of time spent by the farmers to check the health status of plants and fruits. Inspection procedure can be automated with the help machine learning model that processes crop images and classifies as good or defective. This model is trained on images of tomatoes and plants and classifies them as healthy or unhealthy.
Highlights
- Multi-class classifier that classifies tomato or leaf image as healthy or unhealthy
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $0.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $0.00 |
Vendor refund policy
Since you are not being charged currently for the use of this software there will be no refund of any charges.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
First version released to AWS ML Marketplace
Additional details
Inputs
- Summary
Download the Jupyter notebook in "Additional Resources" section and follow readme.txt provided.
- Input MIME type
- jpeg, png
See Input Summary
See Input Summary
Resources
Vendor resources
Support
AWS infrastructure support
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Digital satellite-based one-stop precision agriculture platform for field monitoring
EOSDA Crop Monitoring - digital satellite-based one-stop precision agriculture platform for field monitoring. EOSDA team of data scientists and engineers has developed effective techniques for crop yield estimation using remote sensing and machine learning models. We`re relying on earth observation data retrieved from satellites to cover areas ranging from individual farms to regions.
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