Increase crop yields, product quality, and resource efficiency – reliably detect crops, weeds, ripeness levels, and plant diseases directly in the field. Freely programmable or available as a ready-to-use solution.
Machine Vision for Smart Farming
Typical Challenges in Smart Farming
01
Reliable Plant Detection
Crops, fruits, and weeds must be accurately distinguished despite natural variations.
02
Managing Changing Environmental Conditions
Sunlight, shadows, dust, and weather conditions make reliable image acquisition challenging in outdoor environments.
03
Efficient Monitoring of Large Areas
Fields and crops must be captured and analyzed quickly to enable informed decisions.
04
Increasing Yields and Resource Efficiency
Diseases, signs of plant stress, and weeds must be detected early to protect crop yields and optimize resource use.
Our Approaches
Two Paths to the Right Vision Solution
For OEMs (agricultural machinery and equipment manufacturers) that require flexible machine vision solutions for smart farming applications.
Open Plattform
Flexible Vision Platform
Depending on your integration requirements and project timeline, IMAGO offers two distinct approaches to food inspection.
- Freely programmable with the IMAGO ViewIT software framework, C++, Python, and HALCON – no black box, full control
- Multiple vision tasks on a single device – including crop and weed detection, ripeness assessment, and plant health monitoring.
- Portable across all IMAGO devices, with OPC UA and REST connectivity for Industry 4.0 integration
- Long-term availability, including ODM (Original Design Manufacturer) variants for serial machine production
Ready to use
Preconfigured Solution
For users with a specific inspection challenge who need fast and reliable results without extensive in-house development.
- Deep-learning inspection of visually variable food products without programming using a web-based GUI
- Fast deployment with minimal lead time and a clear focus on rapid time-to-market
- Reliably detects anomalies and natural variations in shape, color, and surface appearance.
- Consulting and support from the experienced IMAGO engineering team – robust hardware made in Germany
Decision Guide
What Is Your Goal?
Choose the approach that best fits your project – both lead to a reliable inspection solution.
Maximum Flexibility
You develop your own smart farming applications or integrate machine vision deeply into agricultural machinery, autonomous robots, or precision farming systems.
Fast Implementation
You have a specific inspection task – such as detecting damaged fruits, plant anomalies, or quality defects – and need a ready-to-use system quickly.
Differentiation
Why IMAGO for Smart Farming?
Hardware, software, and system integration work together to enable reliable crop detection, AI-powered analysis, and real-time processing – even under challenging environmental conditions.
Open Software Framework
Freely programmable instead of a black box – full control over every inspection task on the line.
Multiple Tasks, One System
OCR, code reading, quality control, and AI inspection run on the same hardware instead of multiple systems.
Long-Term Availability
ODM variants and long product life cycles for serial machines and global rollouts.
Future-Proof Architecture
Scalable platform without vendor lock-in that grows with your requirements.
High-Speed-Vision
Hundreds of products per minute with processing power directly in the camera or box PC.
Applications
Typical Applications in Smart Farming
IMAGO vision systems are used in these four application areas of smart farming. This is just a small selection of what machine vision enables in modern agriculture.
Crop and Weed Detection
Crops and weeds are reliably distinguished, enabling targeted weed control and reducing resource consumption.
Harvest and Ripeness Detection
Fruits, vegetables, and other agricultural products are evaluated based on ripeness, size, and quality to determine the optimal harvest time.
Plant Health and Condition Monitoring
Diseases, pest infestations, and signs of plant stress are detected early, allowing timely intervention and reducing crop losses.
Quality Inspection and Sorting
Agricultural products are automatically inspected and sorted based on size, color, and quality to detect damage and foreign objects.
Technology for Demanding Smart Farming Applications
Changing lighting conditions, different plant growth stages, and natural variations place high demands on machine vision systems in the field. Combining traditional image processing with AI enables reliable detection of crops, fruits, weeds, and plant diseases, even under changing environmental conditions.
Plant detection, weed classification, ripeness assessment, and plant condition analysis run in parallel within a single system. This allows multiple tasks to be automated simultaneously without integrating additional hardware or separate vision systems.
Thanks to the open software framework, new crops, cultivation methods, and algorithms can be implemented flexibly. The scalable platform adapts to future requirements and enables the integration of new AI models, sensors, and applications.
Product recommendations
Recommended Solutions for Smart Farming
The following systems are designed to meet the requirements of modern smart farming applications. They combine compact design, AI-powered processing, and flexible integration. Depending on the application, they can be freely programmed or deployed as ready-to-use solutions.
Reliably detect crops, weeds, ripeness levels, and plant diseases directly in the field.
FAQs about Smart Farming
How reliably can the system detect crops, fruits, weeds, or foreign objects under changing environmental conditions?
Agricultural applications must operate reliably in sunlight, shadows, dust, rain, and across different growth stages. AI-powered machine vision detects plants and objects even under highly variable conditions, enabling stable processes in fields and greenhouses.
How can machines and robots adapt to varying plants, sizes, and positions?
Plants do not grow in fixed positions and vary in shape, size, and ripeness. Machine vision detects these variations in real time and provides the information needed for harvesting, sorting, and crop care processes. This makes flexible and autonomous agricultural machinery possible.
Is the platform robust and scalable enough for future automation and AI applications?
Smart farming machines often remain in operation for many years while taking on increasingly complex tasks. An open vision platform enables the integration of new cameras, sensors, and AI models through software. This allows manufacturers to continuously enhance their machines without replacing the entire hardware platform.
