About project
An AI platform that offers surveyors automated quality control and fruit and vegetable categorization.
Facts
A team of 6 members, consisting of 3 software developers
80.000$ budget
Revolutionary technology applied
Customer
under NDA
Our customer is a former top manager of one of the largest Russian retailers and leads a world-class surveyor and logistics professionals.
Goal
- Accuracy of 99%
- Defects Detection
- Product Quality Detection
- Quality Category Detection
- Databases conducted in the accordance with the UNECE and OECD standards
Challenge
-
Our software streamlines the dataset and feature management, training pipeline for Artificial Neural Networks (ANNs), and deployment process.
- Our neural network algorithms efficiently evaluate the freshness of fruits and vegetables.
- Our software solution covers all essential surveyor processes, including product analysis, shipment analysis, and statistical analysis.
- Our examination result databases are extensive and meet the standards set by the United Nations Economic Commission for Europe (UNECE) and the Organization for Economic Cooperation and Development (OECD).
Results
1
A scanner for obtaining high-quality photographs has been developed
2
A unit of trade professionals has been formed to assess the quality of fruits and vegetables
3
Data collection for machine learning is ongoing
4
Developed software to assess the quality of fruits and vegetables
5
The automated process of forming reports has been developed
6
The virtual personal Cabinet of the expert-receiver has been developed
Presentation
Technologies
Basic technologies
1
C#, Microsoft.NET
Python
Python
Artificial intelligence
2
Convolutional neural networks, ResNET
RetinaNET
RetinaNET
Platforms
Web applications
Desktop applications
Mobile applications
Server applications
Screenshots
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