Solutions
AI Computer Vision
Solution for Smart
Agriculture
Revolutionizing the agrifood industry through
advanced automation and real-time image analysis
for Pre-Harvest and Post-Harvest.
Are you still relying on manual processes to manage your field operations?
Automation optimizes harvesting as new computer vision technologies allow for counting, sorting and quantifying fruit with unprecedented accuracy, helping to optimize the entire harvesting process.
How are you assessing if your crop is ready for harvesting?
Manual processes make it difficult to accurately assess crop readiness or production volumes.
- Low accuracy.
- Manual processes.
Are you optimizing your quality control processes with AI Computer Vision?
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.
Tupl AI Computer Vision Solution
What benefits can you
expect from the
AI Computer Vision Solution
for Smart Agriculture?
Less manual labour
0
%
Detection Accuracy
0
%
Consistency
>
0
%
Prediction latency
-
0
"
Discover how our solution can transform your agricultural business, offering control, flexibility and optimal management of your resources, from the field to the final consumer.
Pre-harvest
- 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.
Post-harvest
- Ensures quality control of production lines.
- Automates quality inspection with 100% detection accuracy.
- Reduces costs and improves the quality of the final product.
Technology advantages
- 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 a demo of the AI Computer Vision Solution for Smart Agriculture
Get started and request a demo to learn how the AI Computer Vision Solution can help you.
Frequently Asked Questions
Below you will find answers to the most common questions about AI Computer Vision Solution for Smart Agriculture.
How does the AI Computer Vision Solution for Smart Agriculture work?
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.
How does Tupl SaaS work?
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.
What are the applications of computer vision in agriculture?
Fruit counting, sorting, quantifying, quality issues detection.
How accurate are the ML models?
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%.
Are the machine learning models for computer vision in agriculture self-trained?
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.
Manual vs Automated Visual Inspection?
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.
Cloud VS On-Premise?
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.
How much data / how many images do I need to get started with the AI Computer Vision Solution for Smart Agriculture?
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.
Do I need an AI expert or developer on staff?
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.