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Horticulturae
Special Issues
New Trends in Smart Horticulture
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New Trends in Smart Horticulture

A special issue of Horticulturae (ISSN 2311-7524).

Deadline for manuscript submissions: 15 April 2026 | Viewed by 4227

Special Issue Editors

Dr. Adão Felipe dos Santos

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Guest Editor
Department of Agriculture, Federal University Lavras, Lavras 37200-000, Brazil
Interests: remote sensing; UAV; precision agriculture; digital agriculture; spray drones
Dr. Rouverson Pereira Silva

E-Mail Website1 Website2
Guest Editor
Department of Engineering, São Paulo State University, Jaboticabal, Sao Paulo 14884-900, Brazil
Interests: digital agriculture; agricultural machinery; agricultural mechanization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aitazaz A. Farooque

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Guest Editor
Department of Engineering School of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A4P3, Canada
Interests: irrigation and water management; machine vision and deep learning; map and sensor based variable rate technology; precision harvesting technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Edgard Henrique Costa Silva

E-Mail Website
Guest Editor
Department of Agronomy, Western São Paulo University, Presidente Prudente 19067-175, Brazil
Interests: data-driven decision making; controlled environment agriculture; crop monitoring and decision making; sustainable horticulture; yield prediction; high-throughput phenotyping

Special Issue Information

Dear Colleagues,

The rapid advancements in technology are reshaping horticultural practices, enabling more efficient, sustainable, and data-driven crop management. This Special Issue, New Trends in Smart Horticulture, aims to highlight cutting-edge research and innovations that integrate precision agriculture, artificial intelligence, remote sensing, high-throughput phenotyping (HTP), automation, and robotics into horticultural systems.

We welcome contributions addressing topics such as smart sensors for plant and soil monitoring, UAV and satellite-based imaging for crop analysis, AI-driven decision-making tools, and novel approaches to controlled-environment agriculture. Additionally, studies exploring IoT applications, digital twins, and machine learning models to optimize resource use and improve yield prediction are encouraged.

By gathering recent scientific advancements, this Special Issue will serve as a platform for researchers and practitioners to discuss how smart technologies are transforming horticulture. We invite original research articles and reviews that demonstrate the impact of these innovations on productivity, sustainability, and resilience in horticultural production systems.

We look forward to your contributions to this exciting field of research.

Dr. Adão Felipe dos Santos
Dr. Rouverson Pereira Silva
Prof. Dr. Aitazaz A. Farooque
Dr. Edgard Henrique Costa Silva
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 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. Horticulturae is an international peer-reviewed open access monthly 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 2200 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

  • smart horticulture
  • precision agriculture
  • remote sensing
  • smart harvesting
  • artificial intelligence
  • machine learning
  • Internet of Things (IoT)
  • automation and robotics
  • UAV and satellite imaging
  • digital twins
  • smart sensors
  • data-driven decision making
  • controlled-environment agriculture
  • crop monitoring and decision making
  • sustainable horticulture
  • yield prediction
  • high-throughput phenotyping

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

Published Papers (4 papers)

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Research

21 pages, 18153 KB  
Article
A Two-Stage Canopy Extraction Method Utilizing Multispectral Images to Enhance the Estimation of Canopy Nitrogen Content in Pear Orchards with Full Grass Cover
by Yuanhao Sun, Kai Huang, Quanchun Yuan, Xiaohui Lei and Xiaolan Lv
Horticulturae 2025, 11(12), 1419; https://doi.org/10.3390/horticulturae11121419 - 24 Nov 2025
Viewed by 488
Abstract
Accurately extracting the canopies of fruit trees is crucial to improve the estimation accuracy of CNC inversion as well as determine a reasonable application of nitrogen fertilizer. To date, existing studies have mainly focused on canopy extraction in scenarios with no grass or [...] Read more.
Accurately extracting the canopies of fruit trees is crucial to improve the estimation accuracy of CNC inversion as well as determine a reasonable application of nitrogen fertilizer. To date, existing studies have mainly focused on canopy extraction in scenarios with no grass or sparse grass cover, paying less attention to scenarios with a full grass cover. Thus, in this paper, a two-stage canopy extraction (TCE) method was proposed to address the issue of canopy extraction in scenarios with full grass cover. Firstly, the height difference between the canopies of pear trees and the ground grass was used to eliminate the interference of the ground grass and achieve a coarse-grained canopy extraction. Then, based on the extracted coarse-grained canopies and CIELAB color space, the color thresholds of the L*, a*, and b* channels were determined to remove the interference factors, e.g., branches, shadows, and trellises, for fine-grained canopy extraction by using data distribution from the three channels based on a histogram and the threshold of confidence interval. In canopy extraction experiments, the accuracy, recall, precision, and F1-score of TCE in scenarios with full grass cover can reach 91.725%, 95.789%, 91.284%, and 93.482%, respectively, demonstrating the effectiveness of TCE in addressing canopy extraction issues in this scenario. Thirdly, the RF algorithm was utilized to select suitable VIs based on R2 and RMSE values, and CNC inversion models were constructed. In estimation experiments on CNC inversion, the R2, RMSE, and nRMSE of the constructed CNC inversion based on TCE in a scenario with full grass cover were 0.724, 0.243, and 19.120%, respectively. A comparative analysis with the baseline method revealed that accurate canopy extraction contributed to a high estimation accuracy of CNC inversion. Therefore, our proposed method can provide technical support for the efficient and non-destructive monitoring of the canopy nutrient status in pear orchards. Full article
(This article belongs to the Special Issue New Trends in Smart Horticulture)
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27 pages, 28375 KB  
Article
Modular IoT Hydroponics System
by Manlio Fabio Aranda Barrera and Hiram Ponce
Horticulturae 2025, 11(11), 1306; https://doi.org/10.3390/horticulturae11111306 - 31 Oct 2025
Viewed by 1303
Abstract
Hydroponics offers a promising alternative to soil-based agriculture, enabling higher yields, resource efficiency, and improved crop quality. This study compares traditional hydroponic setups with systems enhanced through the Internet of Things (IoT) framework using the Nutrient Film Technique and a proportional–integral controller, focusing [...] Read more.
Hydroponics offers a promising alternative to soil-based agriculture, enabling higher yields, resource efficiency, and improved crop quality. This study compares traditional hydroponic setups with systems enhanced through the Internet of Things (IoT) framework using the Nutrient Film Technique and a proportional–integral controller, focusing on growth performance and environmental control. Systems incorporating Internet of Things technology achieved a growth rate of 0.94 cm/day versus 0.16 cm/day for conventional setups, due to precise water temperature control, optimized lighting, data acquisition, targeted nutrients, and reduced pest incidence. The integration of Industry 4.0 principles further enhances sustainable production and resource management. Statistical validation under diverse conditions is recommended. Future work will add environmental sensors, refine mechanical design, and explore machine learning for adaptive control, highlighting the potential of Internet of Things–based hydroponics to transform agriculture through intelligent, efficient, and eco-friendly cultivation. Full article
(This article belongs to the Special Issue New Trends in Smart Horticulture)
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27 pages, 6859 KB  
Article
An Explainable Machine Learning Framework for the Hierarchical Management of Hot Pepper Damping-Off in Intensive Seedling Production
by Zhaoyuan Wang, Kaige Liu, Longwei Liang, Changhong Li, Tao Ji, Jing Xu, Huiying Liu and Ming Diao
Horticulturae 2025, 11(10), 1258; https://doi.org/10.3390/horticulturae11101258 - 17 Oct 2025
Viewed by 911
Abstract
Facility agriculture cultivation is the main production form of the vegetable industry in the world. As an important vegetable crop, hot peppers are easily threatened by many diseases in a facility microclimate environment. Traditional disease detection methods are time-consuming and allow the disease [...] Read more.
Facility agriculture cultivation is the main production form of the vegetable industry in the world. As an important vegetable crop, hot peppers are easily threatened by many diseases in a facility microclimate environment. Traditional disease detection methods are time-consuming and allow the disease to proliferate, so timely detection and inhibition of disease development have become the focus of global agricultural practice. This article proposed a generalizable and explainable machine learning model for hot pepper damping-off in intensive seedling production under the condition of ensuring the high accuracy of the model. Through Kalman filter smoothing, SMOTE-ENN unbalanced sample processing, feature selection and other data preprocessing methods, 19 baseline models were developed for prediction in this article. After statistical testing of the results, Bayesian Optimization algorithm was used to perform hyperparameter tuning for the best five models with performance, and the Extreme Random Trees model (ET) most suitable for this research scenario was determined. The F1-score of this model is 0.9734, and the AUC value is 0.9969 for predicting the severity of hot pepper damping-off, and the explainable analysis is carried out by SHAP (SHapley Additive exPlanations). According to the results, the hierarchical management strategies under different severities are interpreted. Combined with the front-end visualization interface deployed by the model, it is helpful for farmers to know the development trend of the disease in advance and accurately regulate the environmental factors of seedling raising, and this is of great significance for disease prevention and control and to reduce the impact of diseases on hot pepper growth and development. Full article
(This article belongs to the Special Issue New Trends in Smart Horticulture)
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21 pages, 7766 KB  
Article
An Intelligent Operation Area Allocation and Automatic Sequential Grasping Algorithm for Dual-Arm Horticultural Smart Harvesting Robot
by Bin Yan and Xiameng Li
Horticulturae 2025, 11(7), 740; https://doi.org/10.3390/horticulturae11070740 - 26 Jun 2025
Cited by 1 | Viewed by 941
Abstract
Aiming to solve the problem that most existing apple-picking robots operate with a single arm and that the overall efficiency of the machine needs to be further improved, a prototype of a dual-arm picking robot was built, and its picking operation planning method [...] Read more.
Aiming to solve the problem that most existing apple-picking robots operate with a single arm and that the overall efficiency of the machine needs to be further improved, a prototype of a dual-arm picking robot was built, and its picking operation planning method was studied. Firstly, based on the configuration and motion mode of the AUBO-i5 robotic arm, the overlapping dual-arm layout of the workspace was determined. Then, a prototype of a dual-arm apple-picking robot was built, and, based on the designed dual-arm spatial layout, a dual-arm picking operation zoning planning method was proposed. The experimental results showed that in the four simulation experiments, the highest value of the maximum parallel operation proportion of the dual arms was 83%, and the lowest value was 50.6%. The highest value of the maximum operation length of the single arm was 7323 mm, and the lowest value was 5654 mm. The total length of the dual-arm operation path was 12,705 mm, and the lowest value was 8770 mm. Furthermore, a fruit-picking sequence planning method based on dual robotic arm operation was proposed. Fruit traversal simulation verification experiments were conducted. The results showed that there was no conflict between the left and right arms during the motion of the dual robotic arms. Finally, the proposed dual-arm robot operation zoning and picking sequence planning method was validated in the apple experimental station. The results showed that the proportion of dual-arm parallel operations was the lowest at 50.7% and the highest at 72.4%. The total length of the dual-arm operation path was the highest at 8604 mm and the lowest at 6511 mm. Full article
(This article belongs to the Special Issue New Trends in Smart Horticulture)
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