AI apple grading: TOMRA vs GREEFA vs Aweta

Choosing apple quality grading for a packhouse

Apple grading determines which fruit enters each quality category after harvest. It is a separate decision from a robot picking apples off trees. TOMRA Spectrim with LUCAi, GREEFA iQS Pro PLUS and Aweta UltraVision apply AI to external quality, but their integration and upgrade propositions differ.

TOMRA Spectrim with LUCAi

TOMRA Spectrim with LUCAi combines an optical grading platform with deep-learning models for apples. Multi-wavelength images supply information about fruit surfaces, while LUCAi considers the fruit in context rather than interpreting every small image region independently. The apple-specific explanation emphasizes difficult stem-area distinctions, where natural texture can resemble a defect. For a packhouse, this is useful because a classification decision needs to separate commercially relevant damage from harmless features without forcing the operator to manage every image detail. Operators retain control over grading tolerances and the resulting product categories. Spectrim with LUCAi is a natural option for a new TOMRA grading project or a compatible system upgrade. It should not be confused with every other sensor in TOMRA's apple portfolio: internal-quality inspection is associated with separate Inspectra technology. A buyer wanting both external and internal information needs those elements specified individually.

GREEFA iQS Pro PLUS

GREEFA iQS Pro PLUS is a deep-learning external-quality system with a particularly clear upgrade proposition. GREEFA describes simpler settings, automatic learning functions and the ability to upgrade existing sorting systems to its Pro or PLUS software. That matters to a packhouse whose handling equipment still works well but whose quality-recognition layer is becoming the bottleneck. The product aims to make quality assessment more uniform while reducing the number of operator adjustments needed to maintain a grading program. Its modular design also supports expansion as requirements change. For new projects, iQS Pro PLUS belongs within a configured GREEFA grading solution rather than a standalone camera purchase. For existing users, the attractive software-upgrade message still requires a compatibility check against the installed cameras, computing and line version. GREEFA's internal-quality measuring system, iFA, is a separate product scope; external deep learning does not by itself establish internal disorder detection.

Aweta UltraVision

Aweta UltraVision connects hyperspectral imaging and four cameras with a three-dimensional representation of each apple. Deep learning assesses defect severity and assigns fruit to the operator's chosen quality categories. The current apple application describes defects including bruises, rot, pitting, sunburn and russet, making the classification relevant to commercial packout rather than simply measuring size or color. UltraVision also sits within Aweta's broader apple handling, grading and packing proposition. FlowMaster controls can connect quality settings with the operating line, while tracking and database interfaces support the information around a batch. This makes Aweta attractive when a packhouse wants the grading decision integrated into a wider process redesign. Internal assessment is offered through separate Inscan Pulse sensors, which use spectroscopy for parameters such as maturity and water core. UltraVision's external AI and Inscan Pulse's internal measurements are complementary scopes, not interchangeable names for one feature.

Understanding a defect versus assigning its grade

Spectrim with LUCAi emphasizes whole-fruit context, particularly for difficult apple stem features. iQS Pro PLUS emphasizes deep-learning recognition with simplified adjustment and learning functions. UltraVision links hyperspectral images and a 3D fruit representation with defect-severity classification. Each still needs a commercial definition of acceptable fruit. A stem mark tolerated in one sales category may be unacceptable in another, even if the camera sees it equally well. TOMRA, GREEFA and Aweta should therefore be compared on how their recognition and operator controls express the packhouse's actual grade distinctions, rather than on the word AI alone.

Upgrading a recognition layer or redesigning the line

GREEFA's public Pro/PLUS description gives existing-system software upgrades a prominent role. TOMRA offers LUCAi models within the Spectrim grading platform, making compatibility with an existing TOMRA installation an important part of its offer. Aweta presents UltraVision inside a tailored apple line with handling, software and packing components. These are different starting points for a project. A packhouse retaining good mechanical equipment may prioritize the first two upgrade discussions. A site changing infeed, sorting and packing together may gain more from Aweta's connected line proposition. Actual eligibility and hardware requirements remain installation-specific.

External quality, internal sensors and quotation scope

Spectrim with LUCAi, iQS Pro PLUS and UltraVision all address external apple quality in this comparison. TOMRA Inspectra, GREEFA iFA and Aweta Inscan Pulse represent separate internal-quality options. This separation matters when comparing quotes: an apparently richer offer may simply include another sensor category. No comparable public numeric price or billing interval was confirmed for the three configurations. Lane count, camera hardware, AI models, operator software and support define the supplied scope. Vendor claims about detection or packout gains do not establish matched performance on the same apple varieties and defect mixture.

Which grading system fits your packhouse?

Start with Spectrim and LUCAi for a TOMRA grading project, iQS Pro PLUS for a compatible GREEFA upgrade, and UltraVision for an integrated Aweta apple line. Keep internal inspection and broader packing equipment explicit in the selected configuration.