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Empowering olive cultivation with artificial intelligence: a systematic literature review on advancements and prospects - Soft Computing
AI enhances olive cultivation efficiency and sustainability through advanced techniques like Deep Learning for tasks such as disease classification and yield forecasting.

Stargo's Stardox can transform unstructured agricultural data into actionable insights, enhancing sustainability and efficiency in olive cultivation.
Executive Summary
This study provides a Systematic Literature Review on the application of Artificial Intelligence algorithms in the primary sector of olive cultivation. It compiles and analyses a collection of studies that leverage AI to enhance the efficiency and sustainability of olive production, maintenance, and harvesting processes. In this study, 43 papers were reviewed from the databases IEEE, Scopus, and Web of Science through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. The findings highlight a significant trend toward adopting advanced AI techniques, particularly Deep Learning algorithms such as Convolutional Neural Networks, for tasks ranging from cultivar identification and foliar disease classification to crop yield forecasting with high accuracies.
Source: SpringerLink
Authors: João Mendes, José Lima, Lino Costa, Ana I. Pereira
Published: 2026-02-07
Original Article: https://link.springer.com/article/10.1007/s00500-025-11067-z
Usage Rights: Open access
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