Evaluation of Variability in Tunisian Olea europaea L. Accessions using Morphological Characters and Computational Approaches

Abstract

The olive trees (Olea europaea L.) have been cultivated for millennia in the Mediterranean basin and its oil has been an important part of human nutrition in the region. In order to distinguish between olive accessions, morphological and biological characters have been widely and commonly used for descriptive purposes and have been used to characterize olive accessions. A comparative study of morphological characters of olive accessions grown in Tunisia was carried out and analyzed using Bayesian Networks (BN) and Principal Components Analysis (PCA). The obtained results showed that averages of fruit and kernel weights were 2.27 grams and 0.41 grams, respectively. Besides, a relatively moderate level of variation (51.22%) being explained by four Principal components. BN revealed that geographical localisation plays a role in the increase of tree habit, size of lenticels and leaf shape. A dendrogram has been carried out in the aim to classify studied olive accessions. We proposed a novel method of analysis based on the three-step scheme, in which first the data set is clustered, then olive tree features are evaluated. The studied accessions can be divided into four main groups by cutting the dendrogram at a similarity value of 0.645. Different relationships are studied and highlighted, and finally the collected features are subjected to a global principal component analysis. Obtained results confirmed that core surface was negatively correlated with geographical location (r = -0.52, p<0.05) and maturation period r = -0.539, p<0.05). Number of lenticels was positively correlated to lenticels size (r = 0.632, p<0.05). Core shape had a negative correlation with fruit shape (r = -0.759, p<0.05). On the basis of these findings, this research confirmed that morphological markers are a preliminary tool to characterize olive oil accessions.

Computer vision-based dimension measurement system for olive identification

“…Additionally, Ennouri et al (2017) implied that the morphological markers are a preliminary tool to characterize olive oil accessions. In conclusion, we can develop a more efficient machine and tools with getting morphological characteristics and digital information of olives for agricultural applications such as fertilization, pesticide applications, crop separation, and classification at olive trees.…”

Using Simulation and Data Mining for Engineering Knowledge Improvement

“…Moreover, diverse data-mining strategies, including multiple linear regression (MLR) [33], inspired from the common least-square approach; principal component regression (PCR), an unsupervised method inspired from the principal component analysis [34]; the Partial Least Squares (PLS), defined as a supervised method [35], and the Nonlinear Partial Least Squares (NLPLS), which utilizes various neural network functions [36] to plot nonlinearity into models, were related to each of the data sets. Each procedure has diverse strategies for use; these distinctive techniques were utilized on each data set first and the best strategy in every system was noted and utilized for global examination with other methods for similar data set [37].…”