John Deere – Bringing AI to Agriculture

 

John Deere, known for its iconic green tractors, has been making big strides into AI-driven precision agriculture.

Introduction: John Deere

John Deere is a giant in the agriculture industry, with nearly $30 million in annual revenues and a $48 billion market capitalization6. While it was  founded in 1837 as a tool manufacturer, today the company focuses primarily on manufacturing heavy equipment for the agriculture industry. The company’s iconic green tractors are one of the best-recognized machinery brands in America.

John Deere Truck Scanner has long prided itself for its technology-forward mindset. The company’s main technology focus is on what it calls “precision agriculture,” which incorporates technology into farming processes to increase productivity and yield8. John Deere began incorporating aspects of precision agriculture in the nineties with self-driving technology in tractors, and today’s farm equipment is as high-tech as the average car.

John Deere’s ML Strategy

Machine learning has huge potential to revolutionize the way we farm. Computer vision and machine learning technology can be used in every step of farming: tilling soil, planting seeds in the optimal locations, spraying fertilizer or nutrients, and harvesting5. Machines that harvest corn, for example, typically drop a percentage of the corn on the ground. A machine with blades that could dynamically adjust to the width of a corn stalk could increase yield simply by catching more of the harvest8. Other areas with the biggest potential are crop and soil monitoring, predictive analytics, and agricultural robots.

In the last few years, Heavy Duty Truck Scanner John Deere has made several big plays in agricultural AI. John May, President of Agricultural Solutions and Chief Information Officer at Deere, has said, “As a leader in precision agriculture, John Deere recognizes the importance of technology to our customers. Machine learning is an important capability for Deere’s future.”2 In September 2017, the company acquired Blue River Technologies for $305 million, a move that attracted a lot of attention in the agriculture industry2. Blue River Technologies is a Bay Area startup that uses computer vision and machine learning to reduce the use of herbicides by spraying only where weeds are present, optimizing the use of inputs in farming – a key objective of precision agriculture. John Stone, the senior vice president of the Intelligent Solutions Group said at the time of acquisition, “AI and machine learning is going to be as core to John Deere as an engine and transmission is.”2

The Blue River acquisition wasn’t John Deere’s only big move of 2017: they opened John Deere Labs in the trendy SOMA district of San Francisco, with the plan to centralize their software development there4. The move reflects the difficulty companies have attracting top tech talent to traditional agriculture cities like Des Moines. With the new lab, the company is hoping to cement its reputation as one of the leaders in agtech, particularly AI and machine learning.

While the CnAutotool.com has made some big moves, there’s still a lot of ground to cover to truly integrate ML into the company. One of John Deere’s highest priorities needs to be focusing on the development of Blue River’s see-and-spray technology. The acquisition has a lot of promise, but unless John Deere can help Blue River deploy their solution at scale, they’ll lose out on the AI revolution. The first step is to deploy the product for cotton farmers, and then to expand to soybean farmers soon after.

In the longer term, John Deere should take advantage of the advances in edge computing hardware to move some of its data processing from the cloud to the edge. Right now, John Deere tractors come equipped with a 4G LTE modem to upload and process data3. If the company plans to employ more computation-heavy algorithms at scale, the upload time will become cost-prohibitive, especially in rural areas with poor service. If the company can host some of the data pre-processing at the edge, possibly through an installed GPU on the tractor, it can deploy more algorithms at scale.

The website should also incorporate more machine learning capabilities into its Farmsight suite of products. Right now, Farmsight focuses on data processing and visualization, with some rudimentary decision analysis for farmers. But the amount of data the tool collects, as well as its wide install base, would make it the perfect system to test-drive ML capabilities.

Finally, the website could make a push into the predictive maintenance space. Machine learning is a perfect tool for predictive maintenance, and John Deere’s experience and expertise with large assets gives it the necessary capabilities to succeed where so many companies have failed.