AI at the Edge: Agriculture, Mining, and Energy

Axceta – July 5, 2024

Collected at: https://www.iotforall.com/ai-at-the-edge-agriculture-mining-and-energy

Artificial Intelligence (AI) is the science and engineering of making intelligent machines, such as computers, robots, or software, that can perform tasks that normally require human intelligence, such as perception, reasoning, learning, decision-making, or natural language processing. AI can help enhance the capabilities and functionalities of IoT devices and create more intelligent, efficient, and responsive IoT applications.

However, AI also poses some challenges, such as the need to have sufficient computing power, memory, and bandwidth, the need to have reliable and timely data, and the need to have robust and trustworthy models. This is where edge computing comes in.

Edge Computing

Edge computing is the paradigm of performing data processing and analysis at the network’s edge, near the data source, rather than in the cloud or a centralized data center. It can help to overcome the limitations and challenges of cloud computing where AI is commonly implemented, such as latency, bandwidth, cost, privacy, and security.

Edge computing can also enable and empower AI at the edge, where IoT devices can run AI models locally without relying on the cloud or the internet. This can help improve IoT devices’ performance, reliability, and autonomy and enable real-time and predictive IoT applications.

We will explore how IoT enables bringing AI workloads to the edge for agriculture, mining, and energy industries, and we will also discuss the benefits and challenges of AI at the edge for these industries.

We will also reference the previous posts in the series about IoT connectivity, IoT cloud platforms, and security, explaining how each topic is paramount to successfully deploying AI at the edge.

AI at the Edge for Agriculture

Agriculture is one of the oldest and most important human activities, providing food and raw materials for various industries. However, agriculture faces many challenges, such as population growth, climate change, resource scarcity, environmental issues, and labor shortages.

To address these challenges, agriculture must adopt innovative practices and technologies, such as precision farming, smart irrigation, crop monitoring, pest detection, and yield prediction.

IoT can help to collect and transmit large amounts of data from various sources, such as soil, water, air, plants, animals, and equipment, using various devices, such as sensors, cameras, drones, or satellites. AI can help to process and analyze these data to extract valuable insights and actionable information.

However, agriculture presents specific challenges, such as the variability and unpredictability of the environment, the connectivity and bandwidth limitations, and the power and cost constraints. This is where edge computing can help.

Edge computing can help to perform data processing and analysis at the edge of the network, near the source of the data, using various devices, such as edge servers, gateways, routers, or even the IoT devices themselves. It can reduce the latency, bandwidth, cost, and privacy issues of cloud computing and enable real-time and predictive IoT applications.

Agriculture Applications of AI at the Edge

Smart Irrigation

IoT devices, such as soil moisture sensors, weather stations, or water valves, can run AI models at the edge to monitor and control the irrigation system based on the soil condition, weather forecast, crop type, and water availability, without relying on the cloud or the internet. This can help to optimize water usage, reduce water wastage, and improve crop yield.

Crop Monitoring

IoT devices, such as cameras, drones, or satellites, can run AI models at the edge to capture and analyze images of the crops using computer vision techniques, such as object detection, segmentation, or classification, without relying on the cloud or the internet.

This can help to detect and identify various crop parameters, such as growth stage, health status, nutrient level, or disease symptoms, and to provide timely and accurate feedback and recommendations to the farmers.

Pest Detection

IoT devices, such as cameras, microphones, or traps, can run AI models at the edge to detect and identify various pests, such as insects, rodents, or birds, using computer vision or audio processing techniques, such as image recognition, face recognition, or speech recognition, without relying on the cloud or the internet. This can help to prevent and control pest infestation, reduce crop damage, and minimize pesticide usage.

AI at the Edge for Mining

Mining is one of the most vital and challenging human activities, providing essential minerals and metals for various industries. However, mining has challenges like resource depletion, environmental degradation, safety hazards, and operational inefficiencies.

To address these challenges, mining must adopt innovative practices and technologies, such as autonomous mining, smart exploration, mineral processing, asset management, and worker protection.

IoT can help to collect and transmit large amounts of data from various sources, such as rocks, ores, equipment, vehicles, or workers, using various devices, such as sensors, cameras, drones, or robots. AI can help to process and analyze these data to extract valuable insights and actionable information.

However, mining comes with a particularly harsh and dynamic environment where connectivity, bandwidth, and power are limited.

This can help reduce the latency, bandwidth, cost, and privacy issues of cloud computing and enable real-time and predictive IoT applications. This can help improve IoT devices’ performance, reliability, and autonomy and enable more intelligent, efficient, safe, and responsive IoT applications.

Mining Applications of AI at the Edge

Autonomous Mining

IoT devices, such as cameras, lidars, or radars, can run AI models at the edge to enable autonomous operation of mining equipment, such as trucks, drills, or excavators, using computer vision techniques, such as object detection, tracking, or recognition, without relying on the cloud or the internet. This can help to improve productivity, safety, and fuel efficiency, as well as to reduce labor costs and human errors.

Smart Exploration

IoT devices, such as sensors, drones, or satellites, can run AI models at the edge to enable smart exploration of mining sites using machine learning techniques, such as regression, classification, or clustering, without relying on the cloud or the internet.

This can help to discover and evaluate new mineral deposits, optimize drilling and blasting operations, and reduce environmental impacts.

Mineral Processing

IoT devices, such as sensors, cameras, or spectrometers, can run AI models at the edge to enable mineral processing of mining ores, using machine learning or computer vision techniques, such as feature extraction, dimensionality reduction, or anomaly detection, without relying on the cloud or the internet.

This can help to improve the quality and quantity of the minerals extracted, reduce waste and emissions, and increase profitability.

AI at the Edge for Energy

Energy is one of the most fundamental and critical human needs, providing power and heat for various industries and applications. Like many other industries, energy faces demand fluctuation, grid instability, and other challenges.

To address these, the energy industry must adopt innovative practices and technologies, such as renewable energy, smart grid, energy storage, demand response, and energy efficiency.

IoT can help to collect and transmit large amounts of data from various sources, such as generation, transmission, distribution, consumption, or storage, using various devices, such as sensors, meters, switches, or batteries. AI can help process and analyze these data.

Still, you have to consider the variability and uncertainty of the sources, the connectivity and bandwidth limitations, and the power and cost constraints, making it challenging to analyze all this data in the Cloud.

Edge computing can help to perform data processing and analysis at the edge of the network, near the source of the data to reduce the latency, bandwidth, cost, and privacy issues of cloud computing and enable real-time and predictive IoT applications.

Energy Applications of AI at the Edge

Renewable Energy

IoT devices, such as solar panels, wind turbines, or hydroelectric generators, can run AI models at the edge to optimize the generation and distribution of renewable energy, using machine learning techniques, such as optimization, forecasting, or control, without relying on the cloud or the internet.

This can help to increase the efficiency and reliability of renewable energy sources, reduce dependence on fossil fuels, and lower greenhouse gas emissions.

Smart Grid

IoT devices, such as smart meters, smart switches, or smart inverters, can run AI models at the edge to enable smart grid management and operation using machine learning techniques, such as anomaly detection, load balancing, or demand response, without relying on the cloud or the internet.

This can help improve the grid’s stability and resilience, reduce peak demand and congestion, and lower operational costs and losses.

Energy Storage

IoT devices, such as batteries, capacitors, or flywheels, can run AI models at the edge to enable energy storage and utilization, using machine learning techniques, such as state estimation, scheduling, or dispatching, without relying on the cloud or the internet.

This can help to store and use the excess or surplus energy, smooth the fluctuations and variations of the energy supply and demand, and increase the flexibility and availability of the energy system.

Energy Efficiency

IoT devices, such as thermostats, lights, or appliances, can run AI models at the edge to enable energy efficiency and conservation, using machine learning techniques, such as classification, regression, or reinforcement learning, without relying on the cloud or the internet.

This can help monitor and control energy consumption and behavior, adjust the temperature, lighting, or power settings, and reduce energy waste and cost.

IoT, AI & Edge Computing

IoT and AI are two of the most disruptive and transformative technologies of our time, and they can offer many opportunities and benefits for various industries, such as agriculture, mining, and energy.

However, IoT and AI also pose many challenges and limitations, such as the need to have sufficient computing power, memory, and bandwidth, the need to have reliable and timely data, and the need to have robust and trustworthy models.

Edge computing can help to overcome these challenges and limitations by enabling and empowering AI at the edge, where IoT devices can run AI models locally without relying on the cloud or the internet. This can help improve IoT devices’ performance, reliability, and autonomy and enable real-time and predictive IoT applications.

However, AI at the edge is not a silver bullet but a tradeoff, as it involves various factors and objectives, such as functionality, efficiency, reliability, scalability, availability, usability, or affordability. It also requires the application of various best practices and tradeoffs, such as security by design, security in-depth, and security in balance, as we discussed in the previous articles in this series.

AI at the edge also requires the involvement and cooperation of various actors and stakeholders, such as device manufacturers, service providers, system operators, application developers, users, regulators, and researchers.

AI at the edge is not an end but a means to achieve the ultimate goal of IoT solutions in the agriculture, mining, and energy industries, creating more value and impact for society and the environment.