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adaviv

Adaviv

Growing any kind of plant, be it in a house or on a farm, requires patience, managing resources and paying attention to the environment. Part of the success comes from instinct; another part from the experience of trial and error. But using data? It&rsquo;s not done enough, mostly because there isn&rsquo;t enough that&rsquo;s actionable. It means that farmers don&rsquo;t know how to best spend, so often they overspend on inputs and labor. It means decreased yields and increased costs, says Ian Seiferling, CEO and co-founder of Adaviv. The challenge is that getting consistent performances from every plant requires individual care and the labor to provide it.
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We&rsquo;re making farms more profitable by automating time-intensive tasks
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Adaviv is looking to help controlled environment farms make more efficient and informed labor decisions, all to get the most out of crops with the least amount of spending. Through artificial intelligence and machine vision, its platform for precision agriculture can capture plant-level data and give it back to growers, taking out the guesswork, and showing them what&rsquo;s effective now and what they should be planning for in the future.
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&ldquo;We&rsquo;re making farms more profitable by automating time-intensive tasks,&rdquo; he says. &ldquo;It allows growers to focus their attention where it matters most.&rdquo;
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Getting In and Getting Up Close
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Adaviv started in 2019, but the roots go deeper. Seiferling grew up in Saskatchewan, a province where seemingly everyone had a farm, he says. That early connection to nature led him to studying environmental sciences and biology. While doing doctoral and post-doc work at MIT on the scalability of growing food around cities, and working in the Senseable City Lab, he met his eventual co-founders, Mo Vazifeh and Julian Ortiz.
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They saw that new technology, like AI and vision technology, gave the ability to collect plant-level data. That information would allow growers to see what was actually working and allow them to streamline operations, allocate tasks better, and improve yields. &ldquo;Overall, it would raise the bar on the quality of the work being performed,&rdquo; Seiferling says. And the ideal target would be greenhouses and indoor farms.
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Those spaces have certain advantages in being able to control elements like water and energy, but they also work with certain constraints, some self-imposed. The scale is enormous and management is labor-intensive. The subjective nature of many decisions leads to inconsistencies in productivity. It requires people to be in the field, but the managers and cultivators, those with the expertise, can&rsquo;t be everywhere. &ldquo;They don&rsquo;t have the time, don&rsquo;t have the bandwidth,&rdquo; he says.
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The result is that rather than knowing what specifically works, a grower might do what they think will work, and, for example, use too much of an input, like water or pesticide, and blanket a crop. As much as experience is in play, there&rsquo;s guesswork as well. Farming relies heavily on the human eye, and without hard data, things get inevitably missed.
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Read

Empowering farmers with real-time AI insights

AdaViv

AdaViv

Adaviv captures plant-level data using artificial intelligence and machine vision to improve the production of high-quality crops at scale.

Read

Empowering farmers with real-time AI insights

AdaViv

Read

Empowering farmers with real-time AI insights

AdaViv

AdaViv

Adaviv captures plant-level data using artificial intelligence and machine vision to improve the production of high-quality crops at scale.

Read

Empowering farmers with real-time AI insights

AdaViv