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Craig Ganssle, Founder & CEOFor over 30 years, Ganssle built a distinguished career in technology, working with AI and image recognition as early as 1995 while serving in the military. His path into agriculture was accidental but transformative, sparked by an encounter that would lead him to rethink how technology could solve age-old farming challenges.
It all started with a pair of Google Glass.
As one of the smart glasses’ first users, Ganssle was invited to a John Deere Developer Conference in Iowa to demonstrate its capabilities. Someone asked if the device could capture crop images using voice commands during the event. This seemingly simple thought led Ganssle to pose a more profound question: Why not use AI to analyze these images and provide actionable insights instantly?
The idea marked a turning point.
Ganssle realized that despite agriculture’s reliance on visual observations—like pest identification and crop health monitoring—AI was barely being applied to the sector. This realization led him to bridge the gap between technology and farming.
Building Solutions through Collaboration
Over a decade later, Farmwave stands as a testament to Ganssle’s vision, blending his 30-plus years of technological expertise with the wisdom of seasoned agronomists.
“The big story,” Ganssle reflects, “is how two industries—agriculture and technology—can come together to solve real-world problems.”
Farmwave’s success stems from its collaborative approach. Farmers and crop advisors bring decades of agronomic knowledge, while Farmwave contributes cutting-edge AI solutions. This partnership has created tools that deliver immediate, tangible value to farmers in the field.
Ganssle emphasizes that his journey into agriculture wasn’t about replacing expertise but amplifying it.
“Our partners and advisors have forgotten more about farming than I’ll ever know. But together, we’ve developed solutions that truly make a difference.”
Tackling Grain Loss: A $2 Billion Problem
Farmwave’s primary focus is grain loss, which costs U.S. farmers over $2 billion annually- and that’s just for corn. Traditional methods of measuring grain loss, such as hand-counting kernels in the dirt or using outdated strike plate sensors are labor-intensive, slow, and imprecise. With labor shortages exacerbating the problem, farmers often skip these checks altogether, leaving significant revenue on the ground.
With Farmwave’s real-time harvest loss system, farmers no longer have to choose between saving time and saving money. Farmwave’s AI-powered harvest loss detection system automates this process, delivering up-to-the-minute granular data. Using camera sensors mounted on combines, the system provides over 140 counts per acre—a vast improvement to the sporadic hand-checks farmers typically perform. The cameras detect losses and pinpoint their source, whether from the header, the combine itself, or even specific sections of the machinery.
This precision empowers farmers to make immediate adjustments.
“One farmer in Illinois discovered he was losing 16 bushels off the left side of his header. Farmwave identified the issue instantly, enabling him to make a quick mechanical adjustment and significantly reduce losses,” Ganssle recalled.
Beyond the Combine: Adapting to Farmers’ Needs
"We designed Farmwave so it could be used by every farmer. It had to be easy to add to any combine, new or old," Ganssle said.
The big story is how two industries—agriculture and technology—can come together to solve real-world problems
Farmwave’s technology is designed to be accessible and versatile. As a standalone, third-party retrofit system, Farmwave works on any make or model of combine - whether farmers have a brand new combine or one that’s seen its fair share of harvests.
The system supports a wide range of crops, including corn, soybeans, wheat, peanuts, cotton, and edible beans. Its cameras, held in place by industrial magnets, can be customized to fit different equipment setups.
“Most farmers use a three-camera system,” Ganssle explains, “but it can support up to ten cameras for more complex setups, like peanut harvesters. Farmers can easily install it in hours, making it flexible and user-friendly.”
Farmwave is continually evolving, supporting crops like canola, barley, oats, lentils, and peas. The upcoming software update will include real-time recommendations, helping farmers optimize their equipment settings based on detected losses—a feature particularly valuable for operators with less experience.
Solving Challenges with AI
Farmwave tackles challenges beyond just grain loss. Labor shortages remain a pressing issue in agriculture, with farmers struggling to find skilled operators to minimize losses. Ganssle notes that many farmers can only drive one combine themselves, relying on hired operators for the others—people who may lack the expertise or time to perform manual loss checks.
Farmwave eliminates this bottleneck by automating the process. Its cameras capture images every three seconds, and results are displayed on a tablet in the cab, allowing farmers to monitor losses in real-time. By reducing the need for manual labor, the system enables operators to focus on strategic tasks, such as equipment maintenance and crop management.
Driving Sustainability and Food Security
Farmwave’s technology reduces waste and contributes to a more sustainable agricultural ecosystem. By recovering grain that would otherwise be lost, farmers can allocate resources more efficiently and improve profitability. This approach aligns with a growing global need for food security as the population continues to rise and agricultural inputs like fuel and equipment become more expensive.
“We’re helping farmers do more with less,” Ganssle emphasizes. “It’s not just about producing more food, it’s about wasting less. Every kernel saved makes a difference— for the farmer, the environment, and the world.”
Transforming the Future of Farming
Farmwave’s impact goes beyond financial gains. The technology has created more motivated and efficient workforces by improving working conditions for farm laborers and reducing manual tasks. Farmers now have the tools to optimize their operations in real-time, leading to better yields, lower costs, and a more sustainable future.
Looking ahead, Ganssle is optimistic about AI’s role in agriculture. Farmwave continues to expand its offerings, exploring machine learning and other advanced technologies to address emerging challenges. While scaling globally presents hurdles, the company is committed to leading the charge in AI-driven farming solutions.
“AI and farming are inseparable now,” Ganssle concludes. “What started as a technological experiment has become a mission to empower farmers and revolutionize agriculture. This is just the beginning.”
AI-Driven Harvest Intelligence Systems in Modern Agriculture
Harvest visibility gaps across large-scale farming operations continue to shape profitability decisions in grain production systems, particularly where combine throughput, crop loss and operator variability intersect. Executives responsible for agricultural technology investment increasingly confront limited feedback during harvest runs, where decisions are often made without precise, continuous measurement of material loss or its origin on equipment.
Traditional inspection practices and legacy sensing tools introduce delayed or aggregated signals that obscure whether inefficiencies originate at the header, within the combine, or in rear discharge. This uncertainty constrains the ability to adjust machinery settings during critical harvesting windows and places pressure on labor availability that is already stretched across multiple machines and shifting weather conditions. A more relevant standard of evaluation now centers on how precisely systems translate field conditions into quantifiable loss data while maintaining compatibility across equipment types and minimizing disruption to ongoing operations.
Decision frameworks for agricultural analytics systems now prioritize the ability to convert high-frequency visual data into structured insights that remain consistent across diverse crop types and harvesting conditions. Machine vision approaches gain relevance when they reduce dependence on manual sampling methods and extend measurement coverage without requiring pauses in field activity. Compatibility across mixed fleets of equipment influences adoption, since many operations deploy combines of varying age and configuration that cannot support uniform sensor standards. Deployment speed and physical adaptability of hardware determine whether systems remain practical within tight harvest schedules, particularly when installation must occur without extensive mechanical modification. Data value is increasingly judged by the clarity of its linkage to actionable adjustments in machine settings, rather than volume of collected imagery alone, creating pressure on solutions to bridge detection, interpretation and decision guidance within a single workflow.
Procurement decisions in agricultural technology continue to reflect constraints tied to workforce availability, variability in operator expertise and the need for systems that integrate without demanding replacement of existing machinery. Solutions that deliver consistent measurement fidelity across multiple crop environments and support incremental expansion tend to align more closely with enterprise-scale farming structures.
Attention also shifts toward the speed at which measured losses translate into guidance that can be applied while equipment is still in motion, reducing dependence on post-run analysis. Systems that remain flexible across crop varieties and harvesting equipment types gain preference in diversified farming portfolios where operational uniformity is limited. Increasingly, executive buyers evaluate whether analytical platforms can evolve from descriptive reporting into decision-support tools that align machine behavior with measured field conditions in near real time, without introducing additional complexity into operator workflows.
Farmwave applies computer vision sensing to harvest equipment through a retrofit approach that does not rely on native machine integration. It uses camera systems on combines to measure grain loss and distinguishes loss sources across header and rear discharge areas. Real-time processing converts image capture into loss metrics displayed in-cab, allowing operators to adjust settings without stopping field activity. Its system supports compatibility across multiple combine models and crop types, including corn, soybeans and wheat, enabling mixed fleet deployment. The platform extends into advisory outputs that interpret loss patterns into recommended adjustments, reducing reliance on manual inspection and improving decision timing.
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