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Horticulturae
Special Issues
Emerging Technologies in Smart Agriculture
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Emerging Technologies in Smart Agriculture

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 6910

Special Issue Editors

Dr. Luís Pádua

E-Mail Website
Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: remote sensing; unmanned aerial vehicles; precision viticulture; precision agriculture; multi-temporal analysis; spectral imaging; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Anabela A. Fernandes-Silva

E-Mail Website
Guest Editor
Agronomy Department, Scholl of Agrarian and Veterinary Sciences, Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes e Alto Douro CITAB-Quinta de Prados, 5000-801 Vila Real, Portugal
Interests: irrigation and water management; drip irrigation; water use efficiency; evapotranspiration; plant water relations; drought; agrometeorology; plant based sensors; precision irrigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart agriculture, driven by the integration of advanced technologies, is redesigning the agricultural landscape to meet the growing demands for food security, sustainability, and climate resilience. This Special Issue delves into the transformative role of emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning, robotics, and precision farming in modern agriculture. These innovations are revolutionizing traditional farming practices by providing real-time data analytics, automating labor-intensive processes, and optimizing the use of resources like water, fertilizers, and pesticides.

Emerging techniques in vertical farming, hydroponics, and smart irrigation systems further push the boundaries of sustainable agricultural practices by conserving water, reducing land use, and promoting year-round crop production. Precision agriculture, utilizing satellite imagery, GPS, and unmanned aerial vehicles (UAVs), enables site-specific management practices, reducing environmental impacts while boosting productivity.

This special issue aims to showcase cutting-edge research, innovative applications, and case studies that demonstrate the practical impact and potential of these technologies. Additionally, it addresses key challenges such as technological adoption barriers, data privacy concerns, and the economic viability of implementing smart agriculture solutions. By fostering a deeper understanding of these technologies, the issue seeks to chart a sustainable path forward for the global agricultural sector in an era of increasing environmental and economic pressures.

Dr. Luís Pádua
Dr. Anabela A. Fernandes-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

  • precision farming
  • internet of things (IoT)
  • remote sensing
  • crop monitoring
  • controlled-environment agriculture
  • vertical farming

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

Published Papers (5 papers)

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Research

20 pages, 71492 KB  
Article
An Edge-Oriented RT-DETR Integrated with Efficient Feature Extraction and Fusion Architecture and Lightweight Processing for Blueberry Maturity Detection
by Lei Shi, Zhuo Bai, Yinyi Zhang, Shuai Wang, Qiyuan Fu, Ziyue Li, Yuhang Cui, Yiman Dong, Zhiyin Yang and Yuxin Ye
Horticulturae 2026, 12(6), 664; https://doi.org/10.3390/horticulturae12060664 - 25 May 2026
Viewed by 406
Abstract
To address challenges such as severe occlusion caused by the dense growth of blueberry fruits in natural environments, complex backgrounds, and the limited computational resources of agricultural edge devices, this study proposes BR-DETR-Prune, a lightweight object detection model oriented towards edge computing environments. [...] Read more.
To address challenges such as severe occlusion caused by the dense growth of blueberry fruits in natural environments, complex backgrounds, and the limited computational resources of agricultural edge devices, this study proposes BR-DETR-Prune, a lightweight object detection model oriented towards edge computing environments. Based on the RT-DETR architecture, the model introduces a PConv-based FasterNet as the backbone network, which effectively reduces memory access latency and floating-point operation costs. Furthermore, it utilizes a “Gather-and-Distribute” (GD) mechanism to reconstruct the feature fusion neck. Through the unified aggregation and multi-branch distribution of global information, it significantly enhances the model’s feature extraction capability for dense and overlapping targets. An AIFI-RepBN encoder is designed, integrating re-parameterization technology into the attention module to further reduce computational redundancy. For lightweight processing, a random channel pruning strategy based on the “Lottery Ticket Hypothesis” is adopted to perform structural compression and fine-tuning on the model, achieving a significant reduction in the number of parameters while inversely improving accuracy. The experimental results demonstrate that BR-DETR-Prune achieves an mAP@0.5 of 97.1% on a self-built blueberry dataset, with only 15.52 M parameters and a computational load reduced to 34.0 GFLOPs. Its comprehensive performance is superior to mainstream models such as YOLOv8, YOLO11, and the original RT-DETR. Particularly, deployment testing on the NVIDIA Jetson Orin Nano Super embedded edge computing platform reveals that the model achieves a real-time inference speed of 20.5 FPS under FP16 precision, exhibiting smooth detection frames and strong robustness against occlusion. This study provides an effective optimization solution for the deployment of high-precision Transformer architectures on low-computational-power devices, offering an efficient and reliable visual perception approach for automated blueberry harvesting and yield estimation. Full article
(This article belongs to the Special Issue Emerging Technologies in Smart Agriculture)
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16 pages, 13549 KB  
Article
YOLO-ALD: An Efficient and Robust Lightweight Model for Apple Leaf Disease Detection in Complex Orchard Environments
by Lei Liu, Yinyin Li, Qingyu Liu, Huihui Sun, Yeguo Sun and Xiaobo Shen
Horticulturae 2026, 12(5), 550; https://doi.org/10.3390/horticulturae12050550 - 30 Apr 2026
Viewed by 1607
Abstract
Real-time detection of apple leaf diseases in orchard environments faces ongoing challenges, particularly in preserving fine-grained disease features with limited computing resources. To address these issues, we propose a high-precision lightweight model based on YOLOv10n, called YOLO-ALD. First, we introduce Spatial and Channel [...] Read more.
Real-time detection of apple leaf diseases in orchard environments faces ongoing challenges, particularly in preserving fine-grained disease features with limited computing resources. To address these issues, we propose a high-precision lightweight model based on YOLOv10n, called YOLO-ALD. First, we introduce Spatial and Channel Reconstruction Convolution into deeper backbone networks to replace standard downsampling layers and convolutions. This suppresses spatial and channel redundancy caused by environmental noise and optimizes feature representation. Second, we design a new C2f-Faster-SimAM module for the neck network. This module combines the inference efficiency of FasterNet with a parameter-free 3D attention mechanism to adaptively focus on early lesions, effectively distinguishing them from leaf veins without increasing model complexity. Third, in the detection head section, we use the Focaler-ShapeIoU loss function to optimize bounding box regression. It utilizes a dynamic focusing mechanism and geometric constraints to ensure the localization accuracy of irregular shapes and hard-to-detect samples. Experimental results on our self-built dataset covering four specific diseases and healthy leaves showed that, compared with YOLOv10n, the mAP@0.5 of YOLO-ALD reached 92.1%, achieving a 2.1% increase. In addition, the model has an inference speed of 105 FPS, with only 2.1 M parameters and 5.6 GFLOPs. Therefore, YOLO-ALD achieves a good balance between efficiency and robustness, showing strong theoretical potential for resource-constrained mobile agriculture diagnosis. Full article
(This article belongs to the Special Issue Emerging Technologies in Smart Agriculture)
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15 pages, 2853 KB  
Article
Automatic Fruit Size Evaluation System Based on LabVIEW
by Runqiang Xu, Yuhua Li, Zhilong Zhang, Jiawei Wang, Qingzhong Liu and Dongzi Zhu
Horticulturae 2025, 11(11), 1341; https://doi.org/10.3390/horticulturae11111341 - 7 Nov 2025
Viewed by 1101
Abstract
Fruit size is a key trait in small-fruit breeding, yet its measurement remains labor intensive and prone to human error. To address this, we developed a non-destructive, automated size measurement system based on machine vision and LabVIEW, designed for small fruits such as [...] Read more.
Fruit size is a key trait in small-fruit breeding, yet its measurement remains labor intensive and prone to human error. To address this, we developed a non-destructive, automated size measurement system based on machine vision and LabVIEW, designed for small fruits such as cherry, blueberry, and walnut. The system integrates a modular architecture, including flat-field correction, calibration, and pattern matching sub-VIs, to ensure user-friendly operation. These sub-VIs also enable the system’s core data analysis, such as real-size conversion through calibration and noise reduction for data accuracy through flat-field correction. An optimized image processing pipeline (grayscale conversion, Canny edge detection, morphological operations) enables precise contour extraction, even for fruits with stems or irregular surfaces. The system supports multi-species adaptation through lightweight parameter adjustments, without hardware modification. Experiments involved 15 samples per species (cherry ‘Tieton’, blueberry ‘Northland’, walnut ‘Xiangling’). A gold-standard protocol was established using a pre-calibrated digital caliper operated by two experienced technicians, with the mean of six replicates per fruit defined as the true value. Results demonstrated low root mean square errors, with coefficients of determination (R2) exceeding 0.98. Paired t-tests confirmed no significant differences from the gold standard. The system achieved a measurement speed of 0.4 s per fruit, six times faster than manual methods, and complied with the precision requirements of GB/T 26906-2024 (Sweet Cherry). This system offers a cost-effective, high-throughput solution for fruit breeding and phenotyping, effectively overcoming the limitations of manual measurement. Full article
(This article belongs to the Special Issue Emerging Technologies in Smart Agriculture)
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30 pages, 125846 KB  
Article
Optimizing Plant Production Through Drone-Based Remote Sensing and Label-Free Instance Segmentation for Individual Plant Phenotyping
by Ruth Hofman, Joris Mattheijssens, Johan Van Huylenbroeck, Jan Verwaeren and Peter Lootens
Horticulturae 2025, 11(9), 1043; https://doi.org/10.3390/horticulturae11091043 - 2 Sep 2025
Viewed by 1603
Abstract
A crucial initial step for the automatic extraction of plant traits from imagery is the segmentation of individual plants. This is typically performed using supervised deep learning (DL) models, which require the creation of an annotated dataset for training, a time-consuming and labor-intensive [...] Read more.
A crucial initial step for the automatic extraction of plant traits from imagery is the segmentation of individual plants. This is typically performed using supervised deep learning (DL) models, which require the creation of an annotated dataset for training, a time-consuming and labor-intensive process. In addition, the models are often only applicable to the conditions represented in the training data. In this study, we propose a pipeline for the automatic extraction of plant traits from high-resolution unmanned aerial vehicle (UAV)-based RGB imagery, applying Segment Anything Model 2.1 (SAM 2.1) for label-free segmentation. To prevent the segmentation of irrelevant objects such as soil or weeds, the model is guided using point prompts, which correspond to local maxima in the canopy height model (CHM). The pipeline was used to measure the crown diameter of approximately 15000 ball-shaped chrysanthemums (Chrysanthemum morifolium (Ramat)) in a 6158 m2 field on two dates. Nearly all plants were successfully segmented, resulting in a recall of 96.86%, a precision of 99.96%, and an F1 score of 98.38%. The estimated diameters showed strong agreement with manual measurements. The results demonstrate the potential of the proposed pipeline for accurate plant trait extraction across varying field conditions without the need for model training or data annotation. Full article
(This article belongs to the Special Issue Emerging Technologies in Smart Agriculture)
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28 pages, 2881 KB  
Article
Segmentation-Based Classification of Plants Robust to Various Environmental Factors in South Korea with Self-Collected Database
by Ganbayar Batchuluun, Seung Gu Kim, Jung Soo Kim and Kang Ryoung Park
Horticulturae 2025, 11(7), 843; https://doi.org/10.3390/horticulturae11070843 - 17 Jul 2025
Viewed by 1246
Abstract
Many plant image-based studies primarily use datasets featuring either a single plant, a plant with only one leaf, or images containing only plants and leaves without any background. However, in real-world scenarios, a substantial portion of acquired images consists of blurred plants or [...] Read more.
Many plant image-based studies primarily use datasets featuring either a single plant, a plant with only one leaf, or images containing only plants and leaves without any background. However, in real-world scenarios, a substantial portion of acquired images consists of blurred plants or extensive backgrounds rather than high-resolution details of the target plants. In such cases, classification models struggle to identify relevant areas for classification, leading to insufficient information and reduced classification performance. Moreover, the presence of moisture, water droplets, dust, or partially damaged leaves further degrades classification accuracy. To address these challenges and enhance classification performance, this study introduces a plant image segmentation (Pl-ImS) model for segmentation and a plant image classification (Pl-ImC) model for classification. The proposed models were evaluated using a self-collected dataset of 21,760 plant images captured under real field conditions in South Korea, incorporating various environmental factors such as moisture, water droplets, dust, and partial leaf loss. The segmentation method achieved a dice score (DS) of 89.90% and an intersection over union (IoU) of 81.82%, while the classification method attained an F1-score of 95.97%, surpassing state-of-the-art methods. Full article
(This article belongs to the Special Issue Emerging Technologies in Smart Agriculture)
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