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Infosys > Case Studies > IIC Precision Crop Management Testbed

IIC Precision Crop Management Testbed

Technology Category

Analytics & Modeling - Big Data Analytics
Networks & Connectivity - Cellular
Sensors - Camera / Video Systems
Sensors - Environmental Sensors

Applicable Industries

Agriculture

Applicable Functions

Process Manufacturing

Use Cases

Agriculture Disease & Pest Management

The Challenge

The global population is continuing to grow at a rapid pace placing increasing demands on the global food supply. In addition, impacts from climate change and a yearly reduction of available arable land will require the Agricultural sector to develop better ways of increasing crop yields and reducing costs. GOAL The goal for the Precision Crop Management Testbed is to create an environment where IoT solutions with the potential to impact world hunger can be developed.

The Solution

*This is an IIC testbed currently in progress.* LEAD MEMBERS Infosys SUPPORTING COMPANIES Sakata Seed America, Inc. MARKET SEGMENT Agriculture Technology (Agri-Tech) FEATURES • Integration of aerial imagery and multiple sensor technologies to provide a ‘360-degree’ view of the plant environment • Near real-time, 24/7 transmission of data via mesh / cellular network • Ability to analyze data, through the provision and analysis of high-volume sensor data TESTBED INTRODCUTION The Testbed will explore the ability of IoT technology to improve Crop Management, through increased production (yield), lower operational costs plus smarter applications of chemicals and fertilizers. The Testbed will focus on improving crop yield through the analysis of real-time data from a variety of environmental sensors and other sources of truth located in commercial crop fields or throughout the enterprise.

Data Collected

Soil Moisture Meters, Water Level, Weather, Wind Speed, Crop Conditions

Operational Impact

[Process Optimization - Real Time Monitoring]
Improved crop productivity (yield) through early abnormality detection and corrective actions capability and the identification of optimal (and sub-optimal) crop conditions with actionable insight
[Efficiency Improvement - Operation]
Improved operational efficiency through optimized aerial sampling and inspections

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