Machine Learning to Identify Olive-Tree Cultivars

Abstract

The identification of olive-tree cultivars is a lengthy and expensive process, therefore, the proposed work presents a new strategy for identifying different cultivars of olive trees using their leaf and machine learning algorithms. In this initial case, four autochthonous cultivars of the Trás-os-Montes region in Portugal are identified (Cobrançosa, Madural, Negrinha e Verdeal). With the use of this type of algorithm, it is expected to replace the previous techniques, saving time and resources for farmers. Three different machine learning algorithms (Decision Tree, SVM, Random Forest) were also compared and the results show an overall accuracy rate of the best algorithm (Random Forest) of approximately 93%.

Automated classification of olive fruit for enhanced olive oil production using computer vision

“…In the case of employing object detectors, YOLO detector was recently used to support the production of high-quality EVOO by automatically and quickly classifying olive lots into different quality classes, for both oil production and table olives (see e.g., Salvucci et al 16 ). A similar approach has been studied in 17 in which the latest two versions of Yolo (v7 and v8) have been considered for counting.…”

Evaluation of Land Suitability for Olive (Olea europaea L.) Cultivation Using the Random Forest Algorithm

“…In that study, in which many ML models were compared, suitable areas for tea cultivation were determined by using 12 parameters including climate, soil, land, and economy factors and the RF algorithm. In the literature, there are studies carried out to determine the distribution of olive trees in a particular region or to determine olive tree varieties using ML or DL algorithms [37][38][39][40]. However, to the best of our knowledge, no study has been performed to determine suitable areas for olive cultivation using ML algorithms.…”