Identification of tomato maturity based on multinomial logistic regression with kernel clustering by integrating color moments and physicochemical indices,Journal of Food Process Engineering

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Identification of tomato maturity based on multinomial logistic regression with kernel clustering by integrating color moments and physicochemical indices
Journal of Food Process Engineering ( IF 2.7 ) Pub Date : 2020-08-17 , DOI: 10.1111/jfpe.13504
Yiping Jiang ₁ , Bei Bian ₁ , Xiaochan Wang ₁ , Sifan Chen ₁ , Yuhua Li ₁ , Ye Sun ₁

Affiliation

The identification of tomato maturity is significant to extend the fruit shelf life and generate the scientific processing strategy. Tomato maturation is a gradual process, and the internal physicochemical characteristics are most related to maturity states. Merely choosing visual features to identify maturity would cause discriminant errors. This study designed a simple and effective identification method for tomato maturity by integrating color moments and physicochemical indices. The color moments were extracted by an adaptive K‐means clustering image processing program, and firmness, soluble solid content and sensory evaluation were measured by professional techniques. The optimal multidimensional index set was formulated according to color moments and physicochemical indices simultaneously. To reduce the confusion between adjacent stages, a novel multinomial logistic regression with kernel clustering (MLRKC) method was designed to identify maturity, and the accuracy was 95.83% for tomato testing set. Moreover, the traditional image features set and some classic methods were applied to verify the performance of proposed method, respectively. Finally, the proposed method was applied to identify the tomatoes in the realistic circumstance. The identification results demonstrated satisfactory performances and promising applications of MLRKC method integrating color moments and physicochemical indices.

中文翻译：

结合色矩和理化指标，基于核聚类的多项式逻辑回归与番茄成熟度鉴定

番茄成熟度的识别对于延长水果的货架寿命并产生科学的加工策略具有重要意义。番茄的成熟是一个渐进的过程，其内部理化特性与成熟状态最相关。仅选择视觉特征来识别成熟度将导致判别错误。本研究通过结合色矩和理化指标设计了一种简单有效的番茄成熟度鉴定方法。通过自适应K均值聚类图像处理程序提取色矩，并通过专业技术测量硬度，可溶性固形物含量和感官评估。同时根据色矩和理化指标制定了最佳的多维指标集。为了减少相邻阶段之间的混淆，设计了一种新的基于核聚类的多项式逻辑回归（MLRKC）来识别成熟度，番茄测试集的准确性为95.83％。此外，分别使用传统图像特征集和一些经典方法来验证所提出方法的性能。最后，将所提出的方法应用于现实情况下的番茄识别。鉴定结果表明，结合色矩和理化指标的MLRKC方法具有令人满意的性能和广阔的应用前景。分别。最后，将所提出的方法应用于实际情况下的番茄识别。鉴定结果表明，结合色矩和理化指标的MLRKC方法具有令人满意的性能和广阔的应用前景。分别。最后，将所提出的方法应用于现实情况下的番茄识别。鉴定结果表明，结合色矩和理化指标的MLRKC方法具有令人满意的性能和广阔的应用前景。

更新日期：2020-10-02

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