Artificial intelligence (AI) is quickly turning the agricultural industry on its head and this is only the beginning, considering the potential of AI-based tools to increase efficiencies  and accuracy. Grain handling and storage are no exception.

Smart harvesting

Long before entering the world of grain handling, AI will start on the farm. Developments in terms of planting and harvesting are already gaining momentum to a staggering extent, says Johan van Rensburg, executive manager of Grain Services at VKB.

“AI will increasingly play a role in bringing all facets of crop production – cultivars, fertilisation, pest and weed control – together, to not only increase yields and decrease costs, but to enable producers to distinguish between quality grades and possible mycotoxin contamination in the field.

AI-powered combine harvesters with machine vision and real-time data processing capabilities will optimise grain collection by adjusting speed and blade height based on crop density and ripeness, and detecting and avoiding diseased or contaminated areas.

“In terms of contamination, mainly with mycotoxins, we will see AI being able to differentiate and place various grades in separate bins right there in the field. Heavily contaminated grains may not be fit for human or animal consumption, but there are other applications, such as ethanol, in which it can be used. “Another application of AI technology involves drones. When used in conjunction with technologies such as satellite imaging, drones can spray specific areas in a field as opposed to the current practice of spraying the entire field. AI-powered drones will be able to identify areas containing weeds and target them specifically. Apart from a cost saving, this will also contribute to more ecologically friendly practices.

AI-assisted drone surveillance is expected to play a growing role in optimising harvest timing and strategy.

“Advancements  in harvester technology will increasingly enable the operator to measure the nutritional value of grain, such as protein, starch, fat, and oil content. Equipped with multiple bins, harvesters will be capable of sorting grain by quality and directing each group to its appropriate bin. Assisted by satellite imaging, the operator will also be able to identify specific areas in the field where the grain meets the unique requirements of individual processors.”

Intelligent logistics

AI technology  is already being used in logistical systems, but will increasingly enhance efficiency. “Grain must be transported from the farm to a specific location, whether a storage facility, mill, or press. AI-coordinated fleet systems will optimise grain transport routes, reduce fuel consumption, and avoid congestion. AI-systems will also be able to coordinate transport so that space and capacity are used optimally, which will have a sizable impact on costs and efficiency.

“Blockchain-based technology will enhance traceability, making it possible to trace a specific batch of grain to a specific field of origin. This will, inter alia, make grain theft more difficult. It will also identify the specific cultivar and the chemicals used during production.

“Sensor-equipped trailers will monitor moisture, temperature, and contamination during transport. Predictive maintenance of vehicles and equipment through AI analysis will reduce downtime. Instead of servicing a vehicle at certain intervals, AI sensors will dictate the frequency and scope of maintenance intervals. The same goes for equipment and other infrastructure,” predicts Van Rensburg.

Smart storage solutions

Grain silos are increasingly being outfitted with AI systems to monitor and regulate internal climate – humidity, temperature, and CO2 – and predict and prevent spoilage, or pest infestation. This could ultimately have a significant impact on insurance costs.

AI will automate stock rotation based on real-time quality data while blockchain- backed grain traceability will be used to ensure transparency from storage to sale.

Automated grading

Machine vision and AI will analyse grain samples for size, shape, colour, and uniformity, as well as for the presence of foreign materials or mycotoxins. AI-enhanced sensors will measure protein, moisture, and oil content to reduce human error and standardise grading across regions and seasons.

Market integration and planning Producers and cooperatives will use AI models to forecast market prices and demand, and decide when and where to sell based on predictive analytics. Which processor, for example, would gain most from a specific batch of grain based on the analysis of that batch?

Leveraging blockchain technology,  the optimal value of each batch begins to be unlocked right from the harvesting stage on the farm and continues throughout the logistics journey, aligning with available processing capacity across various facilities.

Consumer-driven decision-making As consumers grow more discerning about what they eat, it is not at all far-fetched to imagine a kind of device, much like an app on a phone, that will be able to analyse not only the content of a specific food item but also the origin of the ingredients, concludes Van Rensburg. Consumer preferences  will increasingly influence the choice of raw materials, both in terms of production methods

and origin.

While there is much to be expected from AI in the future, numerous technologies worldwide are already using AI to perform certain functions in the grain handling and storage industry. These, in a nutshell (and courtesy of AI), include:

AI-powered sorting and quality

control

•   Improved accuracy: AI algorithms, often using computer vision, can analyse grains with greater speed and accuracy than traditional methods, identifying contaminants, damaged kernels, and other quality issues.

•   Reduced waste and rejects: By quickly identifying and separating subpar grains, AI helps minimise waste and rejection rates, leading to higher quality products and increased profitability.

•   Real-time monitoring: AI can be integrated with sensors to monitor grain quality in real-time, allowing for timely interventions to prevent spoilage or further contamination.

Predictive maintenance

•   Reduced downtime: AI algorithms can analyse data from sensors on machinery to predict potential equipment failures, allowing for proactive maintenance and minimising unplanned downtime.

•   Lower maintenance costs: By addressing issues before they escalate, AI-powered predictive maintenance helps reduce

the overall cost of repairs and maintenance.

Extended equipment lifespan: Optimising equipment performance through predictive maintenance can also extend the lifespan of machinery used in grain handling.

Smart grain management

•   Inventory optimisation: AI algorithms can optimise inventory management by tracking grain quality, quantity, and storage conditions in real-time, preventing spoilage and ensuring optimal storage conditions.

•   Supply chain optimisation: AI can optimise logistics, including transportation routes and delivery schedules, reducing costs and minimising delays.

•   Predictive analytics: AI can analyse weather patterns, historical data, and other factors to predict grain yields and market prices, helping producers and businesses make informed decisions.

Pest and disease detection

•   Early detection: AI-powered systems can analyse images from cameras and sensors to detect pests and diseases in grain storage facilities, enabling prompt and targeted pest control measures.

•   Reduced pesticide use: By precisely targeting pest infestations, AI can help minimise the use of pesticides, contributing to more sustainable grain handling practices.

Other applications

•   Equipment cleaning and maintenance: AI-based  systems can optimise cleaning processes for machinery, ensuring hygiene and product quality.

•   Labour optimisation: AI can automate repetitive tasks, reducing the need for manual labour and potentially alleviating labour shortages in the grain handling industry.

•   Compliance and traceability: AI can aid in ensuring compliance with regulations and improving traceability throughout the grain supply chain.

During a GEAPS Exchange 2025 concurrent session, Dave Smit, OT architect for the company Interstates discussed the role of AI in grain facilities, the critical steps for infrastructure readiness, and the importance of data governance in maximising AI’s potential. In an article in Feed & Grain, Smit said: AI adoption in grain handling follows a structured progression through five stages of digital transformation:

•   Standard reporting: Traditional grain elevators have relied on paper-based systems for data tracking and reporting.

•   Descriptive analytics: With digitalisation, data is now captured and stored centrally, allowing manual analysis.

•   Diagnostic analytics: Automation helps identify trends and informs decision-making for operational improvements.

•   Predictive analytics: AI connects data from multiple departments, enabling proactive maintenance and resource allocation.

•   Prescriptive analytics: Advanced

AI solutions integrate analytics and intelligence to automate complex decision-making.

To effectively implement AI, he said, grain facilities must establish a strong digital foundation. Preparing for AI integration involves several steps that include assessing networks for AI readiness, investing in scalable and secure

hardware and software, developing a roadmap that defines short- and long-term AI implementation goals, and evaluating emerging AI technologies. The most important, perhaps, is training personnel in accepting and using AI tools effectively. 

By Izak Hofmeyr, Plaas Media