Giordano Cristiana, Arcidiaco Lorenzo, Rodolfi Margherita, Ganino Tommaso, Beghè Deborah, Petruccelli Raffaella
Insitute of BioEconomy, CNR, via Madonna del Piano 10, Sesto Fiorentino, 50019 Firenze, Italy.
Food and Drug Department, University of Parma, Parco Area delle Scienze, 27/a, 43124 Parma, Italy.
Plants (Basel). 2025 Jan 23;14(3):333. doi: 10.3390/plants14030333.
Common fig, or simply fig ( L.), is one of the most ancient species originated and domesticated in the Mediterranean basin. The Italian fig germplasm consists of a large number of cultivars, more than 300. This number is approximate; there are many genotypes that are still poorly known and studied that may possess interesting agronomic traits, especially in terms of response to climate change. Therefore, it is extremely important to study and preserve agrobiodiversity, but more importantly to identify simple and rapid characterization methods to catalog "hidden" cultivated plants. In this study, geometric leaf morphometry was used to explore differences among fifteen Tuscan fig cultivars. In addition, the effectiveness of a machine learning (ML) algorithm to characterize cultivars was evaluated. The study analyzed two classes of cultivars, one of plants with predominantly three-lobed leaf shape, and one five-lobed. Thirty-three descriptors for the five-lobed and twenty-three for the three-lobed. Anova analysis showed statistically significant differences for all characters analyzed and allowed an initial characterization of the material. Then, Random Forest algorithm analysis was used to reduce the number of parameters to those most significant for classification. The results showed that machine learning-based techniques are a valid system for analyzing leaves of cultivars and interpreting significant differences in leaf parameters. Classification based on the Random Forest model allowed us to filter out the main descriptors that best differentiate cultivars from each other.
普通无花果,或简称为无花果(Ficus carica L.),是在地中海盆地起源并驯化的最古老物种之一。意大利的无花果种质由大量品种组成,超过300种。这个数字是近似的;有许多基因型仍鲜为人知且研究不足,它们可能具有有趣的农艺性状,特别是在应对气候变化方面。因此,研究和保护农业生物多样性极其重要,但更重要的是确定简单快速的表征方法,以便对“隐藏”的栽培植物进行编目。在本研究中,利用几何叶片形态测量法探究了15个托斯卡纳无花果品种之间的差异。此外,还评估了一种机器学习(ML)算法对品种进行表征的有效性。该研究分析了两类品种,一类是叶片形状主要为三叶的植物,另一类是五叶的。五叶品种有33个描述符,三叶品种有23个。方差分析表明,所有分析的性状在统计学上都有显著差异,并允许对材料进行初步表征。然后,使用随机森林算法分析将参数数量减少到对分类最重要的参数。结果表明,基于机器学习的技术是分析品种叶片和解释叶片参数显著差异的有效系统。基于随机森林模型的分类使我们能够筛选出最能区分不同品种的主要描述符。