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Securing the Prairie: The Five Key Ways Machine Learning Will Defend Agtech

Securing the Prairie: The Five Key Ways Machine Learning Will Defend Agtech
Founder William S. Cromarty poses with Kirkwall's new name plate at the NDSU Research and Technology Park T

By William S. Cromarty, CEO & Founder at Kirkwall

The Kirkwall team poses together at an event

Machine learning (ML) is poised to revolutionize the agricultural technology sector, enabling farmers to make data-driven decisions, reduce machinery downtime, and increase crop yield while defending against the growing risk of cyberattacks by hostile nations.

Over the coming year, America’s farmers are expected to experience over $3 billion in losses due to unexpected downtime of agricultural equipment, with an additional $1.2 billion in excess emergency repair costs. About 53% of farmers have experienced lost crops due to these breakdowns, with these costs being so severe for 33% of farmers that they feared losing their family farm as a result. With the rising wave of cyberattacks and ransomware on AgTech companies and their equipment—noted by the FBI as one of their top national security concerns for 2023—the cost to farmers is only expected to rise. As a result, the integration of ML algorithms into Internet of Things (IoT)-enabled agricultural equipment becomes not only a benefit to farmers and AgTech machinery manufacturers but a necessity for five key reasons:

1. Data-Driven Decision Making: Machine learning algorithms excel in analyzing vast amounts of agricultural data and extracting valuable insights while detecting anomalies. In agriculture, ML can process data from weather patterns, soil conditions, crop health, and machinery performance to provide farmers with precise recommendations and real-time notifications of critical issues. By leveraging historical data and real-time inputs, AI and ML algorithms can offer predictions for optimal planting times, irrigation schedules, and the appropriate use of fertilizers and pesticides. These data-driven insights empower farmers to make informed decisions, maximize crop yield, and minimize resource wastage, leading to increased profitability.

2.Enhanced Security: Cyberattacks on agricultural machinery are increasingly common, with recently publicized attacks on Israeli irrigation systems to alter water flow rates and volume; multiple major agricultural machinery manufacturers have critical vulnerabilities well-known by the security community but undisclosed to customers. Given the time-critical nature of planting and harvest windows, farmers are uniquely vulnerable to timed attacks on their infrastructure. The tremendous benefits of smart agricultural equipment, sprayer drones, and IoT sensor systems can only be unlocked if paired with ML-based analysis to identify anomalous behavior that may be indicative of a cyberattack or hostile compromise. By deploying these solutions on board AgTech equipment at the manufacturer level, farmers will gain the peace of mind that cutting-edge AgTech equipment is trustworthy, worth the investment, and will increase yield.

3.Predictive Maintenance: Unexpected breakdowns result in billions of dollars per year of costly downtime and reduced crop yield. The use of ML algorithms on sensormonitored critical machinery components allows for advance notice of part failures days to weeks ahead of time. This approach reduces emergency maintenance costs and ensures uninterrupted farming operations.

5.Resource Optimization: ML algorithms can optimize resource allocation and promote sustainable practices. By analyzing sensor data and historical information, ML can help farmers determine the precise amount of water, fertilizer, and other resources required for optimal crop growth in a manner that allows spot-spraying and treatment of specific areas at a time when pesticide and fertilizer costs are at an all-time high.

Kirkwall's setup within the NDSU Research and Technology Park

Machine learning is transforming agriculture by offering data-driven decision-making, defending America’s food supply chain from enemy nations, enabling predictive maintenance of critical components, and providing predictive analytics for IoT-enabled agricultural equipment. The incorporation of ML algorithms into smart agricultural equipment has become increasingly critical and serves as the best means to build trust in cutting-edge AgTech equipment at a time when autonomous farming operations are a promising answer to critical labor shortages.

About William S. Cromarty

William S. Cromarty is a former CIA officer and the CEO and Founder of Kirkwall, a Fargo-based software company that leverages Machine Learning to defend autonomous systems from critical failure. Kirkwall’s algorithm enables cyberattack detection, diagnostic monitoring, and preventative maintenance for UAVs, IoT-enabled AgTech, and industrial control systems. Kirkwall was founded in 2022 by a team of former CIA officers and special operations personnel; the company recently completed the NSF Great Plains I-Corps program, graduated from the Gener8tor gBeta investment accelerator, and currently operates out of the NDSU Technology Incubator.

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