How Can Spectral Technology Non-Destructively Determine Fruit Ripeness?

想知道牛油果何時入口最佳，榴蓮是否熟透？水果成熟度檢測一直是農業領域的重要課題，本篇將介紹光譜和高光譜成像技術如何為這一課題提供了創新解決方案，通過無損檢測實現精準判斷。

水果成熟過程中，其內部化學成分（如葉綠素、類胡蘿卜素、糖分、酸度等）會發生規律性變化，這些物質對特定波長的光具有吸收和反射特性。研究表明，可見光波段主要反映色素變化，而近紅外區域則與水分、糖分等內部成分密切相關。

在實際應用中，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?

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

香蕉成熟度檢測常利用高光譜成像技術在400-1000nm范圍內采集數據。一個研究團隊通過主成分分析(PCA)結合極限學習機(ELM)建立的模型，對可溶性固形物和硬度的預測相關系數R2分別達到0.92和0.94。

針對牛油果的研究發現，其成熟度判斷主要依賴于800nm以上的近紅外信息，而520~650nm的可見光范圍則有助于區分未成熟與成熟果實。研究人員開發的高光譜卷積神經網絡(HS-CNN)模型，在牛油果成熟度分類中準確率超過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.

高光譜數據對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

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

對于西瓜、哈密瓜、榴蓮等皮厚且堅硬的水果，成熟度檢測面臨很大的挑戰。

一項西瓜研究使用了近紅外光譜（NIRS）技術，涉及908~1676nm和950~1650nm光譜范圍，檢測了249個完整西瓜（152個淺綠條紋果皮，97個深綠純色果皮）。利用偏最小二乘判別分析（PLS-DA），構建可溶性固形物含量（SSC）的定量模型。結果顯示，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%，針對不同類型西瓜分別建立模型能獲得更好結果。

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反射率存在強相關性。通過優化選擇的特征波長建立的簡化模型，既保持了預測精度，又提高了檢測速度。

榴蓮作為巨大挑戰性的厚皮水果之一，其成熟度檢測一直依賴經驗判斷或破壞性方法。榴蓮成熟度檢測常依賴經驗判斷或破壞性方法。一項研究采用1100~2500nm光譜范圍，使用果皮和莖的光譜信息對果肉干物質，進行間接預測成熟度。

研究發現，在將榴蓮分為未成熟、早成熟和成熟類別的過程中，外皮模型更優；預測干物質含量方面，果皮模型表現更好。研究人員發現，盡管與參考果肉模型的精度相比，準確度相對較低，但在選定波長下，組合分析外皮和莖干光譜數據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儀器預測完整條紋淺綠色和實心深綠色外皮西瓜中可溶性固形物含量(%)的最佳方程的校準統計量 /

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。通過旋轉旋鈕可水平或垂直移動樣品，如指針所示，使其到達檢測焦點位置。

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.

討論和結語 / Disscusion & Conclusion

實現穩健的校準模型是當前研究的重點。模型的低穩健性會阻礙其在跨環境（從實驗室到現場）、跨樣本（不同品種/年份）以及跨設備間的推廣應用。自然界的復雜性和大量變異是主要挑戰?？蓸嫿ǜ采w不同年份、果園和品種的多樣化樣品數據庫增強模型適應性。

模型泛化能力研究仍顯不足，實際生產中的環境條件波動也會進一步考驗模型普適性。

作為高光譜成像系統硬件的提供商，我們致力于為高校研究所和解決方案集成商提供高性能、可靠的光學成像平臺。我們期待與研究機構、系統集成商合作，共同開發面向特定場景的成熟度檢測解決方案，推動這項技術從實驗室走向田間和生產線。

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.