Classification of fruit by color
Why color is a key criterion in classification
Color represents one of the most immediate and significant parameters in fruit classification. It is the first element perceived by the consumer and, consequently, one of the main drivers of purchase. In the fruit and vegetable supply chain, selection by color not only meets aesthetic requirements: it defines ripeness, lot homogeneity, and compliance with commercial standards imposed by distribution. A uniformly colored batch of fruit communicates freshness and quality, while excessive color differences can compromise visual consistency and product positioning.
Classifying fruit by color is thus a practice that combines science and perception. Each variety has a reference color curve that defines its optimal harvest and sales stage. Theevolution of coloration, for example, from green to yellow in bananas or from green to red in Fuji apples, is directly proportional to ripeness and affects texture, sugars, and commercial value. The most advanced companies now use multispectral systems such as Rolvy to analyze coloration in real time, ensuring uniformity even in large batch sizes.
In addition to visual perception, color has commodity and regulatory significance. European regulations define quality standards that also include hue and color uniformity as distinctive criteria for classification into Extra, I and II classes. This means that a color defect (even if it does not compromise edibility) can result in downgrading of the product and affect the price per kilo. Understanding and measuring color, then, is both a technical and commercial act.
What color parameters are used in the selection
Fruit selection by color is based on objective parameters that go beyond simple visual assessment. Modern grading lines use sensors that break down the light reflected from the fruit into different spectral bands, measuring hue (Hue), saturation (Chroma) and brightness (Value) values with precision. These parameters are then compared with the reference values for the variety, allowing the determination of whether or not the batch is compliant.
Technically speaking, hue expresses the predominant color (e.g., green, yellow, red), saturation indicates color intensity, and brightness measures the degree of brilliance. The interaction of these three elements determines the “color signature” of the fruit. In apples, for example, the percentage of red cover to total area is assessed; in citrus, the uniformity of the peel and brightness; in kiwis, the dark green to light green turning, a signal of the degree of ripeness. Color classification integrates these factors to obtain a comprehensive profile that is useful to manufacturers and distributors alike.
| Parameter | Description | Application example | Business impact |
|---|---|---|---|
| Hue (Hue) | Dominant color of the peel | Red, green or yellow for apples | Determines ripeness class and perception of freshness |
| Saturation (Chroma) | Color intensity and purity | Oranges more or less vibrant depending on variety | It affects premium positioning and shelf choice |
| Brightness (Value) | Amount of light reflected from the surface | Brilliance of lemons or luster of pears | Increases visual appeal and perception of cleanliness |
These parameters are now analyzed with multispectral vision devices and high-resolution cameras, such as those installed on Logika‘s lines. Each fruit is scanned on multiple light bands, including infrared light, to identify imperfections, shadow areas, or pigment changes. In this way, selection by color becomes an integrated quality control to combine aesthetics, maturation, and regulatory compliance in one process.
How fruit are classified by color in the supply chain
Color grading in the fruit and vegetable supply chain follows defined protocols based on species, variety, and commercial use. Each company establishes reference color bands that correspond to maturity levels and market standards. For example, in red apples, percentages of pigmented coverage are assessed (less than 50%, between 50% and 75%, over 75%), while in citrus, the ratio of residual green to full orange is monitored. This classification determines not only the quality class but also the final destination of the batch: fresh consumption, processing or export.
| Species | Color range | Commercial class | Destination |
|---|---|---|---|
| Fuji apples | Red ≥75% of the surface area | Extra | GDO and premium export |
| Navel oranges | Green-orange to deep orange | I | National distribution |
| Williams pears | Light green to golden yellow | I / II | Local markets and processing |
Color classification, in this sense, is a shared language between manufacturers and distributors. Each color band corresponds to a precise level of maturity and, therefore, to a specific sensory expectation. A deep red in apples communicates sweetness and developed aroma; a saturated orange in citrus signals juiciness; a bright green in kiwis indicates freshness and texture. These signals, if handled consistently, provide perceptual continuity to the consumer and reduce trade disputes along the supply chain.
Critical issues and trends in color classification
Although grading fruit by color is now an established practice, it still has some critical issues related to natural variability and visual perception. Indeed, fruit color is not static: it varies with sun exposure, nutrition, ripeness and storage conditions. This variability can generate discrepancies between the actual appearance and the reference values set in automated systems. In the past, color selection was based on human judgment, but subjectivity resulted in significant differences between operators and production shifts. Instead, with the introduction of multispectral vision and artificial intelligence, color assessment has become an objective, traceable and replicable process.
Another critical issue concerns the management of so-called “out-of-tone,” that is, fruits that show partial differences in color but retain excellent quality characteristics. These products are often excluded from the main sales classes even though they are perfectly fit for consumption. There has been a revaluation trend in recent years, in line with the “ugly fruit” movement, which promotes the marketing of fruit that is not perfectly colored to reduce food waste and improve the economic sustainability of the supply chain. Technology today makes it possible to manage these lots intelligently, allocating them to secondary lanes or local markets without compromising their perceived quality.
| Type of defect | Main cause | Visual effect | Recommended management |
|---|---|---|---|
| Uneven coloring | Irregular sun exposure | Lighter or greenish areas | Automatic selection by tone bands and channeling to secondary markets |
| Browning or alterations | Heat stress or post-harvest stress | Dark areas or loss of shine | IR sensor detection and processing targeting |
| Excessive coloring | Excessive exposure or over-exposure | Red or yellow too intense | Redirection to domestic market or industrial use |
The latest trend is to integrate color data into traceability systems so that each batch can also be monitored by visual parameters. This allows for improved communication along the supply chain, providing transparency between manufacturer, packer and distributor. Cloud- and IoT-based platforms also allow optical sensor readings to be linked to agronomic information, creating predictive models that anticipate color changes in the field and optimize harvest times.
Futura solutions for selection and color classification
Futura’s technological research has focused on automating visual classification for years. Rolvy vision systems use multispectral cameras that scan the surface of fruit in eight light bands, from visible to infrared. This ability enables the identification of color shades invisible to the human eye, detecting differences in pigmentation, ripening variations, and surface defects. Artificial-intelligence-based software compares the collected data with reference models, classifying fruits according to hue and color uniformity in real time.
In parallel, the Logika line grading machines integrate high-precision optical modules that combine electronic weighing with color detection. This approach allows each fruit to be associated with a complete category of data: size, weight and color are recorded simultaneously, generating total visual traceability. The software interface allows specific color profiles to be defined for each customer or market, adapting the selection to the needs of modern distribution. These solutions reduce subjectivity, increase batch consistency and improve the final visual appearance of the packaged product.
| System | Sensing technology | Main function | Operational benefit |
|---|---|---|---|
| Rolvy | Multispectral cameras with AI | Comprehensive visual and color analysis | Classification by color and surface defects in real time |
| Logika | Integrated optical sensors with dynamic weighing | Color selection combined with caliber and weight | Visual homogeneity and total lot traceability |
Toward intelligent color classification
The grading of fruit by color now represents a meeting point between technology and sensory perception. The ability to measure and standardize a subjective parameter such as color allows it to be transformed into an objective index of quality and commercial value. Multispectral technologies, predictive analytics software, and integrated vision systems are making color selection increasingly accurate and sustainable, reducing waste and improving overall supply chain profitability.
Looking forward, batch color management will become a strategic component of fruit and vegetable supply chain 4.0. Data collected in real time will be able to feed predictive models that indicate the optimal harvest time, the most suitable commercial destination, and shelf life prediction. The challenge will be to integrate these systems across the board, creating a value chain based on visual consistency and color traceability. Color, from a simple aesthetic feature, is emerging as a new unit of value measurement in contemporary fruit.