Manual processes make it difficult to accurately assess crop readiness or production volumes.
Low accuracy.
Manual processes.
The average accuracy of manual inspection is under 85%, but creating ML models for Computer vision can be a very complex problem that requires a high level of expertise.
Low consistency.
Poor quality control.
Difficult to create new models or combine with others.
90%
Less manual labour
99%
Detection Accuracy
>90%
Consistency
<1"
Prediction latency
Analyzes images and videos to assess fruit ripeness by detecting color, measuring size and quantifying production volume.
Allows counting, sorting and quantifying fruit with unprecedented accuracy.
Facilitates planning and decision making, ensuring that harvesting takes place at the optimal time.
Ensures quality control of production lines.
Automates quality inspection with 100% detection accuracy.
Reduces costs and improves the quality of the final product.
Compatible with any type of vision hardware
Ability to retrain existing models and create new custom models to suit specific use cases.
Control your own data: the no-code approach provides the independence to create and adjust vision models without the need for programming knowledge
Get started and request a demo to learn how the AI Computer Vision Solution can help you.
Below you will find answers to the most common questions about AI Computer Vision Solution for Smart Agriculture.
This Computer vision in agriculture solution uses cutting edge deep learning-based technologies to detect and classify fruits or any other crops, with processing times up to 6s, enabling real time decision making. The solution allows for model performance analysis and retraining.
AI Computer Vision Solution for Smart Agriculture SaaS is delivered in cloud service (e.g. AWS, Azure, etc.) and can also be deployed on-premises, in your private cloud, or data center.
Get started with a functional solution in operation within 2-3 weeks. Monthly subscription. No strings attached. Stop at any time.
Fruit counting, sorting, quantifying, quality issues detection.
The more images are used for the model training - the higher the algorithm's accuracy. Our algorithms have reached 95% of detection accuracy, while the average accuracy of manual inspection is under 85%.
ML Performance Drift Detection and Correction with Active Learning features a mechanism to identify drift in a machine learning model over time and subsequently correct it by retraining, with the help of an active learning module. The machine learning models are self-trained and >90% consistent.
Automated visual inspection provides significant, long-lasting benefits to manufacturers. AI Quality Control Toolkit reduces 90% of manual labor; therefore, it decreases the overall cost of production and essentially increases the revenue. The business case for implementing vision systems is recognized on a return on investment (ROI) basis. Contact us now, and we can go through calculations based on your data.
In the digital manufacturing environment, introducing AI into the manufacturing process has become possible due to the cloud computing capabilities of Industry 4.0. Modern manufacturers leverage this development to marry computer vision hardware along the production line with AI-powered cloud-based digital tools.
An inspection line can be set up with just tens of images to build the AI model. The more data is used as input, the more accurate the model will be, and the deep learning models will learn from any additional data, improving accuracy over time.
No-code solutions for AI application development help manufacturers take advantage of this emerging technology without the need to hire technical specialists or investing significant time and capital.
Tupl's software has a simple and intuitive user interface that enables existing personnel to build Vision AI applications for quality assurance in very little time with relatively small datasets and with no programming required.
Get started and request a demo to learn how the AI Computer Vision Solution can help you.
