Transforming farming with computer vision and Edge computing
Innovative solutions for a food-secure world: The role of computer vision and Edge computing in agriculture
The agriculture industry is poised for a breakthrough thanks to the integration of computer vision, Edge computing, and AI integration.
These cutting-edge technologies have the power to unlock a world of possibilities and unleash solutions that can rise to meet the challenges of the modern world.
Edge computing and AI, for example, are transforming the agriculture industry by providing new ways to optimize crop yields and improve decision-making processes.
Edge computing also helps to reduce the cost and latency associated with sending data to remote servers for analysis, making it a more practical solution for remote or rural farming operations.
Some of the key industry challenges that computer vision and Edge computing are helping to improve include:
A diminishing workforce: The percentage of people working in agriculture has dropped from 40% to 27% in the last two decades, resulting in understaffed farm operations.
An increase in climate-related disruptions: More frequent, extreme weather events can negatively impact crop productivity.
Pest problems: The Food and Agriculture Organization estimates that up to 40% of all crops are lost due to pests and highlights that plant diseases alone cause $220 billion in damages annually.
Simply put, the agriculture industry faces the daunting task of feeding a growing population with limited resources in a rapidly changing and complex environment.
To overcome these challenges, the adoption of innovative technologies is essential. And that’s where computer vision and Edge solutions come into play.
These technologies are helping in use cases such as:
Autonomous Farm Equipment: Autonomous farm tools help farmers to farm more land with fewer workers. With advances in robot guidance and edge computer vision, farmers can automate their operations. Machine learning models for object detection and segmentation are also used in tractors and harvesters.
Crop Monitoring: Farmers use soil sensors, weather forecasts, and multi-level imagery data to monitor large land areas to prevent crop loss remotely. This data is synthesized into "crop intelligence" to allow farmers to take informed action.
Grading and Sorting: Previously performed by hand, edge computer vision now helps with this work. Optical sorting uses image processing techniques like object detection, classification, and anomaly detection to improve food production quality control.
See the resources below to learn more about the transformative power of computer vision and Edge computing in the agricultural sector.
Solutions for a food-secure world
The following article focuses on using computer vision, machine learning, and artificial intelligence in the agricultural industry.
The world's farmers face the challenges of feeding the planet while optimizing land and resources.
Computer vision and AI can help drive innovation and meet these goals!
How Computer Vision is Changing Agriculture in 2023
Edge AI transforms agricultural equipment
The paper below discusses using innovative technology to improve the efficiency of agricultural equipment, focusing its analysis on water pumps.
Here, the authors explore adding intelligence to sensors installed on pumps to detect malfunctions using an on-device (Edge-based) intelligence approach, simplifying installation and reducing costs.
AI takes on Mother Nature
Agricultural and construction equipment companies are using AI to help farmers improve crop yield and control daily farm tasks.
Adopting AI offers high-tech solutions for agricultural facilities that face challenges such as bad weather, poor soil conditions, and limited growing seasons.
As this article highlights, implementing AI in agriculture requires Edge computing, as there’s often no central onsite facility for servers and communications networks, making it essential for processing to be onboard the machines doing the work.
AI Meets the Farmer, Helping Make Each Plant Count
Some extra resources
Addressing new cyber risks
According to the article below, Edge computing improves response times, reduces bandwidth usage, and increases the real-time value of data by bringing the processing closer to the source.
However, Edge computing also creates new security vulnerabilities that require new approaches to protect against them.
The Rise of the Edge Is a New Cybersecurity Challenge
The evolving role of cloud engineers
Cloud computing has transformed how organizations store and process data, making it more efficient, cost-effective, and scalable.
Cloud engineers are critical in this transformation as they design, implement, and maintain cloud computing infrastructure and systems.
As this article points out, even cloud engineers must design and deploy edge-based applications to make them more efficient and responsive for end-users.
What Does It Mean to Become a Cloud Professional In 2023?
Balancing latency in Edge computing
Edge computing involves bringing computing and storage resources closer to end-users to support applications and services that require low network latency.
These applications include AR, connected cars, gaming, and video analytics. However, variations in latency can still impact performance.
This article addresses the problem of determining where and when to place application instances on Edge nodes to ensure requests are served within a particular deadline.
Latency-Aware Provisioning of Resources in Edge Computing: A Robust Optimization Approach
Xailient’s newest article
Instead of sending data to a centralized data center or cloud, Edge computing allows data to be processed and analyzed locally, making it a cost-effective, efficient option for businesses of all sizes.
To learn more about Edge computing, see this week’s article What is Edge Computing, and How Does it Work?
Thanks for reading,
See you next week!