Vazquez Dana V, Spetale Flavio E, Nankar Amol N, Grozeva Stanislava, Rodríguez Gustavo R
Instituto de Investigaciones en Ciencias Agrarias de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Rosario (IICAR-CONICET-UNR), Campo Experimental Villarino, Zavalla S2125ZAA, Argentina.
Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Parque Villarino, CC Nº 14, Zavalla S2125ZAA, Argentina.
Plants (Basel). 2024 Aug 23;13(17):2357. doi: 10.3390/plants13172357.
Fruit shape significantly impacts the quality and commercial value of tomatoes ( L.). Precise grading is essential to elucidate the genetic basis of fruit shape in breeding programs, cultivar descriptions, and variety registration. Despite this, fruit shape classification is still primarily based on subjective visual inspection, leading to time-consuming and labor-intensive processes prone to human error. This study presents a novel approach incorporating machine learning techniques to establish a robust fruit shape classification system. We trained and evaluated seven supervised machine learning algorithms by leveraging a public dataset derived from the Tomato Analyzer tool and considering the current four classification systems as label variables. Subsequently, based on class-specific metrics, we derived a novel classification framework comprising seven discernible shape classes. The results demonstrate the superiority of the Support Vector Machine model in terms of its accuracy, surpassing human classifiers across all classification systems. The new classification system achieved the highest accuracy, averaging 88%, and maintained a similar performance when validated with an independent dataset. Positioned as a common standard, this system contributes to standardizing tomato fruit shape classification, enhancing accuracy, and promoting consensus among researchers. Its implementation will serve as a valuable tool for overcoming bias in visual classification, thereby fostering a deeper understanding of consumer preferences and facilitating genetic studies on fruit shape morphometry.
果实形状对番茄（L.）的品质和商业价值有显著影响。在育种计划、品种描述和品种登记中，精确分级对于阐明果实形状的遗传基础至关重要。尽管如此，果实形状分类仍主要基于主观视觉检查，导致过程耗时且 labor-intensive，容易出现人为错误。本研究提出了一种结合机器学习技术的新方法，以建立一个强大的果实形状分类系统。我们利用从番茄分析仪工具获得的公共数据集，并将当前的四种分类系统作为标签变量，对七种监督机器学习算法进行了训练和评估。随后，基于特定类别的指标，我们得出了一个由七个可区分形状类别组成的新分类框架。结果表明，支持向量机模型在准确性方面具有优越性，在所有分类系统中均超过人工分类器。新的分类系统实现了最高准确率，平均为88%，并且在使用独立数据集进行验证时保持了相似的性能。作为一个通用标准，该系统有助于标准化番茄果实形状分类，提高准确性，并促进研究人员之间的共识。其实施将成为克服视觉分类偏差的宝贵工具，从而促进对消费者偏好的更深入理解，并推动果实形状形态计量学的遗传研究。
