Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
SMART Farming Technology Research Centre (SFTRC), Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
J Sci Food Agric. 2022 Jun;102(8):3266-3276. doi: 10.1002/jsfa.11669. Epub 2021 Dec 9.
Evaluation of the quality properties of papaya becomes essential due to the acceleration of the fruit shelf-life senescence and the deterioration factor of the expected postharvest operations. In this study, the colour features in RGB, normalised RGB, HSV and Lab* channels were extracted and correlated with mechanical properties, moisture content (MC), total soluble solids (TSS) and pH for the prediction of quality properties at five ripening stages of papaya (R1-R5).
The mean values of colour features in RGB , normalised RGB HSV , and Lab* were the best estimator for predicting TSS with R ≥ 0.90. All colour channels also showed satisfactory accuracies of R ≥ 0.80 in predicting the bioyield force, apparent modulus and mean force. The highest average classification accuracy was obtained using linear discriminant analysis (LDA) with an average accuracy of more than 82%. The study showed that LDA, linear support vector machine, quadratic discriminant analysis and quadratic support vector machine obtained the correct classification of up to 100% for R5, whereas R1, R2, R3 and R4 gave classification accuracies in the range 83.75-91.85%, 85.6-90.25%, 85.75-90.85% and 77.35-87.15%, respectively. This indicates that R5 colour information was obviously different from R1-R4. The mean values of the HSV channel indicated the best performance to predict the ripening stages of papaya, compared to RGB, normalised RGB and Lab* channels, with an average classification accuracy of more than 80%.
The study has shown the versatility of a machine vision system in predicting the quality changes in papaya. The results showed that the machine vision system can be used to predict the ripening stages as well as classifying the fruits into different ripening stages of papayas. © 2021 Society of Chemical Industry.
由于果实货架期衰老的加速和预期采后操作的恶化因素，评估木瓜的品质特性变得至关重要。在这项研究中，提取了 RGB、归一化 RGB、HSV 和 Lab*通道中的颜色特征，并将其与机械性能、水分含量 (MC)、总可溶性固形物 (TSS) 和 pH 相关联，以预测木瓜五个成熟阶段 (R1-R5) 的品质特性。
在预测 TSS 方面，RGB 、归一化 RGB HSV 和 Lab* 的颜色特征平均值是最好的预测指标，相关系数 R ≥ 0.90。所有颜色通道在预测生物产量力、表观模量和平均力方面也表现出令人满意的 R ≥ 0.80 的准确性。线性判别分析 (LDA) 获得的平均分类准确率最高，平均准确率超过 82%。研究表明，LDA、线性支持向量机、二次判别分析和二次支持向量机对 R5 的正确分类准确率高达 100%，而 R1、R2、R3 和 R4 的分类准确率范围分别为 83.75-91.85%、85.6-90.25%、85.75-90.85%和 77.35-87.15%。这表明 R5 的颜色信息与 R1-R4 明显不同。与 RGB、归一化 RGB 和 Lab* 通道相比，HSV 通道的平均值表现出预测木瓜成熟度阶段的最佳性能，平均分类准确率超过 80%。
该研究表明机器视觉系统在预测木瓜品质变化方面具有多功能性。结果表明，机器视觉系统可用于预测成熟度阶段，并将果实分为不同的成熟度阶段。 © 2021 化学工业协会。
