How Can Spectral Technology Non-Destructively Determine Fruit Ripeness?

想知道牛油果何時(shí)入口最佳，榴蓮是否熟透？水果成熟度檢測(cè)一直是農(nóng)業(yè)領(lǐng)域的重要課題，本篇將介紹光譜和高光譜成像技術(shù)如何為這一課題提供了創(chuàng)新解決方案，通過(guò)無(wú)損檢測(cè)實(shí)現(xiàn)精準(zhǔn)判斷。

水果成熟過(guò)程中，其內(nèi)部化學(xué)成分（如葉綠素、類胡蘿卜素、糖分、酸度等）會(huì)發(fā)生規(guī)律性變化，這些物質(zhì)對(duì)特定波長(zhǎng)的光具有吸收和反射特性。研究表明，可見(jiàn)光波段主要反映色素變化，而近紅外區(qū)域則與水分、糖分等內(nèi)部成分密切相關(guān)。

在實(shí)際應(yīng)用中，不同水果種類因其生理特性差異，需要采用特定的特征波長(zhǎng)和不同的算法模型。

Want to know when an avocado is at its peak or if a durian is perfectly ripe? Fruit ripeness detection has always been a critical topic in agriculture. This article explores how spectral and hyperspectral imaging technologies provide innovative solutions for this challenge, enabling precise judgment through non-destructive testing.

During fruit ripening, internal chemical components (such as chlorophyll, carotenoids, sugars, acidity, etc.) undergo regular changes. These substances exhibit unique absorption and reflection characteristics for specific wavelengths of light. Research shows that the visible light spectrum primarily reflects pigment changes, while the near-infrared region is closely related to internal components like moisture and sugar content.

In practical applications, different fruit types require specific characteristic wavelengths and distinct algorithm models due to variations in their physiological properties.

小果的成熟度分析 / How Can Spectral Technology Non-Destructively Determine Fruit Ripeness?

一項(xiàng)甜橙研究采用400-1000nm波段的可見(jiàn)/近紅外光譜，結(jié)合偏最小二乘法(PLS)，成功預(yù)測(cè)了可溶性固形物、可滴定酸和維生素C含量，為成熟期預(yù)測(cè)提供了量化依據(jù)。

香蕉成熟度檢測(cè)常利用高光譜成像技術(shù)在400-1000nm范圍內(nèi)采集數(shù)據(jù)。一個(gè)研究團(tuán)隊(duì)通過(guò)主成分分析(PCA)結(jié)合極限學(xué)習(xí)機(jī)(ELM)建立的模型，對(duì)可溶性固形物和硬度的預(yù)測(cè)相關(guān)系數(shù)R2分別達(dá)到0.92和0.94。

針對(duì)牛油果的研究發(fā)現(xiàn)，其成熟度判斷主要依賴于800nm以上的近紅外信息，而520~650nm的可見(jiàn)光范圍則有助于區(qū)分未成熟與成熟果實(shí)。研究人員開(kāi)發(fā)的高光譜卷積神經(jīng)網(wǎng)絡(luò)(HS-CNN)模型，在牛油果成熟度分類中準(zhǔn)確率超過(guò)90%。

A study on sweet oranges utilized visible/near-infrared spectroscopy in the 400–1000 nm range, combined with partial least squares (PLS), to successfully predict soluble solids, titratable acidity, and vitamin C content, providing a quantitative basis for ripening stage prediction.

For banana ripeness detection, hyperspectral imaging technology is often employed to collect data within the 400–1000 nm range. One research team developed a model using principal component analysis (PCA) combined with an extreme learning machine (ELM), achieving prediction correlation coefficients (R2) of 0.92 and 0.94 for soluble solids and firmness, respectively.

Research on avocados found that ripeness determination primarily relies on near-infrared information above 800 nm, while the visible light range of 520–650 nm helps distinguish unripe from ripe fruit. A hyperspectral convolutional neural network (HS-CNN) model developed by researchers achieved over 90% accuracy in avocado ripeness classification.

高光譜數(shù)據(jù)對(duì)牛油果的成熟度分類的決策影響：牛油果的空間維、光譜維圖像 / The impact of the input on the decision of the class for an avocado

皮厚且堅(jiān)硬的水果，如何檢測(cè)？ / How to Detect Ripeness in Thick-Skinned, Hard Fruits?

對(duì)于西瓜、哈密瓜、榴蓮等皮厚且堅(jiān)硬的水果，成熟度檢測(cè)面臨很大的挑戰(zhàn)。

一項(xiàng)西瓜研究使用了近紅外光譜（NIRS）技術(shù)，涉及908~1676nm和950~1650nm光譜范圍，檢測(cè)了249個(gè)完整西瓜（152個(gè)淺綠條紋果皮，97個(gè)深綠純色果皮）。利用偏最小二乘判別分析（PLS-DA），構(gòu)建可溶性固形物含量（SSC）的定量模型。結(jié)果顯示，淺綠條紋和深綠純色西瓜的正確分類率分別為66.4%和82.2%，針對(duì)不同類型西瓜分別建立模型能獲得更好結(jié)果。

哈密瓜與西瓜類似，研究顯示，其可溶性固形物含量與特定波長(zhǎng)反射率存在強(qiáng)相關(guān)性。通過(guò)優(yōu)化選擇的特征波長(zhǎng)建立的簡(jiǎn)化模型，既保持了預(yù)測(cè)精度，又提高了檢測(cè)速度。

榴蓮作為巨大挑戰(zhàn)性的厚皮水果之一，其成熟度檢測(cè)一直依賴經(jīng)驗(yàn)判斷或破壞性方法。榴蓮成熟度檢測(cè)常依賴經(jīng)驗(yàn)判斷或破壞性方法。一項(xiàng)研究采用1100~2500nm光譜范圍，使用果皮和莖的光譜信息對(duì)果肉干物質(zhì)，進(jìn)行間接預(yù)測(cè)成熟度。

研究發(fā)現(xiàn)，在將榴蓮分為未成熟、早成熟和成熟類別的過(guò)程中，外皮模型更優(yōu)；預(yù)測(cè)干物質(zhì)含量方面，果皮模型表現(xiàn)更好。研究人員發(fā)現(xiàn)，盡管與參考果肉模型的精度相比，準(zhǔn)確度相對(duì)較低，但在選定波長(zhǎng)下，組合分析外皮和莖干光譜數(shù)據(jù)可提供較高分類精度。

A watermelon study employed near-infrared spectroscopy (NIRS) technology, covering spectral ranges of 908–1676 nm and 950–1650 nm, to examine 249 intact watermelons (152 with light green striped rinds and 97 with dark green solid rinds). Using partial least squares discriminant analysis (PLS-DA), a quantitative model for soluble solids content (SSC) was constructed. Results showed correct classification rates of 66.4% for light green striped watermelons and 82.2% for dark green solid ones, indicating that separate models for different types yield better outcomes.

Similar to watermelons, cantaloupe studies revealed strong correlations between soluble solids content and reflectance at specific wavelengths. Simplified models built with optimized characteristic wavelengths maintained prediction accuracy while improving detection speed.

Durian, one of the most challenging thick-skinned fruits, has traditionally relied on experiential judgment or destructive methods for ripeness assessment. A study used the 1100–2500 nm spectral range, leveraging rind and stem spectral data to indirectly predict pulp dry matter content as an indicator of ripeness.

The study found that for classifying durians into unripe, early ripe, and ripe categories, the rind model performed better. In predicting dry matter content, the rind model also showed superior performance. Researchers noted that although the accuracy was relatively lower compared to reference pulp models, combining rind and stem spectral data at selected wavelengths could achieve higher classification precision.

西瓜研究 / Watermelon Study：

西瓜的平均近紅外光譜 / Average near-infrared spectra of watermelon

使用LVF儀器預(yù)測(cè)完整條紋淺綠色和實(shí)心深綠色外皮西瓜中可溶性固形物含量(%)的最佳方程的校準(zhǔn)統(tǒng)計(jì)量 /

Calibration statistics of the optimal equation for predicting soluble solids content (%) in intact striped light-green and solid dark-green rind watermelons using LVF instrumentation

榴蓮研究 / Durian Study：

(a)果柄和(b)果皮被放置在樣品架中的情況。通過(guò)旋轉(zhuǎn)旋鈕可水平或垂直移動(dòng)樣品，如指針?biāo)?，使其到達(dá)檢測(cè)焦點(diǎn)位置。

Photographs showing (a) the stem and (b) the rind placed in the sample holder. Knob rotations are used  to move the samples horizontally and vertically to the focal position for irradiation as indicated by the needle.

基于近紅外光譜的不同成熟階段均值光譜變化：(a) 果肉；(b) 果皮；(c) 果柄

Variationwithrespect to maturation stages of mean spectra using near-infrared spectroscopy of: (a) pulp;  (b) rind; and (c) stem.

討論和結(jié)語(yǔ) / Disscusion & Conclusion

實(shí)現(xiàn)穩(wěn)健的校準(zhǔn)模型是當(dāng)前研究的重點(diǎn)。模型的低穩(wěn)健性會(huì)阻礙其在跨環(huán)境（從實(shí)驗(yàn)室到現(xiàn)場(chǎng)）、跨樣本（不同品種/年份）以及跨設(shè)備間的推廣應(yīng)用。自然界的復(fù)雜性和大量變異是主要挑戰(zhàn)?？蓸?gòu)建覆蓋不同年份、果園和品種的多樣化樣品數(shù)據(jù)庫(kù)增強(qiáng)模型適應(yīng)性。

模型泛化能力研究仍顯不足，實(shí)際生產(chǎn)中的環(huán)境條件波動(dòng)也會(huì)進(jìn)一步考驗(yàn)?zāi)Ｐ推者m性。

作為高光譜成像系統(tǒng)硬件的提供商，我們致力于為高校研究所和解決方案集成商提供高性能、可靠的光學(xué)成像平臺(tái)。我們期待與研究機(jī)構(gòu)、系統(tǒng)集成商合作，共同開(kāi)發(fā)面向特定場(chǎng)景的成熟度檢測(cè)解決方案，推動(dòng)這項(xiàng)技術(shù)從實(shí)驗(yàn)室走向田間和生產(chǎn)線。

Developing robust calibration models remains a key focus of current research. Low model robustness hinders their application across environments (from lab to field), samples (different varieties/years), and devices. The complexity and vast variability in nature pose major challenges. Building diverse sample databases covering multiple years, orchards, and varieties can enhance model adaptability.

Research on model generalization capability is still insufficient, and fluctuating environmental conditions in real-world production further test model universality.

As a provider of hyperspectral imaging system hardware, we are committed to delivering high-performance, reliable optical imaging platforms to academic institutions and solution integrators. We look forward to collaborating with research organizations and system integrators to develop ripeness detection solutions tailored to specific scenarios, advancing this technology from the lab to fields and production lines.

案例來(lái)源 / Source：

1. Varga LA, Makowski J, Zell A. Measuring the ripeness of fruit with hyperspectral imaging and deep learning. 2021 International Joint Conference on Neural Networks (IJCNN). 2021:1-8.

2. Vega-Castellote M, Sánchez MT, Torres I, de la Haba MJ, Pérez-Marín D. Assessment of watermelon maturity using portable new generation NIR spectrophotometers. Scientia Horticulturae. 2022;304:111328. 

3. Somton W, Pathaveerat S, Terdwongworakul A. Application of near infrared spectroscopy for indirect evaluation of “Monthong" durian maturity. International Journal of Food Properties. 2015;18(6):1155-1168. 

4. Liu J, Meng H. Research on the maturity detection method of Korla pears based on hyperspectral technology. Agriculture. 2024;14:1257.