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Jay Shroeder, CTO at CNH – Interview Series

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Jay Shroeder, CTO at CNH – Interview Series

Jay Schroeder serves as the Chief Technology Officer (CTO) at CNH, overseeing the company’s global research and development operations. His responsibilities include managing areas such as technology, innovation, vehicles and implements, precision technology, user experience, and powertrain. Schroeder focuses on enhancing the company’s product portfolio and precision technology capabilities, with the aim of integrating precision solutions across the entire equipment range. Additionally, he is involved in expanding CNH’s alternative propulsion offerings and providing governance over product development processes to ensure that the company’s product portfolio meets high standards of quality and performance.

Through its various businesses, CNH Industrial, produces, and sells agricultural machinery and construction equipment. AI and advanced technologies, such as computer vision, machine learning (ML), and camera sensors, are transforming how this equipment operates, enabling innovations like AI-powered self-driving tractors that help farmers address complex challenges in their work.

CNH’s self-driving tractors are powered by models trained on deep neural networks and real-time inference. Can you explain how this technology helps farmers perform tasks like planting with extreme precision, and how it compares to autonomous driving in other industries like transportation?

While self-driving cars capture headlines, the agriculture industry has quietly led the autonomous revolution for more than two decades. Companies like CNH pioneered autonomous steering and speed control long before Tesla. Today, CNH’s technology goes beyond simply driving to conducting highly automated and autonomous work all while driving themselves. From precisely planting seeds in the ground exactly where they need to be, to efficiently and optimally harvesting crops and treating the soil, all while driving through the field, autonomous farming isn’t just keeping pace with self-driving cars – it’s leaving them in the dust. The future of transportation may be autonomous, but in farming, the future is already here.

Further, CNH’s future-proofed tech stack empowers autonomous farming far beyond what self-driving cars can achieve. Our software-defined architecture seamlessly integrates a wide range of technologies, enabling automation for complex farming tasks that are much more challenging than simple point-A-to-B navigation. Interoperability in the architecture empowers farmers with unprecedented control and flexibility to layer on heightened technology through CNH’s open APIs. Unlike closed systems, CNH’s open API allows farmers to customize their machinery. Imagine camera sensors that distinguish crops from weeds, activated only when needed—all while the vehicle operates autonomously. This adaptability, combined with the ability to handle rugged terrain and diverse tasks, sets CNH’s technology apart. While Tesla and Waymo make strides, the true frontier of autonomous innovation lies in the fields, not on the roads.

The concept of an “MRI machine for plants” is fascinating. How does CNH’s use of synthetic imagery and machine learning enable its machines to identify crop type, growth stages, and apply targeted crop nutrition?

Using AI, computer vision cameras, and massive data sets, CNH is training models to distinguish crops from weeds, identify plant growth stages, and recognize the health of the crop across the fields to determine the exact amount of nutrients and protection needed to optimize a crop’s yield. For example, with the Augmenta Field Analyzer, a computer vision application scans the ground in front of the machine as it’s quickly moving through the field (at up to 20 mph) to assess crop conditions on the field and which areas need to be treated, and at what rate, to make those areas healthier.

With this technology, farmers are able to know and treat exactly where in the field a problem is building so that instead of blanketing a whole field with a treatment to kill weeds, control pests, or add necessary nutrients to boost the health of the crops, AI and data-informed spraying machines automatically spray only the plants that need it. The technology enables the exact amount of chemical needed, applied in exactly the right spot to precisely address the plants’ needs and stop any threat to the crop. Identifying and spraying only (and exactly) weeds as they grow among crops will eventually reduce the use of chemicals on fields by up to 90%. Only a small amount of chemical is needed to treat each individual threat rather than treating the whole field in order to reach those same few threats.

To generate photorealistic synthetic images and improve datasets quickly, CNH uses biophysical procedural models. This enables the team to quickly and efficiently create and classify millions of images without having to take the time to capture real imagery at the scale needed. The synthetic data augments authentic images, improving model training and inference performance. For example, by using synthetic data, different situations can be created to train the models – such as various lighting conditions and shadows that move throughout the day. Procedural models can produce specific images based on parameters to create a dataset that represents different conditions.

How accurate is this technology compared to traditional farming methods?

Farmers make hundreds of significant choices throughout the year but only see the results of all those cumulative decisions once: at harvest time. The average age of a farmer is increasing and most work for more than 30 years. There is no margin for error. From the moment the seed is planted, farmers need to do everything they can to make sure the crop thrives – their livelihood is on the line.

Our technology takes a lot of the guesswork out of farmers’ tasks, such as determining the best ways to care for growing crops, while giving farmers extra time back to focus on solving strategic business challenges. At the end of the day, farmers are running massive businesses and rely on technology to help them do so most efficiently, productively and profitably.

Not only does the data generated by machines allow farmers to make better, more informed decisions to get better results, but the high levels of automation and autonomy in the machines themselves perform the work better and at a higher scale than humans are able to do. Spraying machines are able to “see” trouble spots in thousands of acres of crops better than human eyes and can precisely treat threats; while technology like autonomous tillage is able to relieve the burden of doing an arduous, time-consuming task and perform it with more accuracy and efficiency at scale than a human could. In autonomous tillage, a fully autonomous system tills the soil by using sensors combined with deep neural networks to create ideal conditions with centimeter-level precision. This prepares the soil to allow for highly consistent row spacing, precise seed depth, and optimized seed placement despite often drastic soil changes across even one field. Traditional methods, often reliant on human-operated machinery, typically result in more variability in results due to operator fatigue, less consistent navigation, and less accurate positioning.

During harvest season, CNH’s combine machines use edge computing and camera sensors to assess crop quality in real-time. How does this rapid decision-making process work, and what role does AI play in optimizing the harvest to reduce waste and improve efficiency?

A combine is an incredibly complex machine that does multiple processes — reaping, threshing, and gathering — in a single, continuous operation. It’s called a combine for that very reason: it combines what used to be multiple devices into a single factory-on-wheels. There is a lot happening at once and little room for error. CNH’s combine automatically makes millions of rapid decisions every twenty seconds, processing them on the edge, right on the machine. The camera sensors capture and process detailed images of the harvested crops to determine the quality of each kernel of the crop being harvested — analyzing moisture levels, grain quality, and debris content. The machine will automatically make adjustments based on the imagery data to deploy the best machine settings to get optimal results. We can do this today for barley, rice, wheat, corn, soybeans, and canola and will soon add capabilities for sorghum, oats, field peas, sunflowers, and edible beans.

AI at the edge is crucial in optimizing this process by using deep learning models trained to recognize patterns in crop conditions. These models can quickly identify areas of the harvest that require adjustments, such as altering the combine’s speed or modifying threshing settings to ensure better separation of grain from the rest of the plant (for instance, keeping only each and every corn kernel and removing all pieces of the cob and stalk). This real-time optimization helps reduce waste by minimizing crop damage and collecting only high-quality crops. It also improves efficiency, allowing machines to make data-driven decisions on the go to maximize farmers’ crop yield, all while reducing operational stress and costs.

Precision agriculture driven by AI and ML promises to reduce input waste and maximize yield. Could you elaborate on how CNH’s technology is helping farmers cut costs, improve sustainability, and overcome labor shortages in an increasingly challenging agricultural landscape?

Farmers face tremendous hurdles in finding skilled labor. This is especially true for tillage – a critical step most farms require to prepare the soil for winter to make for better planting conditions in the spring. Precision is vital in tillage with accuracy measured to the tenth of an inch to create optimal crop growth conditions. CNH’s autonomous tillage technology eliminates the need for highly skilled operators to manually adjust tillage implements. With the push of a button, the system autonomizes the whole process, allowing farmers to focus on other essential tasks. This boosts productivity and the precision conserves fuel, making operations more efficient.

When it comes to crop maintenance, CNH’s sprayer technology is outfitted with more than 125 microprocessors that communicate in real-time to enhance cost-efficiency and sustainability of water, nutrient, herbicide, and pesticide use. These processors collaborate to analyze field conditions and precisely determine when and where to apply these nutrients, eliminating an overabundance of chemicals by up to 30% today and up to 90% in the near future, drastically cutting input costs and the amount of chemicals that go into the soil. The nozzle control valves allow the machine to accurately apply the product by automatically adjusting based on the sprayer’s speed, ensuring a consistent rate and pressure for precise droplet delivery to the crop so each drop lands exactly where it needs to be for the health of the crop. This level of precision reduces the need for frequent refills, with farmers only needing to fill the sprayer once per day, leading to significant water/chemical conservation.

Similarly, CNH’s Cart Automation simplifies the complex and high-stress task of operating a combine during harvest. Precision is crucial to avoid collisions between the combine header and the grain cart driving within inches of each other for hours at a time. It also helps lessen crop loss. Cart Automation enables a seamless load-on-the-go process, reducing the need for manual coordination and facilitating the combine to continue performing its job without having to stop. CNH has done physiological testing that shows this assistive technology lowers stress for combine operators by approximately 12% and for tractor operators by 18%, which adds up when these operators are in these machines for up to 16 hours a day during harvest season.

CNH brand, New Holland, recently partnered with Bluewhite for autonomous tractor kits. How does this collaboration fit into CNH’s broader strategy for expanding autonomy in agriculture?

Autonomy is the future of CNH, and we are taking a purposeful and strategic approach to developing this technology, driven by the most pressing needs of our customers. Our internal engineers are focused on developing autonomy for our large agriculture customer segment– farmers of crops that grow in large, open fields, like corn and soybeans. Another important customer base for CNH is farmers of what we call “permanent crops” that grow in orchards and vineyards. Partnering with Bluewhite, a proven leader in implementing autonomy in orchards and vineyards, allows us the scale and speed to market to be able to serve both the large ag and permanent crop customer segments with critically needed autonomy. With Bluewhite, we are delivering a fully autonomous tractor in permanent crops, making us the first original equipment manufacturer (OEM) with an autonomous solution in orchards and vineyards.

Our approach to autonomy is to solve the most critical challenges customers have in the jobs and tasks where they are eager for the machine to complete the work and remove the burden on labor. Autonomous tillage leads our internal job autonomy development because it’s an arduous task that takes a long time during a tightly time-constrained period of the year when a number of other things also need to happen. A machine in this instance can perform the work better than a human operator. Permanent crop farmers also have an urgent need for autonomy, as they face extreme labor shortages and need machines to fill the gaps. These jobs require the tractors to drive 20-30 passes through each orchard or vineyard row per season, performing important jobs like applying nutrients to the trees and keeping the grass between vines mowed and free of weeds.

Many of CNH’s solutions are being adopted by orchard and vineyard operators. What unique challenges do these environments present for autonomous and AI-driven machinery, and how is CNH adapting its technologies for such specialized applications?

The windows for harvesting are changing, and finding skilled labor is harder to come by. Climate change is making seasons more unpredictable; it’s mission-critical for farmers to have technology ready to go that drives precision and efficiency for when crops are optimal for harvesting. Farming always requires precision, but it’s particularly necessary when harvesting something as small and delicate as a grape or nut.

Most automated driving technologies rely on GPS to guide machines on their paths, but in orchards and vineyards those GPS signals can be blocked by tree and vine branches. Vision cameras and radar are used in conjunction with GPS to keep machines on their optimal path. And, with orchards and vineyards, harvesting is not about acres of uniform rows but rather individual, varied plants and trees, often in hilly terrain. CNH’s automated systems adjust to each plant’s height, the ground level, and required picking speed to ensure a quality yield without damaging the crop. They also adjust around unproductive or dead trees to save unnecessary inputs. These robotic machines automatically move along the plants, safely straddling the crop while delicately removing the produce from the tree or vine. The operator sets the desired picking head height, and the machines automatically adjust to maintain those settings per plant, regardless of the terrain. Further, for some fruits, the best time to harvest is when its sugar content peaks overnight. Cameras equipped with infrared technology work in even the darkest conditions to harvest the fruit at its optimal condition.

As more autonomous farming equipment is deployed, what steps is CNH taking to ensure the safety and regulatory compliance of these AI-powered systems, particularly in diverse global farming environments?

Safety and regulatory compliance are central to CNH’s AI-powered systems, thus CNH collaborates with local authorities in different regions, allowing the company to adapt its autonomous systems to meet regional requirements, including safety standards, environmental regulations, and data privacy laws. CNH is also active in standards organizations to ensure we meet all recognized and emerging standards and requirements.

For example, autonomous safety systems include sensors like cameras, LiDAR, radar and GPS for real-time monitoring. These technologies enable the equipment to detect obstacles and automatically stop when it detects something ahead. The machines can also navigate complex terrain and respond to environmental changes, minimizing the risk of accidents.

What do you see as the biggest barriers to widespread adoption of AI-driven technologies in agriculture? How is CNH helping farmers transition to these new systems and demonstrating their value?

Currently, the most significant barriers are cost, connectivity, and farmer training.

But better yields, lowered expenses, lowered physical stress, and better time management through heightened automation can offset the total cost of ownership. Smaller farms can benefit from more limited autonomous solutions, like feed systems or aftermarket upgrade kits.

Inadequate connectivity, particularly in rural areas, poses challenges. AI-driven technologies require consistent, always-on connectivity. CNH is helping to address that through its partnership with Intelsat and through universal modems that connect to whatever network is nearby–wifi, cellular, or satellite–providing field-ready connectivity for customers in hard to reach locations. While many customers fulfill this need for internet connectivity with CNH’s market-leading global mobile virtual network, existing cellular towers do not enable pervasive connection.

Lastly, the perceived learning curve associated with AI technology can feel daunting. This shift from traditional practices requires training and a change in mindset, which is why CNH works hand-in-hand with customers to make sure they are comfortable with the technology and are getting the full benefit of systems.

Looking ahead, how do you envision CNH’s AI and autonomous solutions evolving over the next decade?

CNH is tackling critical, global challenges by developing cutting-edge technology to produce more food sustainably by using fewer resources, for a growing population. Our focus is empowering farmers to improve their livelihoods and businesses through innovative solutions, with AI and autonomy playing a central role. Advancements in data collection, affordability of sensors, connectivity, and computing power will accelerate the development of AI and autonomous systems. These technologies will drive progress in precision farming, autonomous operation, predictive maintenance, and data-driven decision-making, ultimately benefiting our customers and the world.

Thank you for the great interview, readers who wish to learn more should visit CNH.

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