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Horticulturae
Special Issues
Plant-Based, Proximal, and Remote Sensing Techniques in Horticultural Crop Production
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Plant-Based, Proximal, and Remote Sensing Techniques in Horticultural Crop Production

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

Deadline for manuscript submissions: 20 March 2026 | Viewed by 1692

Special Issue Editors

Dr. Fabricio Macedo

E-Mail Website
Guest Editor
1. ISOPlexis Centre of Sustainable Agriculture and Food Technology, University of Madeira, Campus da Penteada, 9020-105 Funchal, Portugal
2. Centre for the Research and Technology of Agroenvironmental and Biological Sciences, CITAB, Inov4Agro, Universidade de Trás-os-Montes e Alto Douro, UTAD, Quinta de Prados, 5000-801 Vila Real, Portugal
Interests: remote sensing; precision agriculture; organic production; crop modeling
Dr. Adélia Sousa

E-Mail Website
Guest Editor
MED—Mediterranean Institute of Agriculture, Environment and Development & CHANGE—Global Change and Sustainability Institute, Remote Sensing Laboratory—EaRSLab, Department of Rural Engineering, School of Science and Technology, University of Évora, Ap. 94, 7002-544 Évora, Portugal
Interests: remote sensing; precision agriculture; biomass estimation; crop monitoring and modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of remote sensing, precision agriculture, and digital technologies is revolutionizing the monitoring, management, and optimization of agricultural systems. In the face of increasing global demands for sustainable and resilient food production, innovative approaches that combine geospatial data, unmanned aerial vehicles (UAVs), Internet of Things (IoT), and machine learning are becoming indispensable.

Remote sensing and digital tools are now being applied across a wide range of agricultural domains, from crop monitoring and irrigation management to soil analysis, early disease detection, and yield prediction. These technologies enable more accurate, data-driven decisions that support both productivity and sustainability.

The aim of this Special Issue is to bring together recent advances and practical applications of remote sensing and digital technologies in agriculture. We encourage submissions that showcase novel methodologies, sensor integration, modeling approaches, and data analysis techniques that contribute to improving the efficiency and environmental performance of agri-food systems.

Particularly welcome are interdisciplinary studies and case studies that explore applications across diverse agroecosystems and demonstrate real-world impact.

We invite original research articles, reviews, and case studies focusing on, but not limited to, the following topics:

  • Remote sensing for crop monitoring and modeling;
  • UAV and IoT applications in precision agriculture;
  • Irrigation optimization through digital technologies;
  • Soil property assessment and spatial variability analysis;
  • Early detection of pests and diseases;
  • Yield estimation and predictive modeling;
  • Integration of geospatial and ground-based data for agricultural decision-making;
  • Contributions to sustainable agri-food systems through digital innovation.

We look forward to receiving your contributions to this Special Issue.

Dr. Fabricio Macedo
Dr. Adélia Sousa
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

  • remote sensing
  • precision agriculture
  • UAV and IoT applications
  • crop monitoring and modeling
  • sustainable agri-food systems

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

Published Papers (3 papers)

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Research

19 pages, 2788 KB  
Article
Universal Image Segmentation with Arbitrary Granularity for Efficient Pest Monitoring
by L. Minh Dang, Sufyan Danish, Muhammad Fayaz, Asma Khan, Gul E. Arzu, Lilia Tightiz, Hyoung-Kyu Song and Hyeonjoon Moon
Horticulturae 2025, 11(12), 1462; https://doi.org/10.3390/horticulturae11121462 - 3 Dec 2025
Abstract
Accurate and timely pest monitoring is essential for sustainable agriculture and effective crop protection. While recent deep learning-based pest recognition systems have significantly improved accuracy, they are typically trained for fixed label sets and narrowly defined tasks. In this paper, we present RefPestSeg, [...] Read more.
Accurate and timely pest monitoring is essential for sustainable agriculture and effective crop protection. While recent deep learning-based pest recognition systems have significantly improved accuracy, they are typically trained for fixed label sets and narrowly defined tasks. In this paper, we present RefPestSeg, a universal, language-promptable segmentation model specifically designed for pest monitoring. RefPestSeg can segment targets at any semantic level, such as species, genus, life stage, or damage type, conditioned on flexible natural language instructions. The model adopts a symmetric architecture with self-attention and cross-attention mechanisms to tightly align visual features with language embeddings in a unified feature space. To further enhance performance in challenging field conditions, we integrate an optimized super-resolution module to improve image quality and employ diverse data augmentation strategies to enrich the training distribution. A lightweight postprocessing step refines segmentation masks by suppressing highly overlapping regions and removing noise blobs introduced by cluttered backgrounds. Extensive experiments on a challenging pest dataset show that RefPestSeg achieves an Intersection over Union (IoU) of 69.08 while maintaining robustness in real-world scenarios. By enabling language-guided pest segmentation, RefPestSeg advances toward more intelligent, adaptable monitoring systems that can respond to real-time agricultural demands without costly model retraining. Full article
(This article belongs to the Special Issue Plant-Based, Proximal, and Remote Sensing Techniques in Horticultural Crop Production)
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30 pages, 3386 KB  
Article
Explainable AI for Predicting Latent Period and Infection Stage Progression in Tomato Fungal Diseases
by Haiyan Gu, Seyed Mohamad Javidan, Yiannis Ampatzidis and Zhao Zhang
Horticulturae 2025, 11(11), 1376; https://doi.org/10.3390/horticulturae11111376 - 14 Nov 2025
Viewed by 390
Abstract
Accurate prediction of the latent period and disease progression in tomato fungal infections is critical for enabling timely interventions and effective disease management. Unlike existing AI-based approaches that primarily classify diseases after symptom emergence, this study innovates by predicting infection stages from the [...] Read more.
Accurate prediction of the latent period and disease progression in tomato fungal infections is critical for enabling timely interventions and effective disease management. Unlike existing AI-based approaches that primarily classify diseases after symptom emergence, this study innovates by predicting infection stages from the asymptomatic (latent) phase through complete symptom development, integrating biologically grounded feature extraction with explainable artificial intelligence (XAI). This study presents a novel, XAI framework capable of day-wise prediction of infection stages, including the latent period, for four major fungal pathogens in tomatoes: Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum. A high-resolution (Red-Green-Blue) RGB image dataset was collected under controlled inoculation conditions, capturing daily changes in infected and healthy tomato leaves over six days post-infection. The pipeline included image preprocessing, lesion segmentation, and extraction of biologically meaningful features (texture, color, and shape) reflecting underlying physiological changes in the plant. Feature relevance across infection stages was dynamically assessed using the Relief algorithm, providing interpretability by linking visual changes to disease biology. Machine learning classifiers, Support Vector Machine (SVM) and Random Forest (RF), were optimized using Particle Swarm Optimization (PSO), achieving significant improvements in infection day prediction accuracy across all four pathogens. For example, RF accuracy increased from 76.14% to 94.17% for A. alternata (with 97.96% sensitivity and 99.48% specificity on day 6 post-inoculation) and from 80.01% to 97.08% for B. cinerea. Critically, the model accurately identified the latent period for each pathogen, detecting microscopic texture changes on day 1 post-inoculation when no visible symptoms were present. By bridging the gap between AI and plant pathology, this framework enables early diagnosis of fungal diseases with explainable outputs. The approach offers a scalable, non-destructive, and biologically grounded tool for integrated disease management, with potential applications across diverse crops in precision agriculture. Full article
(This article belongs to the Special Issue Plant-Based, Proximal, and Remote Sensing Techniques in Horticultural Crop Production)
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20 pages, 5095 KB  
Article
Leveraging Multispectral and 3D Phenotyping to Determine Morpho-Physiological Changes in Peppers Under Increasing Drought Stress Levels
by Annalisa Cocozza, Accursio Venezia, Rosaria Macellaro, Carlo Di Cesare, Chiara Milanesi and Pasquale Tripodi
Horticulturae 2025, 11(11), 1318; https://doi.org/10.3390/horticulturae11111318 - 3 Nov 2025
Viewed by 491
Abstract
The expected population rise will require a maximum exploitation of agricultural lands with a consequent increase in the demand for freshwater for irrigation uses. Future trends predict increasing periods of drought stress, which may impact on crop performance and limit the future production. [...] Read more.
The expected population rise will require a maximum exploitation of agricultural lands with a consequent increase in the demand for freshwater for irrigation uses. Future trends predict increasing periods of drought stress, which may impact on crop performance and limit the future production. Pepper is one of the most economically important crops and globally consumed vegetables. This crop is highly demanding in terms of water supply, and so far, developing tolerant cultivars is one of the main targets for breeding. The aim of this study is to accurately determine how pepper plants react to water stress at the vegetative stage in order to select genotypes that better cope with drought. We implemented the PhenoHort Plant Eye phenotyping platform to precisely assess changes in plant architecture and morpho-physiological parameters on 25 cultivated pepper genotypes (Capsicum annuum) under drought stress conditions. Three different irrigation supply levels were considered, including the control, intense, and severe water stress, by irrigating every 24, 72, and 96 h, respectively. Daily monitoring of 20 traits allowed ~190,000 multispectral and tridimensional data points through scans over 6 weeks of cultivation, thus shedding light on changes in plant architecture and vegetation indices’ values during stress. The dissection of genotype (G) and treatment (T) interactions revealed that digital biomass and plant height traits were strongly affected by the T factor (more than 50% of total variance), whereas color and multispectral parameters were under greater genotypic control, accounting for 58.27% and 64.97% of the total variance for HUE and NPCI, respectively. The comparison of each accession with respect to the control and the application of multivariate models allowed us to select four drought-tolerant lines (G1, G2, G22, and G25) able to reduce the effects of drought on the morphological parameters and architecture of the plant with positive effects on vegetative indices. This work represents the first attempt to dissect the response of pepper under drought stress at the vegetative stage using a high-throughput and non-invasive phenotyping system, offering new insights for selecting resilient genotypes. Full article
(This article belongs to the Special Issue Plant-Based, Proximal, and Remote Sensing Techniques in Horticultural Crop Production)
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