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Agriculture
Special Issues
Innovations and Improvements for Sustainable Olive and Olive Oil Production
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Innovations and Improvements for Sustainable Olive and Olive Oil Production

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Crop Production".

Deadline for manuscript submissions: 25 June 2026 | Viewed by 1064

Special Issue Editor

Dr. Marina Lukić

E-Mail Website
Guest Editor
Department of Agriculture and Nutrition, Institute of Agriculture and Tourism, Karla Huguesa 8, 52440 Poreč, Croatia
Interests: olive variety; olive oil production and quality; olive oil chemistry; chemical and sensorial analyses of agri-food products; phenols; volatile aromatic compounds

Special Issue Information

Dear Colleagues,

The future of olive cultivation and olive oil production relies on combining traditional heritage with smart agriculture to address pressing challenges such as resource depletion and pollution, population growth and climate disruption. Achieving sustainability in olive growing requires continued research and innovation across several key areas.

Thus, this Special Issue invites contributions exploring innovations in sustainable crop management, including reduced reliance on synthetic inputs through integrated and organic practices; climate adaptation strategies such as efficient irrigation, soil moisture retention, and the use of drought- and heat-tolerant cultivars; circular economy approaches that repurpose cultivation and processing residues into biofuels, compost, and valuable plant extracts; and process improvements aimed at enhancing efficiency and product quality, including the development of functional foods, flavoured oils, and bioactive extracts. Precision agriculture, supported by remote sensing, sensor networks, and AI-based predictive tools, is also a rapidly advancing field with broad application across the production cycle. While these are key focus areas, relevant work beyond them is also welcome.

This Special Issue aims to become a collection of the latest insights and practical advancements toward sustainable, resilient, and high-quality olive and olive oil production in the face of climate change.

Dr. Marina Lukić
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Olea europaea L.
  • sustainability
  • integrated and organic production
  • biodiversity
  • biostimulants
  • soil conservation
  • water management
  • new value-added products
  • by-products
  • sensors and predictive models

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

21 pages, 6362 KB  
Article
Efficient Olive Leaf Disease Detection via Hybrid Artificial Rabbit Optimization and Genetic Algorithm-Based Deep Feature Selection
by Cumali Turkmenoglu, Hakan Gunduz and Emrullah Gazioglu
Agriculture 2026, 16(5), 626; https://doi.org/10.3390/agriculture16050626 - 9 Mar 2026
Cited by 1 | Viewed by 478
Abstract
Artificial intelligence (AI)-supported agricultural disease detection has become increasingly important for addressing global food security challenges. In this study, a hybrid meta-heuristic optimization-based feature selection approach is proposed for the detection of peacock eye disease (Venturia oleaginea) on olive leaves. The [...] Read more.
Artificial intelligence (AI)-supported agricultural disease detection has become increasingly important for addressing global food security challenges. In this study, a hybrid meta-heuristic optimization-based feature selection approach is proposed for the detection of peacock eye disease (Venturia oleaginea) on olive leaves. The proposed method combines Artificial Rabbit Optimization (ARO) and Genetic Algorithm (GA) strategies to balance global exploration and local exploitation during feature selection. Comprehensive experiments conducted on a dataset of 954 olive leaf images demonstrate that the proposed approach achieves an F1-score of 99.7% while reducing the feature dimensionality by 95%, selecting only 100 features from ResNet101. Statistical analysis confirms that the method significantly outperforms standalone GA and ARO approaches (p<0.05, paired t-tests), demonstrating superior long-term convergence behavior and a 47–56% reduction in performance variance across repeated runs. Compared to existing approaches in the literature, the proposed method attains competitive or superior accuracy with substantially fewer features, indicating a marked reduction in computational complexity. These results suggest that the proposed hybrid feature selection framework has strong potential for deployment in resource-constrained agricultural monitoring scenarios, where efficient inference and reduced model complexity are critical. Full article
(This article belongs to the Special Issue Innovations and Improvements for Sustainable Olive and Olive Oil Production)
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