From Guesswork to Growth: A Grower’s Journey with AI in the Field

By Gabe Sibley, CEO & Co-Founder, Verdant Robotics

Farming is full of pressure points—rising labor costs, thinning margins, tighter regulations, and the need to do more with less. Nowhere is that pressure more intense than in specialty crops across the Western U.S., where success hinges on rapid decisions, razor-thin timing, and reliable outcomes.
There’s a growing group of operators who are rewriting what’s possible. One of them is Daniel Alameda, a third-generation grower at Topflavor Farms in California.  Alameda was among the first to adopt the SharpShooter, and what started as an early trial has grown into something much bigger. His operation is now expanding its fleet, and his team uses Verdant Robotics’ AI-powered tools in the field every day as a practical solution to today’s challenges.
Alameda’s operation shows us that precision isn’t just a buzzword—it’s a business advantage – and in his hands, it’s redefining what efficient, sustainable farming looks like.
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Meet the Grower
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Topflavor Farms is a family-run operation in the heart of the Salinas Valley, CA. For three generations, they’ve grown specialty crops like romaine, broccolini, leeks, and cauliflower, supplying fresh produce to major shippers across the U.S. They set themselves apart through their dedication to sustainable practices and delivering fresh, high-quality produce.
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Daniel Alameda is part of the latest generation at the helm and is one of the most forward-thinking growers I know when it comes to applying new technology that works in real-world field conditions.
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A New Kind of Pressure
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Daniel’s operation grows vegetables year-round, moving between Yuma, Arizona in the winter and Salinas, CA in the summer. He’s dealing with the same challenges every specialty crop farmer faces—rising labor costs and scarcity, regulatory pressure, shrinking margins, and consumer-driven demand for perfect product.
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“Fifteen years ago, a box of heads of lettuce sold for $10 to $20 a box. It still does,” Daniel told us. “While everything else—labor, fuel, fertilizer—has gone up. We had to find new ways to stay ahead.”
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Like many growers in the Western U.S., Daniel isn’t just trying to grow food. He’s trying to build resilience into an operation that has no margin for error. He knows that the old model—more horsepower, more labor, more inputs—isn’t sustainable anymore.
Daniel shared, "Now it’s about making smarter, more precise decisions to increase operational productivity."
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The Importance of Action
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Daniel and his team started experimenting with precision robotics and other tools that could help with labor shortages and reduce input waste. He wasn’t looking for silver bullets. He was looking for systems that could fit into the way they already work, not force them to change everything overnight.
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The first wave of AI in agriculture helped farmers analyze data—mapping fields, forecasting yields, or flagging anomalies. The missing piece was always action. What do you do with that data?
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“We didn’t need more data,” he told me. “We needed something that could act on it.”
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That’s when he saw the SharpShooter in action.
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What is the SharpShooter?
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At Verdant Robotics, we built the SharpShooter to answer the challenges that Daniel and his peers have clearly stated. It’s the first precision application system that aims before it applies—delivering targeted input to individual plants in real time. Weeds, thinning, beneficials—every shot is calculated and delivered with millimeter-level accuracy, even under canopy or in tight crop spacing.
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Using computer vision and robotic aiming, the system identifies and acts on individual plants in real time—scanning the field as it moves, detecting weeds or crop targets, and delivering micro-doses of inputs like herbicides or beneficials precisely where they’re needed.
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Unlike traditional sprayers or mechanical weeders, the SharpShooter aims before it applies, delivering microliter amounts of input to millimeter-sized targets—at up to 270 shots per second. The result is more precise application, dramatically reduced chemical use, and significantly less reliance on manual labor.
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The Real Work Is in the Margins
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Daniel witnessed the system scanning rows of broccolini, identifying weeds, and applying micro-doses of herbicide—all without damaging the crop. What stood out wasn’t just speed or accuracy, but its ability to handle the edge cases: the last 10% of weeds that only hand crews had been able to reach—until now.
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“We can do 90% of the job with the tools we’ve got,” Daniel explained. “But that last 10%? That’s where the margin is. That’s where the opportunity is.”
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His crew still plays a vital role. He’s not trying to replace them—he’s trying to support them. Precision tools help fill the gaps, especially during peak season when labor is tight, and time is short.
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He described it like this: “You can’t throw enough people at the field. Even if we wanted to, we can’t. So we have to be smarter. That’s the future. The SharpShooter is changing the game. What used to take 30 people all day can now be done with one machine and one operator.”
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Seeing and Adapting in Real Time
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One of the things Daniel appreciates most is the ability to see and evaluate what the machine is doing as it happens. He can sit in the driver’s seat, watch the footage, and literally see the decisions being made. If something’s off, his team—and ours—can adjust. Quickly.
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“It’s making decisions in real time,” Daniel says. “We watch it analyze, target, and fire. We grade the job at the end of the day, and see the machine getting smarter. That’s the goal—real-time decisions and real-time improvement.”
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Daniel believes our ability to adapt and iterate is key to the next phase of AgTech: individualized plant care. Not just removing weeds, but monitoring nutrition, diagnosing plant health, and applying beneficials exactly when and where they’re needed.
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“We’re not treating a field as one unit anymore,” he said. “We’re treating each plant individually. That’s the kind of decision-making we need to stay competitive.”
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Why His Story Matters
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When we founded Verdant Robotics, we didn’t start with a product roadmap. We started with questions. We walked fields. We asked farmers what they needed, and we listened.
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Daniel’s story reminds me why that matters. Because technology that doesn’t fit the realities of farming is just noise. The real breakthroughs happen when our team of agricultural experts and best-in-class technologists co-create—with farmers, not just for them.
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“I think we’ve always been progressive,” Daniel told me. “However, what used to be progressive is now just the way we farm. We have to evolve.”
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Daniel’s not the only one making the shift. Across the U.S., specialty crop growers are starting to see precision not as a luxury, but as a necessity. The economics demand it. The environment demands it. The market demands it.
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The good news? The Sharpshooter works and it’s translating into meaningful results:
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1. Labor cost reduction up to 85%

Daniel shared that in some fields, the need for hand crews has dropped dramatically—allowing labor to focus on higher-value tasks, not repetitive weeding.
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2. Input savings of up to 99%
Input waste is nearly eliminated because the SharpShooter applies only what’s needed - where it’s needed. For farms using herbicides or foliar nutrients, this creates both cost savings and regulatory benefits.
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3. Return on investment in as little as 6–18 months
Growers like Daniel are seeing ROI in a single season, especially when using SharpShooter for multiple applications and in dense crops.
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Most importantly—they’re taking back control of their operations. One field. One plant. One decision at a time.
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“We used to assume we were making the right call,” Daniel reflects. “Now, with AI, we know.”
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Where We’re Headed
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Looking ahead, the SharpShooter is just the beginning. Our team is working alongside farmers like Daniel to unlock what’s next: real-time crop diagnostics, plant-by-plant prescriptions, and dynamic application strategies.
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We’re not just automating tasks. We’re creating an ecosystem of adaptive agriculture—where every pass across a field generates new insight, and every decision is tracked and leads to better outcomes.

About the Author
A robotics pioneer with a background in AI and computer vision, Gabe Sibley is the CEO and co-founder of Verdant Robotics. He has worked on groundbreaking projects including autonomous vehicles and space exploration. His vision drives Verdant Robotics to deliver practical, high-impact solutions for growers.

Gabe's career includes positions as an assistant professor in Computer Science at George Washington University and the University of Colorado Boulder, where he directed the Autonomous Robotics and Perception Group. His research has significantly contributed to robotics, computer vision, and simultaneous localization and mapping (SLAM), with numerous publications and citations in these fields.

In industry, Gabe co-founded the autonomous delivery company Zippy.ai (acquired by General Motors) and served as Chief Science Officer for the autonomous taxi company Zoox (acquired by Amazon). His work focuses on integrating perception, learning, planning, and control to enable long-term autonomy in robots.

Under his leadership at Verdant Robotics, the company is at the forefront of developing innovative robotic solutions to enhance agricultural productivity and sustainability.

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