" The greenhouse industry is at a crossroads where people face a polar choice : embrace the potential of artificial intelligence ( AI ) to stand up out in a crowded marketplace or risk being commodified by sticking to outdated AI method " , say Adam Greenberg with IUNU . " Before implementing Modern applied science , one must enquire themselves : How will AI give me a free-enterprise advantage or am I buying sand in the desert ? "
To thrive in this environment , he explains we must embrace what is being called " The gamey and Hard Problem . " " This is the challenge of better both the types of data we collect and the manner we analyze it . Without both of these , growers risk becoming medium , miss out on opportunities to attain excellency . "
Right now , there ’s a spike of AI cock claiming to metamorphose greenhouse procedure . " But many of these solutions miss the Deutsche Mark because they ’re not approaching the trouble the right way . "
The four quadrants of nursery AIAdam excuse the breakdown of AI shaft with the image of two simple axis . " one-time vs. new datum and old vs. new analysis " , he say , explaining how they break off down :
sure-enough data , older analysis : Using traditional metric ( temperature , humidness ) processed with basic tools like Excel . This has its place but is n’t enough any longer . sure-enough data , new analytic thinking : lend oneself AI and motorcar learning to the same onetime data . This is where most company operate , but it does n’t unlock new opportunities . New data , old depth psychology : gather up innovative data point ( for example , automatise craw outgrowth measurements ) analyzed with outdated tools , leave its possible untapped . New data , new analysis : The fresh spot — combining bracing data with advanced analytics to deliver transformative brainstorm .
Why following the average is n’t enoughAccording to Adam , one of the big challenge cultivator front today is working with technology companies that are stuck in the " Old Data , New Analysis " quarter-circle . " We call this the commoditized quadrant . These companies commoditize AI , taking your datum , running generic algorithms , and deal it back to you . This attack forces cultivator to follow the average , making it harder to stand out and succeed long - terminus . "
" As AI becomes more common , it ’s not enough to just have it . Anyone can apply AI to temperature or humidness data — it ’s no longer groundbreaking . Without new type of datum , these system provide less value over time , leaving growers paying for solutions that only sustain the minimum operation . "
Supporting growers , not controlling themTo truly meet the motivation of the manufacture , Adam says AI providers must aim higher . " They should focus on usher in new types of data point ( like automated crop readjustment metric unit ) alongside new analytic techniques . provider should forefend hoarding information or dictate how it ’s used . Instead , they should endow growers with tools that allow them to introduce , excel , and rise above the norm . "
Why it matters"This is n’t just about technology — it ’s about how we approach emergence and collaboration " , he conclude . Solving the High and Hard Problem mean :
● Encouraging teamwork across the diligence . ● Avoiding " closed source " ecosystem that limit access to datum . ● Empowering growers with tools that wreak tangible , measurable improvements .
" To remain competitive in a speedily evolving industry , we must take on the High and Hard Problem principal - on . That signify going beyond AI that preserve affair modal and alternatively creating real note value with raw information and new analysis . Anything less is like selling sand in the desert . The hereafter of nursery foundation depend on set up and meet these expectation . "
Click here to learn more register for IUNU ’s coming webinar on AI data in the greenhouse space or contact them directly .
For more data : IUNU[email protected]iunu.com
© FloralDaily.com/Arlette Sijmonsma
