Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

Copyright: © 2024 Elmetwalli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

水果品质参数的估计通常基于破坏性技术，这些技术在处理大量水果时繁琐、昂贵且不可靠。相比之下，非破坏性技术如图像处理和光谱反射率可用于快速检测水果品质参数。本研究旨在评估图像处理、光谱反射度指数（SRIs）以及决策树（DT）和随机森林（RF）等机器学习模型在不同成熟阶段的柑橘和番茄果实品质特征定性估计中的潜力。测量的质量参数包括叶绿素a (Chl a)、叶绿素b (Chl b)、可溶性固形物(TSS)、滴定酸度(TA)、TSS/TA比值、类胡萝卜素(car)、番茄红素和硬度。结果显示，红-蓝-绿（RGB）指数以及新开发的SRIs对量化不同水果特性表现出高效率。例如，所有RGB指数(RGBI)与测量参数之间的关系R²在柑橘中变化范围为0.62到0.96，在番茄中变化范围为0.29到0.90。如可见大气抵抗指数(VARI)和归一化红色(Rn)的RGBI对柑橘果实中的类胡萝卜素(car)表现出最高的R² = 0.96。而过剩红植被指数(ExR)对番茄果实中的类胡萝卜素(car)表现出最高的R² = 0.84。SRIs如RSI 710,600和R730,650在柑橘果实中显示出最大的与Chl a相关的R²值（R² = 0.80），而在番茄果实中GI对Chl a表现出最大的R²（R² = 0.68）。结合RGB和SRIs与DT和RF模型会是估计八个观测变量的稳健策略，且具有合理的准确性。对于柑橘果实，在预测Chl a的任务中，DT-2HV模型取得了卓越的结果，在训练集中的R²为0.993，RMSE为0.149；在验证集中的R²为0.991，RMSE为0.114。同样对于番茄果实，DT-5HV模型在Chl a预测中表现出色，训练数据集中R²为0.905，RMSE为0.077；验证数据集中R²为0.785，RMSE为0.077。总体结果表明，RGB、新开发的SRIs以及基于DT和RF的RGBI和SRIs可以用于评估柑橘和番茄果实的测量参数。

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