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Horticulturae
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Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations
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Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations

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

Deadline for manuscript submissions: 10 June 2025 | Viewed by 5142

Special Issue Editors

Dr. Yang Liu

E-Mail Website
Guest Editor
Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alaer 843300, China
Interests: sustainable agriculture; fruit quality; non-destructive detection; machine learning
Prof. Hong Zhang

E-Mail Website
Guest Editor
College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
Interests: intelligent agriculture; post-harvest; horticulture; nut
Prof. Dr. Haipeng Lan

E-Mail Website
Guest Editor
College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
Interests: sustainable agriculture; post-harvest; fruit ripening; non-destructive detection
Prof. Dr. Silvio José Gumiere

E-Mail Website
Guest Editor
Department of Soil and Agri-Food Engineering, Universite Laval, Québec, QC G1V 0A6, Canada
Interests: climate change; drought management; soil and water conservation; irrigation; hydrological modeling; surface hydrology; rainfall runoff modeling; hydraulics; numerical modeling; hydrology; hydrologic and water resource management; environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the development of modern agriculture, artificial intelligence has achieved initial results in the field of horticulture, and the progress of its research and application continues to promote the development of precision agriculture. The integrated application of artificial intelligence has involved many links such as monitoring, forecasting, decision-making and execution, forming an intelligent monitoring and management system. Although the application of artificial intelligence in digital gardening is promising, the challenges cannot be ignored.

This Special Issue titled "Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations" focuses on exploring the application and innovation of artificial intelligence technology in the field of digital horticulture, aiming to promote the progress and sustainable development of agricultural science and technology. This Special Issue introduces the technological innovations of artificial intelligence in plant growth monitoring, automatic management, precision irrigation, non-destructive testing and evaluation of fruit and vegetable quality, intelligent perception control systems and cloud platform construction. The journal encourages original research across disciplines, with a particular emphasis on research methods that combine theory and practice. Scholars from all walks of life are welcome to contribute to promote the development of artificial intelligence in digital horticulture, in order to achieve an efficient and intelligent agricultural production system.

Dr. Yang Liu
Prof. Hong Zhang
Prof. Dr. Haipeng Lan
Dr. Silvio José Gumiere
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. 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

  • artificial intelligence
  • digital gardening
  • automated management
  • non-destructive testing
  • intelligent perception

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Published Papers (5 papers)

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Research

20 pages, 5331 KiB  
Article
A Blueberry Maturity Detection Method Integrating Attention-Driven Multi-Scale Feature Interaction and Dynamic Upsampling
by Haohai You, Zhiyi Li, Zhanchen Wei, Lijuan Zhang, Xinhua Bi, Chunguang Bi, Xuefang Li and Yunpeng Duan
Horticulturae 2025, 11(6), 600; https://doi.org/10.3390/horticulturae11060600 - 27 May 2025
Abstract
In the context of blueberry orchard management and automated harvesting, this study introduces an improved YOLOv8 model, ADE-YOLO, designed for precise blueberry ripeness detection, enhancing automated picking efficiency. Built on the YOLOv8n architecture, ADE-YOLO features a dimensionality-reducing convolution at the backbone’s end, reducing [...] Read more.
In the context of blueberry orchard management and automated harvesting, this study introduces an improved YOLOv8 model, ADE-YOLO, designed for precise blueberry ripeness detection, enhancing automated picking efficiency. Built on the YOLOv8n architecture, ADE-YOLO features a dimensionality-reducing convolution at the backbone’s end, reducing computational complexity while optimizing input features. This improvement enhances the effectiveness of the AIFI module, particularly in multi-scale feature fusion, boosting detection accuracy and robustness. Additionally, the neck integrates a dynamic sampling technique, replacing traditional upsampling methods, allowing for more precise feature integration during feature transfer from P5 to P4 and P4 to P3. To further enhance computational efficiency, CIOU is replaced with EIOU, simplifying the aspect ratio penalty term while maintaining high accuracy in bounding box overlap and centroid distance calculations. Experimental results demonstrate ADE-YOLO’s strong performance in blueberry ripeness detection, achieving a precision of 96.49%, recall of 95.38%, and mAP scores of 97.56% (mAP50) and 79.25% (mAP50-95). The model is lightweight, with just 2.95 M parameters and a 6.2 MB weight file, outpacing YOLOv8n in these areas. ADE-YOLO’s design and performance underscore its significant application potential in blueberry orchard management, providing valuable support for precision agriculture. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations)
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15 pages, 3602 KiB  
Article
Non-Linear Models for Assessing Soil Moisture Estimation
by Rui Li, Susu Wang, Han Wu, Hao Dong, Dezhi Kong, Hanxue Li, Dorothy S. Zhang and Haitao Chen
Horticulturae 2025, 11(5), 492; https://doi.org/10.3390/horticulturae11050492 - 30 Apr 2025
Viewed by 162
Abstract
Accurately estimating soil moisture (SM) without direct measurements poses significant challenges due to nonlinear interactions in meteorological variables and the lagged response of SM to precipitation. This study evaluates two approaches: the auto-regressive integrated moving average (ARIMA) model for one-day-ahead SM forecasting and [...] Read more.
Accurately estimating soil moisture (SM) without direct measurements poses significant challenges due to nonlinear interactions in meteorological variables and the lagged response of SM to precipitation. This study evaluates two approaches: the auto-regressive integrated moving average (ARIMA) model for one-day-ahead SM forecasting and a K-means clustering-based multilayer perceptron (K-MLP) for real-time SM estimation at depths of 5 cm, 20 cm, and 50 cm in Changbai Mountain region. Although the K-MLP model outperformed the MLP model, achieving a maximum R2 of 0.728, its estimation accuracy remains suboptimal. By contrast, the ARIMA model effectively leveraged SM persistence, achieving high accuracy in one-day-ahead forecasting. Specifically, the ARIMA (0, 1, 6), ARIMA (1, 1, 2), and ARIMA (2, 1, 1) models yield R2 values of 0.9677, 0.9853, and 0.9684 and RMSE values of 0.02 m3·m3, 0.015 m3·m3, and 0.006 m3·m3 at depths of 5 cm, 20 cm, and 50 cm, respectively. This study explores ARIMA’s robustness in short-term SM forecasting and its adaptability to dynamic meteorological conditions, offering potential applications in agricultural water management and ecological monitoring. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations)
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15 pages, 1821 KiB  
Article
Study on Color Detection of Korla Fragrant Pears by Near-Infrared Spectroscopy Combined with PLSR
by Yifan Xia, Yang Liu, Hong Zhang, Jikai Che and Qing Liang
Horticulturae 2025, 11(4), 352; https://doi.org/10.3390/horticulturae11040352 - 25 Mar 2025
Viewed by 281
Abstract
The difficulty in controlling the quality of Korla pears is the main factor limiting their market value. The key to solving this problem is to detect the color of Korla pears quickly and accurately. This study employed near-infrared spectroscopy (NIRS) technology to measure [...] Read more.
The difficulty in controlling the quality of Korla pears is the main factor limiting their market value. The key to solving this problem is to detect the color of Korla pears quickly and accurately. This study employed near-infrared spectroscopy (NIRS) technology to measure the absorbance of Korla fragrant pears. The full-spectrum data were pre-processed using six methods: Savitzky–Golay convolution smoothing (SGCS), Savitzky–Golay convolution derivative (SGCD), multiplicative scatter correction (MSC), vector normalization (VN), min–max normalization (MMN), and standard normal variate transformation (SNV). The pre-processed spectral data were subjected to characteristic band extraction using the successive projections algorithm (SPA) and uninformative variable elimination (UVE) methods. Subsequently, detection models for the color indices L*, a*, and b* of Korla fragrant pears were established using the partial least squares regression (PLSR) with full-spectrum and characteristic extracted spectral data. The optimal detection models were determined. The results indicated that pre-processing and characteristic extraction improved the accuracy of the PLSR model. The optimal detection model for the color index L* was SGCD-UVE-PLSR (correlation coefficient (R) = 0.80, Root Mean Square Error (RMSE) = 1.19); for the color index a*, it was VN-SPA-PLSR (R = 0.84 and RMSE = 1.28), and for the color index b*, it was MSC-UVE-PLSR (R = 0.84 and RMSE = 1.25). This research provides a theoretical reference for developing color detection instruments for Korla fragrant pears. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations)
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17 pages, 2623 KiB  
Article
Exploring the Grape Agrivoltaic System: Climate Modulation and Vine Benefits in the Puglia Region, Southeastern Italy
by Andrea Magarelli, Andrea Mazzeo and Giuseppe Ferrara
Horticulturae 2025, 11(2), 160; https://doi.org/10.3390/horticulturae11020160 - 3 Feb 2025
Cited by 3 | Viewed by 1764
Abstract
Climate change poses significant challenges to agriculture, a sector with a long-standing tradition in the Mediterranean basin. The region faces altered rainfall patterns, extreme temperatures, aridification, loss of biodiversity, and changes in crop yield and quality. These impacts, combined with intensive farming practices, [...] Read more.
Climate change poses significant challenges to agriculture, a sector with a long-standing tradition in the Mediterranean basin. The region faces altered rainfall patterns, extreme temperatures, aridification, loss of biodiversity, and changes in crop yield and quality. These impacts, combined with intensive farming practices, threaten long-term agricultural sustainability. This study investigates agrivoltaics (AVs), a dual-use technology that integrates solar energy production (photovoltaic panels) with agriculture, as a potential solution to enhance resilience and adaptation of crops. Research at an AV system in Puglia (Southeastern Italy), combined with grapevine (Vitis vinifera L.), assessed soil moisture, temperature, and microclimate conditions together with vine yield and fruitfulness. Results showed that shading from photovoltaic panels increased soil moisture and moderated soil temperature, thus benefiting crops. Vines beneath the panels yielded more grapes (+277%) than in the full sun, confirmed by even the better bud fruitfulness of the shaded canes. While panels had minimal impact on air temperature, they reduced wind speed and vapor pressure deficit, creating a better microenvironment for vines. Spectral analysis revealed an increase in UV and blue light under the panels, potentially affecting photosynthesis. The AV system also produced substantial electricity, more than 90% compared to a ground-mounted system, demonstrating its dual-use application. The higher land equivalent ratio (LER) achieved by the AV system (3.54) confirmed that such systems can be advantageous in areas with a Mediterranean climate, allowing crop and energy production on the same land. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations)
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21 pages, 45821 KiB  
Article
OMC-YOLO: A Lightweight Grading Detection Method for Oyster Mushrooms
by Lei Shi, Zhanchen Wei, Haohai You, Jiali Wang, Zhuo Bai, Helong Yu, Ruiqing Ji and Chunguang Bi
Horticulturae 2024, 10(7), 742; https://doi.org/10.3390/horticulturae10070742 - 14 Jul 2024
Cited by 5 | Viewed by 1730
Abstract
In this paper, a lightweight model—OMC-YOLO, improved based on YOLOv8n—is proposed for the automated detection and grading of oyster mushrooms. Aiming at the problems of low efficiency, high costs, and the difficult quality assurance of manual operations in traditional oyster mushroom cultivation, OMC-YOLO [...] Read more.
In this paper, a lightweight model—OMC-YOLO, improved based on YOLOv8n—is proposed for the automated detection and grading of oyster mushrooms. Aiming at the problems of low efficiency, high costs, and the difficult quality assurance of manual operations in traditional oyster mushroom cultivation, OMC-YOLO was improved based on the YOLOv8n model. Specifically, the model introduces deeply separable convolution (DWConv) into the backbone network, integrates the large separated convolution kernel attention mechanism (LSKA) and Slim-Neck structure into the Neck part, and adopts the DIoU loss function for optimization. The experimental results show that on the oyster mushroom dataset, the OMC-YOLO model had a higher detection effect compared to mainstream target detection models such as Faster R-CNN, SSD, YOLOv3-tiny, YOLOv5n, YOLOv6, YOLOv7-tiny, YOLOv8n, YOLOv9-gelan, YOLOv10n, etc., and that the mAP50 value reached 94.95%, which is an improvement of 2.62%. The number of parameters and the computational amount were also reduced by 26%. The model provides technical support for the automatic detection of oyster mushroom grades, which helps in realizing quality control and reducing labor costs and has positive significance for the construction of smart agriculture. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations)
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