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Agriculture
Special Issues
Application of Smart Technologies in Orchard Management
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Application of Smart Technologies in Orchard Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1365

Special Issue Editors

Dr. Alessandra Vinci

E-Mail Website
Guest Editor
Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, Italy
Interests: precision agriculture; UAV; crop coefficient; irrigation management; crop management
Special Issues, Collections and Topics in MDPI journals
Dr. Daniela Farinelli

E-Mail Website
Guest Editor
Department of Agricultural, Food, and Environmental Sciences (DSA3), University of Perugia, Via Borgo XX Giugno 74, 06121 Perugia, Italy
Interests: mechanical harvest; breeding and clonal selection of new varieties; abiotic stress; fruit growth; ripening indexes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

From several years the role of the technology applied to the agriculture led to promising results. In fact, the smart technologies help the optimization of the use of the input such as water and fertilizers for the increasing sustainability of the orchard. In a contest of climate change, it is important to develop and test smart technology solutions for increasing of the production. In addition, IoT-enabled sensor networks and aerial drones generate high-resolution spatial-temporal data, empowering growers with actionable insights for proactive management. 

This Special Issue focuses on the role that smart technology in the production of high-quality food and continuously over time. For this reason, it welcomes highly quality studies includes, but is not limited to the follow scope:

  • Computer vision for fruit detection and yield estimation;
  • AI-driven microclimate and soil health monitoring systems;
  • IoT-based pest and disease monitoring models;
  • AI-optimized irrigation and nutrient delivery systems;
  • scalable digital for orchard management.

Dr. Alessandra Vinci
Dr. Daniela Farinelli
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • irrigation
  • UAV
  • irrigation systems
  • yield
  • IoT
  • precision agriculture

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

Published Papers (2 papers)

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Research

22 pages, 9279 KB  
Article
ORD-YOLO: A Ripeness Recognition Method for Citrus Fruits in Complex Environments
by Zhaobo Huang, Xianhui Li, Shitong Fan, Yang Liu, Huan Zou, Xiangchun He, Shuai Xu, Jianghua Zhao and Wenfeng Li
Agriculture 2025, 15(15), 1711; https://doi.org/10.3390/agriculture15151711 - 7 Aug 2025
Viewed by 466
Abstract
With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further [...] Read more.
With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further complicates accurate assessment of fruit maturity. To address these challenges, this study proposes an improved model based on YOLOv8, named ORD-YOLO, for citrus fruit maturity detection. To enhance the model’s robustness in complex environments, several key improvements have been introduced. First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. Second, the feature fusion process is optimized and inference speed is increased by integrating a Re-parameterizable Generalized Feature Pyramid Network (RepGFPN). Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. Experimental results demonstrate that the enhanced ORD-YOLO model achieves a precision of 93.83%, a recall of 91.62%, and a mean Average Precision (mAP) of 96.92%, representing improvements of 4.66%, 3.3%, and 3%, respectively, over the original YOLOv8 model. ORD-YOLO not only maintains stable and accurate citrus fruit maturity recognition under complex backgrounds, but also significantly reduces misjudgment caused by manual assessments. Furthermore, the model enables real-time, non-destructive detection. When deployed on harvesting robots, it can substantially increase picking efficiency and reduce post-maturity fruit rot due to delayed harvesting. These advancements contribute meaningfully to the quality improvement, efficiency enhancement, and digital transformation of the citrus industry. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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28 pages, 15894 KB  
Article
Laser Scanning for Canopy Characterization in Hazelnut Trees: A Preliminary Approach to Define Growth Habitus Descriptor
by Raffaella Brigante, Laura Marconi, Simona Lucia Facchin, Franco Famiani, Marta Sánchez Piñero, Silvia Portarena, Rodrigo José De Vargas, Fabiola Villa, Chiara Traini, Alessandra Vinci, Fabio Radicioni and Daniela Farinelli
Agriculture 2025, 15(12), 1251; https://doi.org/10.3390/agriculture15121251 - 9 Jun 2025
Viewed by 563
Abstract
The accurate definition of tree growth descriptors is a crucial step in enhancing orchard management, allowing cultivar identification within an orchard and in new genotype selection for breeding programs. In apple, almond, and olive orchards, Terrestrial Laser Scanning (TLS) technologies have been already [...] Read more.
The accurate definition of tree growth descriptors is a crucial step in enhancing orchard management, allowing cultivar identification within an orchard and in new genotype selection for breeding programs. In apple, almond, and olive orchards, Terrestrial Laser Scanning (TLS) technologies have been already used to identify different architectural groups, but not in hazelnut yet. This study utilized TLS to investigate the canopy structure of hazelnut trees of four different Italian varieties, with and without leaves. TLS proved to be a sensor capable of collecting three-dimensional data from hazelnut field trials and allowed the definition and selection of hazelnut plant descriptors by morphological traits and morphological indexes. Nineteen descriptors, eight morphologic traits and 11 morphological indexes have been identified as reliable suitable descriptors of hazelnut cultivar and in breeding evaluations, according to Biodiversity, FAO and CIHEAM. Many of the selected descriptors are related to the tree habit, vigour and branching density. Two useful indexes have also been defined: Canopy Uprightness (CU) Index and the Index of Canopy Opening (ICO). The descriptors allowed us to distinguish the four studied hazelnut cultivars based on their growth habit; in particular the cultivar Tonda Gentile delle Langhe showed a growth habit that is a lot different from that of the other ones. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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