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Todd Odman, Owner and Lead Design Engineer“Pearl Sort is the leading technology for the agricultural industry, specializing in cherries and blueberries for now,” says Todd Odman, owner and lead design engineer.
Pearl Sort began developing automation systems decades ago after identifying rising labor costs and inefficiencies inside agricultural packhouses. It initially focused on automated sorting systems before expanding into connected machinery and AI-driven quality analysis.
Its AI platform, A.D.A.M. (AI Developed for Agricultural Machinery), was introduced after six years of machine learning development using millions of labeled fruit images. The system analyzes cherries and blueberries for defects including mildew, rain cracks, glossiness, stem cracks and wind damage without requiring highly specialized human operators.
Pearl Sort also developed its patented 360-degree roller technology allowing each berry or cherry to rotate fully beneath the camera system. That structure gives A.D.A.M. visibility from every angle during analysis and sorting, improving classification accuracy across export, local market, frozen and cull categories.
It reported measurable improvements after implementing A.D.A.M. within its sorting systems. Internal testing showed a 90 percent increase in correctly classified local-grade fruit and a 94 percent correct classification rate for rot and mold detection. Pearl Sort also reduced rot and mold entering export shipments by 83 percent.
Turning Agricultural Data into Actionable Insight
Many growers and packhouses struggle with crop monitoring, yield optimization, labor shortages and understanding how environmental conditions affect harvest quality. Pearl Sort structured provides immediate feedback as fruit moves through the sorting process.
Its Red Pearl cherry sorter captures multiple images of every fruit using the rotating conveyor system and transmits that information directly to A.D.A.M. for classification. The AI system categorizes fruit quality in real time while identifying environmental issues affecting orchard performance.
During a recent season in New Zealand, Pearl Sort detected wind burn and rain cracking patterns concentrated on cherries from orchard rows closest to a valley. Instead of discarding the damaged fruit entirely, the system separated sellable local-market cherries from unusable culls, helping growers recover additional value from the harvest.
It believes AI-generated sorting data gives growers stronger visibility into how thinning programs, spray applications and growing practices influence fruit size and quality. The technology eliminates close to 80 percent of traditional hand-sorting labor inside packhouses.
Expanding AI across Agricultural Automation
One customer in New Zealand demonstrated how the technology could support growers during difficult harvest conditions. Severe wind and rain damage affected nearly half of the orchard’s cherry crop, placing the economic viability of harvesting the fruit in question.
The grower used the Red Pearl sorter with A.D.A.M. to separate export-grade fruit, local-market cherries and culls with greater precision. Other growers later brought fruit from their own orchards to the same facility after seeing the system’s ability to recover marketable product from damaged harvests.
Pearl Sort reported that the packhouse maintained normal processing speeds despite poor crop conditions while reducing labor requirements to a single operator and one worker per pack table. The AI system also differentiated between dry cracks and more severe wet cracking, improving local-market sorting decisions.
Looking ahead, Pearl Sort expects AI to expand into predictive analytics, autonomous harvesting, production monitoring and broader packhouse automation. It also sees opportunities for AI systems to support sustainable farming through environmental monitoring, crop selection and automated agricultural management.
Pearl Sort plans to continue developing AI-centered agricultural machinery while expanding into additional crop categories and international markets.
AI Agricultural Solutions Info
Q1
What Are AI Agricultural Solutions, And What Do They Cover?
AI Agricultural Solutions use artificial intelligence, machine vision and automated equipment to assess agricultural products and support sorting or production decisions. In practice, AI Agricultural Solutions can help identify defects, classify produce by quality and provide real-time information as crops move through processing. The category can also connect sorting data with broader agricultural automation and analytics
Q2
How Does Pearl Sort Apply AI Agricultural Solutions To Fruit Sorting?
Pearl Sort applies AI Agricultural Solutions through A.D.A.M. (AI Developed for Agricultural Machinery), an AI platform developed through six years of machine learning work using millions of labeled fruit images. The system currently analyzes cherries and blueberries for issues such as mildew, rain cracks, glossiness, stem cracks and wind damage. Its Red Pearl cherry sorter captures multiple images of each fruit and sends the information to A.D.A.M. for real-time classification.
Q3
What Problems Can AI Agricultural Solutions Help Packhouses Address?
AI Agricultural Solutions can address labor pressure, inconsistent manual sorting, crop-quality variation and limited visibility into conditions affecting harvested fruit. By classifying produce as it moves through a sorting line, these systems can help packhouses separate export-grade, local-market, frozen and cull categories. They can also provide data that helps growers understand how environmental conditions and growing practices affect fruit quality.
Q4
Why Does Machine Vision Matter In AI Agricultural Solutions?
Machine vision gives AI Agricultural Solutions a way to assess produce from visual information rather than relying only on manual inspection. Pearl Sort's patented 360-degree roller technology rotates each berry or cherry fully beneath the camera system, giving A.D.A.M. views from every angle. This approach supports more detailed classification, including distinctions between dry cracks and more severe wet cracking, while reducing reliance on highly specialized human operators.
Q5
How Can AI Agricultural Solutions Turn Sorting Data Into Agricultural Insight?
AI Agricultural Solutions can do more than separate good fruit from damaged fruit when sorting systems capture and organize information in real time. Pearl Sort's system identified patterns of wind burn and rain cracking in cherries from orchard rows near a valley in New Zealand. The sorter separated sellable local-market fruit from culls, while the resulting data provided insight into how environmental conditions, thinning programs, spray applications and other growing practices may affect size and quality.
Q6
What Should Buyers Look For When Evaluating Agricultural AI Systems?
Useful AI Agricultural Solutions should combine accurate classification with practical integration into existing agricultural operations. Buyers can also consider whether the system supports real-time decisions, handles different quality categories and produces data that can inform later operational choices. Factors include the range of defects the system can identify, how consistently it processes produce, the quality of data it generates and how much manual labor it can reduce. Pearl Sort reports a 94 percent correct classification rate for rot and mold detection, an 83 percent reduction in rot and mold entering export shipments and close to 80 percent less traditional hand-sorting labor. Its development roadmap also points toward predictive analytics, autonomous harvesting and broader packhouse automation.
Company
Pearl Sort
Management
Todd Odman, Owner and Lead Design Engineer
Description
Pearl Sort develops AI-powered agricultural sorting systems for cherry and blueberry packhouses. The company combines machine vision technology, automated sorting equipment and predictive agricultural analytics to help growers improve quality classification, reduce labor dependency and increase processing efficiency across agricultural operations.
