Fruit Sorting Machinery Market Size, AI Grading, Optical Sorting & Forecast 2035

Fruit Sorting Machinery Market Size & Forecast 2035

The global fruit sorting machinery market was valued at US$354.17 million in 2025 and is projected to reach US$576.91 million by 2035, growing at a CAGR of 5.0% during 2026-2035. Automation of packhouses, higher fresh-fruit quality standards, labor constraints, and advances in camera-based grading, artificial intelligence, and non-destructive internal-quality inspection are supporting equipment investment.

Fruit sorting machinery grades produce according to characteristics such as size, weight, color, shape, maturity, and visible or internal defects. The DataM Intelligence market structure covers fresh, frozen, whole and processed fruit; manual and automatic feeding; major fruit categories including blueberries, cherries, peaches and pears; and processing and packing applications. Automatic systems hold the leading position because they improve consistency and reduce dependence on subjective manual grading.

The most important technology change is the movement from simple dimensional sorting toward value-based fruit classification. Current systems can distinguish blemish severity, detect internal browning or watercore, measure Brix and dry matter, identify soft or dehydrated berries, and route each piece of fruit into a pack specification matched to its quality. TOMRA, UNITEC, Ellips and MAF RODA now combine optical imaging, AI and internal sensing across multiple fresh-fruit categories.

Market Highlights

• 2025 Market Size: US$354.17 Million
• 2035 Market Size: US$576.91 Million
• CAGR, 2026-2035: 5.0%
• Largest Region: North America
• North America Share: 38.75% in 2025
• Fastest-Growing Region: Asia-Pacific
• Leading Fruit Type: Fresh Fruit
• Leading Feeding System: Automatic Feeding
• Leading Application: Fruit Processing
• Core Technologies: Optical cameras, machine vision, deep learning, NIR spectroscopy, weight grading and size grading
• High-Value Applications: Apples, citrus, blueberries, cherries, kiwifruit, avocados and stone fruit
• Key Operating Metrics: Pack-out yield, defect detection, false rejection, throughput, gentle handling and grade consistency.

Fruit Sorting Is Shifting From Grading Produce to Recovering Product Value

Conventional fruit grading separated produce by weight, diameter and color. Those functions remain essential, but advanced sorting lines now make more detailed decisions about what each fruit is worth and which market it should enter.

A minor cosmetic mark may still be acceptable in a standard retail grade. The same defect at higher severity may send the fruit into a lower grade or processing stream. Internal quality can determine whether fruit should be exported, sold locally, ripened earlier or diverted from premium packs.

TOMRA's LUCAi platform illustrates this transition. Its deep-learning models classify apples, blueberries, cherries, citrus, stone fruit and kiwifruit, while its Severity Score assigns defects a 0-100 rating so pack specifications can distinguish between minor and severe blemishes. TOMRA reports more than 3,500 LUCAi lanes sold globally as of 2025 and processing capability of up to 40,000 images per second.

Ellips uses a similar value-recovery model. Its apple systems combine 360-degree external inspection with internal-quality analysis for defects including internal browning and watercore, while production data can be connected with dashboards, ERP systems and grower traceability.

The commercial metric is increasingly pack-out accuracy rather than the number of fruits that can simply pass through a grader.

Key Takeaways

• The fruit sorting machinery market is projected to grow from US$354.17 million in 2025 to US$576.91 million by 2035, with automation and higher quality-control requirements supporting a 5.0% CAGR.
• North America held 38.75% of global revenue in 2025, supported by high labor costs, large fruit-processing operations and adoption of automated grading. Asia-Pacific records the fastest regional growth.
• Automatic feeding and sorting remain the dominant operating model. DataM Intelligence identifies automation as the leading feeding segment because of its consistency, accuracy and ability to reduce manual grading requirements.
• AI is moving from defect detection into grade optimization. TOMRA's LUCAi grades defect severity, while Ellips True-AI and UNITEC's Vision 4.0 AI platforms classify challenging external defects across apples, berries, citrus, cherries and other fruits.
• Internal-quality inspection is becoming a premium machinery layer. TOMRA Inspectra² uses near-infrared spectroscopy to assess Brix, dry matter and hidden defects, while MAF RODA, Ellips and UNITEC offer their own non-destructive internal analysis systems.
• Berries require a different automation architecture from apples and citrus. Blueberry sorting increasingly combines optical grading with softness, dehydration, bloom protection and extremely gentle transfers because mechanical damage can eliminate the value gained from higher sorting speed.
• Competition is concentrated among specialist fruit-technology companies. A current optical fruit-sorting benchmark places UNITEC, TOMRA, Bühler, Ellips and Aweta at about 60% of the optical segment, with UNITEC alone at about 28%. 

Automatic Sorting Holds the Leading Position

Automatic feeding and grading systems dominate the current DataM Intelligence segmentation because they can maintain more consistent quality at higher throughput than manual sorting.

Modern automated lines integrate several operations:

infeed, singulation, weighing, imaging, internal-quality analysis, electronic grading, discharge, filling, packing and traceability.

The automation advantage becomes larger when a packhouse handles millions of individual fruits during a short harvest season. Manual graders can become inconsistent as shift duration, lighting conditions, fruit variety and defect complexity change.

AI reduces another source of variation: operator interpretation. A trained model can apply the same defect and grade criteria across every shift while still allowing grading thresholds to be changed when export specifications or retailer requirements differ.

Ellips reports more than 3,500 grading systems in use worldwide across 19 fruit and vegetable categories, while its AI grading architecture links defect detection with weight, size, color, internal quality and traceability.

Optical Sorting Is Becoming the Core Technology Platform

Optical systems inspect fruit using cameras, controlled illumination and image-processing software.

Traditional machine vision measures:

color, diameter, shape, external blemishes and surface defects.

Deep learning makes classification more sophisticated because models can evaluate patterns across the entire fruit rather than relying only on predefined color or pixel thresholds.

TOMRA's Spectrim with LUCAi supports apples, avocados, citrus, kiwifruit and stone fruit. Its InVision² architecture applies deep learning to cherries, while specialized platforms serve blueberries.

UNITEC takes a commodity-specific approach. Its current portfolio includes dedicated Vision technologies for blueberries, cherries, apricots and numerous other fruit categories. Cherry Vision 4.0 AI analyzes the entire external surface, while UNIQ CHERRY evaluates internal quality.

MAF RODA's current sorting range includes Globalscan 7, Cherryscan G7, Berryscan G7 and multiple internal-quality systems. The company states that AI is increasingly used to improve external and internal defect classification.

This technology direction favors software-rich sorting platforms that can be improved after installation through new models, defect libraries and classification recipes.

AI Is Changing How Packhouses Define a Grade

Traditional optical sorting identifies whether a defect exists.

AI-based grading increasingly determines what the defect means commercially.

TOMRA's Severity Score differentiates defect intensity on a 0-100 scale. A lightly scarred citrus fruit can remain in a higher-value grade, while a more severe version of the same defect is routed elsewhere. This reduces the risk that acceptable fruit is unnecessarily downgraded.

The same principle appears in Ellips True-AI. Its systems evaluate surface defects across the full fruit and combine classifications with predefined packing criteria. Ellips states that its apple grading can detect defects down to 0.2 mm and uses internal analysis to identify conditions not visible externally.

AI grading also becomes more valuable when natural variation is high. Fruit appearance changes with cultivar, orchard, climate, maturity and season. Static machine-vision rules can require repeated manual adjustment, while trained deep-learning models can handle more complex visual variation.

Internal Quality Inspection Is the Next High-Value Layer

A fruit can look acceptable externally and still contain internal defects or fail eating-quality requirements.

That creates a separate machinery segment around non-destructive internal analysis.

TOMRA Inspectra² uses near-infrared spectroscopy to assess attributes including Brix, dry matter and internal defects in apples, avocados, citrus and kiwifruit. The resulting measurements can support premium grading and supply-chain decisions without cutting samples open.

MAF RODA combines external Globalscan 7 inspection with Insight internal analysis. Its internal system is designed to evaluate fruit quality without destructive testing and can be incorporated into wider electronic grading lines.

Ellips uses light-transmittance technology to scan inside apples, including up to 20 internal scans per fruit, and identifies conditions including internal browning and watercore.

UNITEC's UNIQ systems perform internal analysis across fruit categories including blueberries, cherries and apricots.

Internal sensing increases equipment value because it can change destination decisions rather than merely improve cosmetic consistency.

Fresh Fruit Holds the Largest Market Share

Fresh fruit represented the largest fruit-type segment in 2025. Premium retail and export channels require consistent size, appearance and maturity, while defects that escape grading can generate claims or reduce shelf acceptance.

Fresh-fruit machinery also has demanding handling requirements.

The product cannot simply be inspected accurately; it must arrive at the end of the line without new bruises, punctures, bloom loss or compression damage caused by the equipment itself.

Apples and citrus tolerate different handling from cherries or blueberries. The result is a market in which commodity-specific infeed, carrier, transfer and discharge systems matter almost as much as the imaging technology.

Frozen and processed fruit use more belt and free-fall optical sorting architectures because the material is already detached, cut or frozen. Bühler's SORTEX F PolarVision processes frozen fruit and vegetables at throughputs up to 20 tonnes per hour while removing color defects and foreign material.

Apples Require External, Internal and Weight Grading on One Line

Apple packhouses represent one of the most mature markets for electronic grading.

The fruit can be sorted according to:

weight, diameter, color, shape, sunburn, russet, bruising, punctures, stem-bowl defects, bitter pit, and internal conditions.

Ellips combines 360-degree external imaging with internal-quality analysis and individual weighing in its current apple systems. Its platform also links pack information back to grower and field records through traceability software.

TOMRA combines Spectrim/LUCAi external grading with Inspectra² internal sensing. The latter can identify internal defects and quality attributes that cannot be reliably evaluated using external cameras alone.

The equipment trend is toward one digital fruit record containing both appearance and internal quality rather than separate grading decisions.

Citrus Sorting Is Moving Toward Defect Severity and Internal Sweetness

Citrus grading needs to identify external issues such as rot, sunburn, cuts, long stems, and surface damage while managing substantial natural color and shape variation.

TOMRA currently applies LUCAi to more than 20 common citrus defect classes and reports detection above 99% for selected defects including rot, sunburn, clipper cut, RBD, long stem and doubles.

Ellips uses high-resolution imaging across the complete citrus surface and combines it with internal-quality sensing for oranges, lemons and limes.

Throughput can be substantial. Agrofrut Hellas deployed an Elisam citrus grader powered by Ellips True-AI and runs at more than 40 tonnes per hour, replacing a more labor-dependent quality-control model with automated inspection.

Internal measurements such as Brix create an additional opportunity to separate premium eating-quality fruit from product suitable for other markets.

Blueberry Sorting Is Driving Innovation in Gentle Automation

Blueberries present one of the hardest mechanical problems in fruit sorting.

The machinery must inspect very small fruit at high speed while protecting bloom, avoiding compression, and identifying defects such as softness, dehydration, splits, and internal breakdown.

TOMRA's current 5S Blueberry with Spectrim and LUCAi combines optical grading with dehydration detection, controlled transfers, low-impact belts and automated feedback. The design specifically targets bloom retention and lower recirculation.

Ellips' Elifab platform grades blueberries according to defects and attributes including ripeness, slipskin, bruising, compression damage, shrivel, softness, bloom and Brix/acidity.

UNITEC's Blueberry Vision 4.0 AI evaluates external quality, while UNIQ BLUEBERRY adds internal analysis.

The blueberry market demonstrates why fruit sorting machinery increasingly consists of inspection technology plus mechanical handling engineering rather than cameras alone.

Cherry Sorting Requires Full-Surface Inspection at Very High Piece Counts

Cherries combine small size with high unit counts and strict cosmetic requirements.

Common defects include cracks, stem issues, softness, bruising, pitting and shape abnormalities. Manual inspection becomes difficult because a large packhouse may process enormous numbers of individual cherries during a compressed harvest period.

UNITEC's Cherry Vision portfolio uses high-resolution imaging to inspect the entire fruit surface and classify external quality, while UNIQ CHERRY provides internal analysis.

TOMRA's InVision² with LUCAi applies deep learning specifically to cherries and is designed to maintain grading consistency as crop conditions change.

This category favors equipment with high pieces-per-second capacity, accurate singulation and extremely rapid electronic decision-making.

Stone Fruit Needs Accuracy Without Creating New Bruising

Peaches, nectarines, plums and apricots can be damaged easily during transfers.

Sorting lines therefore need to balance imaging coverage with low drop heights and controlled handling.

UNITEC's APRICOT VISION 4.0 AI and UNIQ APRICOT evaluate external and internal quality, while the same line architecture can be adapted to similar fruits including plums, peaches and nectarines.

TOMRA's LUCAi is also available for stone fruit and is trained to identify defects including rot, scars, and split-pit-related conditions.

The machinery value proposition depends on increasing premium pack-out without generating mechanical damage during inspection.

Traceability Is Becoming Part of Sorting Machinery

Every fruit passing through an electronic grader creates data.

The machinery can record:

grower, orchard block, incoming lot, size distribution, defect rate, internal-quality distribution, pack-out, waste stream and final package destination.

Ellips integrates grading information with dashboards and ERP systems and supports traceability from package back to grower and field.

MAF RODA similarly integrates sorting with traceability software and real-time process-cost information across calibrators, automated systems, AGVs and palletizing.

This information turns the grader into an orchard feedback system. High bruising from one block, lower Brix in one lot or rising defect frequency in one variety can be traced upstream rather than being discovered only through downstream customer complaints.

North America Holds 38.75% of Global Revenue

North America accounted for 38.75% of fruit sorting machinery revenue in 2025, making it the largest regional market in the current benchmark. High labor costs, large fruit-processing operations and established automation infrastructure support adoption.

The United States has major apple, citrus, cherry, blueberry and stone-fruit industries, creating demand for both large multilane packhouse systems and specialized berry equipment.

AI-based grading is already well established in commercial installations. Ellips lists apple operations in the United States using True-AI for defects including stem-bowl cracks, while TOMRA operates across U.S. fruit packhouses through its fresh-produce sorting portfolio.

Replacement demand also supports regional revenue. Older electronic graders can be upgraded with new cameras, software, AI models and internal-quality systems without rebuilding every mechanical section of the packing line.

Asia-Pacific Is the Fastest-Growing Region

Asia-Pacific records the strongest growth in the current fruit sorting machinery outlook, supported by modernization of packhouses and rising automation across China, India, Japan and other regional fruit-producing markets.

Optical sorting has an especially strong regional manufacturing and installation base. A separate 2026 optical-fruit-sorting benchmark places Asia-Pacific at 51% of the optical machinery segment, showing the scale of advanced sorting activity when the market is narrowed specifically to optical systems.

Japan is becoming a visible market for advanced berry automation. TOMRA's integrated KATO260, LUCAi and CURO blueberry line won the FOOMA Japan 2025 Jury Prize, and the company stated that it planned to expand its fresh-produce sorting portfolio in the Japanese market.

China's large apple, citrus and other fruit industries create strong volume potential, while expanding export-oriented production across Asia increases the requirement for more consistent grading.

Europe Has a Dense Base of Fruit Sorting Technology Companies

Europe combines advanced fruit production with a large concentration of sorting-equipment manufacturers.

UNITEC is headquartered in Italy, MAF RODA in Europe, Ellips in the Netherlands, Bühler in Switzerland and Aweta in the Netherlands. This supplier density supports rapid adoption of new imaging, software and mechanical handling technologies.

European packhouses increasingly deploy high-speed AI inspection for citrus, apples, berries and stone fruit. Agrofrut Hellas in Greece runs AI citrus grading above 40 tonnes per hour, while Ellips has installations across major European fruit categories.

Bühler addresses a complementary part of the market through frozen and processed-fruit optical sorting. Its SORTEX F platform can remove defects and foreign materials from frozen fruit at up to 20 tonnes per hour.

Latin America Is Important for Export Fruit Automation

Latin America is strategically important because Chile, Peru, Mexico and other countries export large volumes of berries, cherries, grapes, citrus, avocados and tropical fruit over long supply chains.

Export fruit needs particularly consistent grading because transit times are longer and customer specifications can differ between destination markets.

UNITEC's early Blueberry Vision deployments in Chile demonstrated the value of electronic internal and external quality grading where berries need to withstand export distribution. Its current systems have evolved into Blueberry Vision 4.0 AI and UNIQ BLUEBERRY.

The region's growth opportunity is strongest in export packhouses where labor reduction and higher premium-grade recovery can justify automated optical investment.

Competitive Landscape

UNITEC

UNITEC has one of the broadest commodity-specific fruit sorting portfolios.

The company provides processing, grading, and packing technology for more than 45 types of fruits and vegetables with more than 20 dedicated sorting systems.

Current systems include Blueberry Vision 4.0 AI, Cherry Vision 4.0 AI, Apricot Vision 4.0 AI, and UNIQ technologies for internal-quality detection. UNITEC emphasizes complete line integration from feeding and grading through packing and traceability.

A current optical-fruit-sorting benchmark places UNITEC at about 28% of the optical segment, making it the largest individual supplier in that dataset.

TOMRA Food

TOMRA combines fruit sorting hardware with machine vision, AI, NIR, and packing systems.

Its fresh-fruit portfolio covers apples, avocados, blueberries, cherries, citrus, kiwifruit, stone fruit and other produce. Spectrim with LUCAi performs deep-learning external grading, while Inspectra² provides non-destructive internal analysis.

LUCAi had more than 3,500 lanes installed globally as of 2025. Its current model includes severity-based grading rather than binary defect recognition alone.

Ellips / Elisam / Elifab

Ellips specializes in grading software and AI, with Elisam and Elifab providing associated mechanical machinery.

The company reports more than 3,500 grading systems operating worldwide, coverage of 19 fruit and vegetable types and more than 350 Elisam and Elifab machines.

True-AI performs external defect classification, while TrueSort internal-quality technology detects characteristics invisible to conventional cameras. The platform is particularly active across apples, citrus and blueberries.

MAF RODA Agrobotic

MAF RODA provides complete post-harvest automation spanning sorting, packing, palletizing and traceability.

Its electronic sorting range includes Globalscan 7, Cherryscan G7, Berryscan G7, IDD 8 and Insight for internal quality. The company integrates AI, spectrometry and advanced sensors into its grading architecture.

Its competitive position is strongest where a packing facility needs one automation provider across multiple processing stages.

Bühler

Bühler's SORTEX platform has a strong position in processed, frozen and dried fruit applications.

The SORTEX F PolarVision can process frozen fruit and vegetables at up to 20 tonnes per hour and uses optical detection to remove defects and foreign material.

Bühler is also extending deep learning across its broader optical-sorting platform. The company launched SORTEX AI700 in May 2025, demonstrating its wider investment in AI-based defect classification across food processing.

Aweta

Aweta remains an established name in electronic grading and packing systems for fresh produce and is included among the five largest companies in current optical-fruit-sorting market analysis.

Its installed-base position in fresh-produce grading makes it a relevant competitor across apple, citrus and other packhouse applications.

Recent Developments Reshaping Fruit Sorting Machinery

2026 - TOMRA Introduces 5S Blueberry with Spectrim and LUCAi

TOMRA's current 5S Blueberry platform combines deep-learning optical grading, dehydration detection, low-impact mechanical handling and dynamic batch feedback.

2026 - AI Grading Expands Across More Fruit Categories

TOMRA's current LUCAi portfolio now covers apples, blueberries, cherries, citrus, stone fruit and kiwifruit and uses a continually updated defect-image library.

August 2025 - TOMRA Adds Defect Severity Scoring

LUCAi Severity Score introduced a 0-100 scale for selected blemishes, allowing packhouses to distinguish defect severity when defining grade thresholds.

June 2025 - TOMRA Blueberry System Wins FOOMA Japan Award

The KATO260 with LUCAi and CURO integrated blueberry line received the FOOMA Japan 2025 Jury Prize, supporting TOMRA's expansion in advanced fresh-produce automation in Japan.

May 2025 - Ellips Demonstrates 40+ Tonne/Hour AI Citrus Grading

Agrofrut Hellas deployed an Ellips True-AI-powered Elisam grader operating above 40 tonnes per hour across citrus varieties.

May 2025 - Bühler Launches Deep-Learning SORTEX AI700

Bühler introduced its first SORTEX AI700 application in 2025, reinforcing the wider movement toward deep-learning optical classification across food sorting.

Market Growth Drivers

Packhouse Automation

Automatic sorting reduces reliance on large seasonal grading teams and applies the same quality thresholds across shifts. DataM Intelligence identifies automatic feeding as the dominant segment.

Higher Quality Specifications

Fresh-fruit retailers and export channels increasingly distinguish fruit according to detailed appearance, maturity and internal-quality specifications. More accurate grading allows multiple value tiers to be recovered from one incoming lot.

Artificial Intelligence

Deep-learning systems can classify difficult defects that traditional rule-based machine vision struggles to separate from normal fruit variation. TOMRA, Ellips and UNITEC have all commercialized AI grading across multiple fruit categories.

Internal Quality Inspection

NIR, transmittance and related sensing technologies allow non-destructive measurement of Brix, dry matter and hidden defects. This expands machinery revenue beyond external optical inspection.

Food-Waste Reduction

Higher grading precision reduces the amount of acceptable fruit rejected into lower-value or waste streams. TOMRA specifically positions internal grading around recovering usable produce and routing maturity levels more effectively through the supply chain.

Market Restraints

High Capital Cost

Advanced multi-lane systems combine mechanical handling, cameras, lighting, servers, AI software, NIR sensors, electronic discharge equipment, and packing automation. Capital requirements can delay adoption among smaller operations with limited seasonal throughput.

Seasonal Utilization

Many fruit packhouses operate around concentrated harvest windows. Machinery economics become stronger where one line can process multiple varieties or commodities across a longer season.

UNITEC, for example, designs selected apricot lines to handle related fruits including plums, peaches, nectarines and kiwifruit.

Fruit-to-Fruit Variation

One grading model cannot simply be applied across every commodity. Apples, citrus, cherries and blueberries differ in shape, surface properties, internal defects and handling sensitivity.

This keeps commodity-specific engineering and model training important.

False Rejection

Excessively aggressive grading can remove acceptable fruit from premium packs. AI severity scoring and adjustable classification thresholds are increasingly used to reduce unnecessary downgrading.

Gentle Handling Requirements

Greater speed can create more product damage if transfers are poorly engineered. Blueberry and stone-fruit lines require especially low-impact handling, making mechanical design a core performance constraint.

Fruit Sorting Machinery Market Scope