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AI Computer Vision for
Smart AgriFood

Optimize growth, planning, and productivity

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AI Computer Vision brings visual intelligence to the heart of AgriFood operations.

It monitors crop development, optimizes harvesting, detects defects in real time, and ensures traceability and flawless presentation in every package, helping agri-food companies make smarter decisions based on precise visual data.

By seamlessly integrating with ERP platforms, sensors, satellite imagery, and even advisory systems via WhatsApp, our solution simplifies harvest planning, resource management, and regulatory reporting.

AgriFood Pain Points:

Lack of Real-Time Crop Insight

Inefficient Use of Resources
(Water, Labor, and Materials)

Disconnected Systems
and Manual Reporting

Poor Planning and Limited Predictability

Human Error and Inconsistent Inspection

Defect Detection Accuracy

> 0 %
> 0 %

Reduction in Quality Control Labor Costs

0 %

Real-Time Visibility and Alerts

What benefits can you expect?

Optimizing AgriFood Quality with Computer Vision

In the AgriFood industry, quality, freshness, and safety are non-negotiable. Our AI Computer Vision solution automates visual inspection across the value chain, ensuring consistent quality, operational efficiency, and sustainable outcomes.
Streamlining Visual Tasks with Automation

Automating Quality Control in the Field and Factory

AI Computer Vision removes the need for manual inspection of fresh produce, meat, or packaged goods by automating visual checks like shape, color, size, and surface quality. It reduces reliance on skilled labor, improves throughput, and ensures consistent product grading — even under varying lighting or positioning conditions.
Packaging Validation with Precision

Compliance Secured Through Accurate Labeling

Ensure food safety and traceability with precise, automated packaging inspection. Our system validates barcodes, expiration dates, lot numbers, and label presence or alignment — preventing labeling errors that could lead to product recalls or non-compliance with food safety standards.

Cutting Food Waste with Real-Time Defect Detection

By detecting non-compliant or defective items early in the process, our solution helps minimize product waste and rework. This reduces the environmental impact of AgriFood production while increasing yield and line efficiency — all in support of your sustainability goals.

Computer Vision Use Cases

Tupl’s AI Computer Vision automates quality checks, reduces inspection time, and ensures consistent product standards with no-code AI.

Preserved food manufacturer

Real-time defect detection in high-speed canning lines

Real-time defect detection in high-speed canning lines

The solution is a machine vision system for real-time detection of marking and filling defects across various types of containers, without interrupting production. It triggers automatic rejection within milliseconds and includes a touchscreen interface for model selection, alerts, and defect threshold configuration, fully integrated with existing factory systems.

Challenge

  • Detection and rejection of defects in under 0.5 seconds per unit, ensuring real-time execution synchronized with actuators and conveyor belt speed.
  • Integration with a PLC, under strict electrical and I/O constraints, while interfacing with sensors, alarms, and ERP systems.
  • Training of accurate models (>90% accuracy) using limited, high-quality datasets across various defect types and container formats.
  • Configuration of custom alarm logic allowing operators to set alarm thresholds, for example, triggering alerts after a defined number of consecutive defects.

Benefits

  • Reduces operator workload related to inspection by 80%, minimizing manual intervention.
  • Meets the accuracy threshold above 90%.
  • Prevents quality issues from escalating through real-time alarms triggered by consecutive anomalies.
  • Reduces false rejects and under-detected defects by 40%, improving product yield and consistency.
  • Seamlessly adapts to different container types and formats, enabling scalability across production lines.
AgriFood Producer

Automation of Fruit Counting and Classification

The solution is a computer vision system based on mobile devices that automatically detects, counts, and classifies fruits according to their ripeness level using images taken with a smartphone. The solution enables reliable weekly yield estimates, facilitates real-time harvest planning, and reduces reliance on manual processes, offering scalable and highly accurate agricultural forecasts.

Challenge

  • Automatic Detection and Classification of Fruits of Different Sizes, Colors, and Visibility Across Various Plant Types and Growth Stages
  • Accurately count fruits from images without standardized backgrounds or fixed positions.
  • Assign fruit counts to specific field zones and aggregate the results for weekly yield forecasts.
  • Development of a mobile application to ensure a consistent image flow from on-field capture to cloud storage, enabling subsequent AI-based detection.

Benefits

  • Forecast accuracy improves from ~75% to ≥95%, cutting estimation error in half.
  • Field coverage efficiency increases fourfold (from 10 to 40 hectares per worker per day).
  • Reduces manual workload and operator fatigue.
  • Generates actionable outputs such as maturity and harvest estimation tables.
  • Seamlessly integrates with SAP and agronomic platforms via API.

AI Computer Vision Solution for Smart AgriFood

Frequently Asked Questions

AI-powered computer vision can analyze all agricultural and production chain stages, from crop monitoring (germination, flowering, fruit development, and ripening) to anomaly detection (pests, diseases, water or nutrient stress). It also optimizes harvesting (selecting optimal timing and automated sorting), post-harvest processing (washing, cutting, canning, and packaging with quality control), and logistics (storage, transportation, and tracking until delivery to the customer), enhancing efficiency and reducing losses across the entire agri-food supply chain.

No. The solution is fully software-based and works with standard camera feeds or images captured via mobile, drone, or installed field cameras.

Yes. It’s built to integrate easily with agricultural ERPs and data platforms, allowing for automated synchronization of work orders, production records, and harvest planning.

The system delivers >90% precision in identifying crop stages and anomalies, ensuring data-driven decisions and efficient resource use.

They can receive alerts and submit updates through WhatsApp or a mobile app, ensuring real-time communication without requiring new tools or technical skills.

From Farm to Factory: One Vision, Many Applications

Explore how our AI Computer Vision delivers real-time insights across industries—from agriculture to manufacturing.
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