Client: Universidad de Colombia
Location: Colombia
Monitoring agricultural conditions in greenhouse crops using Edge Computing and IoT
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We develop monitoring technology with IoT and Edge Computing for better management of agricultural resources
Our technology integrates machine learning algorithms with expert systems, allowing for deep and detailed analysis of multiple variables and scenarios.
In the context of modern agriculture, resource optimization and maximizing crop yield are crucial challenges. Our project addresses these challenges by developing an advanced monitoring system using Internet of Things (IoT) and Edge Computing technologies.
The developed IoT and Edge Computing monitoring system provides a powerful tool for efficient management of agricultural resources. The integration of advanced technologies such as machine learning and expert systems enables in-depth analysis and informed decision making, significantly improving crop outcomes and contributing to agricultural sustainability.
Eco-efficiency
The purpose of this project is to provide a powerful tool for real-time data collection and analysis, allowing for accurate management of greenhouse crops.
Initial results are more than promising: our system provides a complete overview of the status of the parameters, facilitating more informed and efficient decision-making.
This project is a testament to our commitment to technological innovation and its practical application for the benefit of the agricultural community and the agro-industrial sector. We look forward to continuing to collaborate on innovative projects that have a positive impact on our world.
IoT Sensors
Sensor Types: Soil moisture, temperature, light, pH, nutrient levels.
Functions: Collect real-time data on environmental and soil conditions.
Edge Computing
Local Processing: Collected data is processed locally on Edge nodes to reduce latency and network load.
Machine Learning Algorithms: Application of machine learning models for preprocessing and initial analysis.
Central Server
Storage and Processing: Data processed locally is sent to a central server for further analysis.
Expert Systems: Integration with expert systems to provide specific recommendations and alerts.
User Interface
Remote Access: Users can access the system and its data through a web or mobile interface.
Alerts and Notifications: Users receive real-time alerts and notifications about critical conditions.
Components
of the system
Workflow
Data collection
IoT sensors installed in fields collect data on environmental and soil conditions.
Processing at the Edge
The collected data is processed on nearby Edge devices for a fast and efficient response.
Sending to central server
The preprocessed data is sent to the central server where further analysis is performed.
Analysis and decision making
Machine learning algorithms and expert systems analyze data and generate recommendations.
User interaction
Users access processed information and receive alerts, enabling proactive crop management.
Benefits
Being able to connect
your crop to the network
Information in
real time
Better efficiency
energetic
The developed IoT and Edge Computing monitoring system provides a powerful tool for efficient management of agricultural resources. The integration of advanced technologies such as machine learning and expert systems enables in-depth analysis and informed decision making, significantly improving crop outcomes and contributing to agricultural sustainability.