Artificial Intelligence at John Deere 

John Deere is a leading agricultural machinery and precision technology company dedicated to advancing productivity and sustainability in farming industries worldwide. The company manufactures a comprehensive range of equipment—spanning tractors, combines, and intelligent spraying solutions—serving producers of food, fuel, and infrastructure across the globe.

In 2024, John Deere reported annual revenue of $51.7 billion according to internal reports. At the end of 2024, Deere & Company employed approximately 75,800 people, with a presence on six continents and a dominant footprint across North America, Europe, and Asia.

Although John Deere does not publicly disclose precise investment amounts in AI, its commitment is evidenced by strategic acquisitions (such as the $305 million purchase of Blue River Technology) and public demonstrations of breakthrough AI-powered systems.

This analysis focuses on two AI use cases that directly support John Deere’s business goals:

• Optimizing herbicide application: Utilizing machine vision to detect and selectively spray weeds, significantly reducing chemical usage and cutting operational costs for farmers.
• Enabling autonomous field operations: Applying computer vision and AI to power driverless tractors and equipment, increasing efficiency and addressing labor shortages through fully automated farm tasks.

Optimizing Herbicide Application

The company’s traditional spraying equipment operated on a “broadcast application” model, where herbicides were applied uniformly across entire fields regardless of weed density or location, per a company news release in 2024. 

John Deere claims this approach created significant business challenges for the company and its farming customers, according to the John Deere 2022 Sustainability report. 

For John Deere, the inefficient application method:

• Limited the value proposition of their spraying equipment
• Led farmers to seek more cost-effective solutions to manage rising input costs
• Created customer dissatisfaction 

For customers, the broadcast application method: 

• Induced inconsistent application coverage
• Generated chemical waste and environmental waste
• Necessitated unsustainable costs

The broader agricultural industry also faces severe herbicide-related challenges that extend far beyond equipment manufacturers. Herbicide costs for farmers have escalated dramatically, per a news report from AgWeb, with typical applications now costing $50-$100 per acre. 

Traditional broadcast spraying systems waste enormous quantities of herbicides, with farmers spending $25 billion annually on 3 billion pounds of herbicides. Research by BloombergNEF indicates that as little as 1% of pesticides applied via broadcast actually reach the target pest. Instead, herbicides land on bare soil, healthy plants, or are carried away with rainwater. 

John Deere needed a differentiated technology that could justify premium pricing while resolving customer pain points around chemical usage and operational efficiency.

John Deere’s See & Spray technology, developed through its $305 million acquisition of Blue River Technology in 2017, represents this sophisticated solution. The system employs multiple AI capabilities working in concert:

• Computer Vision and Image Processing: The system utilizes boom-mounted cameras to scan over 2,100 square feet of crop area per second, according to John Deere’s 2024 news release, generating a continuous stream of visual data for analysis.
• Deep Neural Networks: Each image is processed through a deep neural network trained on a database of weed and crop images to achieve pixel-level classification, determining whether each plant is a crop or weed.
• Machine Learning Algorithms: The system employs convolutional neural networks powered by NVIDIA Jetson Xavier processors capable of performing “tens of trillions of operations per second,” according to the company’s use case documentation, to identify plant characteristics and make real-time spraying decisions.

Screenshot from Nature of a Workflow Diagram for Implementing a Site-specific Weed Control Approach in a Corn Field. (Source: Nature)  

In an interview with OpenAI, John Deere’s President of Lifecycle Solutions Justin Rose said that the AI solution also pioneered a shift in John Deere’s pricing model. The company shifted to subscription-based, renewable licenses, invoicing customers solely based on the acres where the technology is actively used. Farmers are only billed after spraying operations, aligning costs directly with the tangible value received. 

For farmers, the model lowers the upfront risk of investing in this new technology and ensures payment reflects the actual input cost savings and efficiency gains. For John Deere, it created a recurring revenue stream that is directly tied to customer success.

The system’s continuous learning capability also acted as a significant advancement over static spraying, per Rose in the OpenAI interview. As new machines operate across different fields, they collect new imagery and performance data that feeds back into the machine learning platform, continuously improving effectiveness. 

In 2024, See & Spray technology saved farmers an estimated 8 million gallons of herbicide and delivered an average herbicide savings of 59%, per the company’s 2024 news release. 

Independent studies by Iowa State University showed even higher savings rates, with individual fields achieving herbicide reductions ranging from 43.9% to 90.6%, averaging 76% across 415 acres tested. For farmers operating at scale, these savings can offset equipment costs rapidly.

The Iowa State study documented economic savings of $6,500 worth of herbicide products, translating to $15.7 per acre in direct chemical cost savings. 

Enabling Autonomous Field Operations

John Deere’s traditional tractor business faced mounting pressure from labor shortages and changing customer demands. In an executive conversation with RCR Wireless News, the company’s Chief Technology Officer, Jahmy Hindman revealed that farmers increasingly needed to “do more work with fewer people,” with the average farmer being over 58 years old and working 12-18 hour days. 

At large, the American Farm Bureau Federation estimates approximately 2.4 million farm jobs need to be filled annually. The USDA reports that the situation has worsened since the number of farms continues declining—dropping 7% from 2017 to 2023. 

The labor crisis threatened John Deere’s core business model, as equipment sales depend on farmers’ ability to operate machinery effectively. The company needed to evolve from selling individual machines to providing comprehensive automation solutions that could address systemic labor challenges.

John Deere’s autonomous tractor technology, enhanced through its $250 million acquisition of Bear Flag Robotics in 2021, integrates multiple AI systems to enable full autonomy. 

In a 2025 news release, the company claims the technology features 16 individual cameras arranged in pods providing 360-degree visibility around the tractor and implements. The tractors utilize high-precision GPS systems accurate to within less than an inch, ensuring precise field operations and maintaining position relative to programmed geofences.

Screenshot from Machine Journal Study on Applications and Systems in John Deere’s Autonomous Tractor Technology. (Source: Current and Future Applications of Agricultural Machines Based on Intelligent Methods)  

The autonomous tractors maintain consistent speed, depth, and pattern accuracy that human operators cannot match over extended periods. Meanwhile, farmers can monitor and control machines simultaneously through mobile applications, enabling strategic resource allocation and multi-site management.

Autonomous tractors can operate continuously day and night, only stopping for refueling or maintenance. This enables farmers to fill critical labor gaps and complete time-sensitive operations like tillage during optimal windows. 

So far, John Deere’s autonomous tractor technology has already produced measurable business impacts: 

• According to the GriNext Conference, field evidence demonstrates that farms had a 15-20% surge in productivity after technology adoption. 
• The technology has effectively addressed critical labor shortages, with autonomous tractors handling time-intensive operations like tillage that previously required skilled operators for 12+ hour shifts.
• GPS guidance and accuracy resulted in an average of 6% fuel and labor reduction, per John Deere’s 2024 Impact report.