June 24, 2025
Blog
The agricultural sector has undergone a major technical overhaul that is calling the age of agriculture 4.0. This agricultural revolution is characterized by autonomous machines carrying rafts of sensing and processing equipment. These machines collect and analyze data to make real-time decisions that improve productivity, efficiency, sustainability, and cost-effectiveness.
Agriculture is increasingly shaped by AI-powered edge computing systems. Traditional agricultural equipment such as tractors, harvester combinations, and irrigation systems carry sensors and processors that can collect data, process it at the edges, and turn those decisions into appropriate and timely interventions. AI-enabled systems check if the crop needs more water, if the soil has the right nutrients, or if the plants and livestock are being attacked by pests or diseases. Not only can these systems keep farmers informed, they can also find the right solution with minimal human input.
AI can also reduce the cost and burden of maintaining a machine. Predictive maintenance is predicted using machine learning techniques such as anomaly detection, before equipment occurs, and based on audio data collected on the machine. This reduces maintenance costs and minimizes downtime.
Industrial systems review data collected from numerous sensors sent to the cloud for processing and analysis to improve insights and develop long-term strategies. This principle is the same for farm systems, but here, field and farm remoteness uploads a large amount of data to an unreliable cloud. Local processing enabled by Edge AI resolves this issue. On-chip AI capabilities allow for smart, low-latency decisions, reducing the need to send large amounts of data to the cloud for analysis. Devices in the form of CPU, GPU, dedicated ASIC, and NPUs often process this data locally because they have built-in AI capabilities. Market Analysis House Grandview Research forecasts the global market size of Edge AI chips, reaching US$1200 billion by 2030, growing at a combined speed of 33.9% over that period, up from 16 billion in 2023.
Edge AI applications feature embedded computing modules that carry these AI-enabled processors. TRIA Technologies offers a wide range of computer-on-modules (COMs), designed in collaboration with a variety of CPU vendors, including AMD, Intel, NXP, and Renesas. A notable collaboration with Qualcomm is to enable Tria to create new generations of computing modules, based on the ARM architecture, around Qualcomm's high-performance processors, Dragonwing and Snapdragon. The latest TRIA SMARC modules are applied to a wide range of applications that meet the needs of smart agricultural systems, providing machine vision, anomaly detection, sensor collection and analysis, audio classification and more.
AI-enabled embedded computing boards are extremely advantageous for smart agricultural applications with compact size, ruggedness, flexibility and a variety of options for powerful computing. These small boards are embedded in tractors and machines, allowing ML and AI models to run locally. TRIA's board portfolio supports multiple cameras that are easily adapted for use with autonomous agricultural robots and drones. It also supports computationally intensive AI applications such as large-scale language models (LLMS) for applications that require natural language processing. This will soon allow the machine to respond to verbal communication.
The embedded computing board hardware is built to handle parallel processing to speed up models such as CNNS (convolutional neural networks). Specialized processors can handle these tasks very quickly, very low power, and run batteries and solar power. TRIA's low-power AI-enabled boards are used in anomaly detection applications that use a combination of audio and accelerometer data to predict irrigation leaks.
The agricultural community is currently testing several projects that rely on mechanical vision and ML to detect plant and livestock diseases. One such program determines that the diseased plant is suffering based on a photograph of the leaves. CNN is trained on existing datasets of leaf images to identify diseases, resulting in over 96% accuracy. Plant disease can be determined immediately, and then obtain appropriate measurements before the disease spreads.
Machine vision is also seen in agricultural “spraying” projects, which use robots and drones to refuel plants and selectively spray herbicides. These robots can function autonomously by using sensors to navigate the field, or are controlled manually via an app. One of the advantages of autonomous farming machines is that driverless machines can be smaller and lighter and reduce soil compaction in healthier soils that require less tilling. Using mechanical vision, these systems accurately identify weeds and spray them with herbicides to ensure that they have significantly fewer herbicides. Compact soil-low soil and herbicide usage saves farmers money, provides more nutritious foods, and maintains a healthy environment.
Tria takes pride in the benefits it offers its partners and customers. The company designs a board of directors for applications designated by partner companies based on device capabilities and specifications. Additionally, a partnership with Avnet (its parent company) with Tria will ensure that inventory is available to customers over a 15-year lifecycle, so items will not become outdated within that time.
Tria develops and integrates boards and devices into customized systems on behalf of its customers, eliminating the technical difficulties and long development stages that design engineers take on when introducing new products, especially as AI is a critical part of these products.
Choosing a processor for AI-enabled applications can be a difficult task. Tria offers a wide range of system-on-modules (SOMs) built around AI-enabled processors. It also offers reference designs that provide compatible baseboards based on popular standards such as SMARC and examples of machine learning applications. These allow customers to hit the ground running on the AI-On-The Edge project. This improves the customer's ability to quickly respond to new requirements without the need for involvement, time-consuming, expensive development stages, and further determines the success of the work.
