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Algorithms
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Data Sensing Techniques and Processing Algorithms for Smart and Sustainable...
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Data Sensing Techniques and Processing Algorithms for Smart and Sustainable Agriculture

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 5132

Special Issue Editors

Dr. Telmo Adão

E-Mail Website
Guest Editor
Department of Engineering, School of Sciences and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: computer graphics; extended reality; computer vision; artificial intelligence; deep learning; data synthetization engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Emanuel Peres

E-Mail Website
Guest Editor
Department of Engineering, School of Sciences and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: remote sensing; precision agriculture; in-field data processing; remote monitoring; UAV; UAS; precision forestry; sensors and data processing; human–computer interfaces; augmented reality; virtual reality; embedded systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technological developments have enabled agriculture to become smarter through the integration of a diverse array of sensing instruments and techniques used to measure, monitor, and manage crop and soil conditions. From proximal IoT devices to aerial and satellite-based remote sensing platforms, data is captured in various formats, across multiple resolutions and spectral ranges.

While these technologies generate vast amounts of raw data, their true value lies in how that data is processed and transformed into actionable insights. Effective data processing is essential to address critical agricultural challenges such as irrigation management, phenological monitoring, pest and disease detection, and adaptation to climate variability. Techniques may span from traditional statistical approaches to advanced artificial intelligence and machine learning models. Whether through one-time classifications or multitemporal analyses, these methods empower data-driven decision-making, ultimately contributing to more sustainable, efficient, and responsive farming practices.

This Special Issue seeks to highlight innovative research in data sensing and processing for agriculture. Submissions focused on the analysis, interpretation, and application of agricultural data using both conventional and cutting-edge techniques are welcomed. Topics of interest include, but are not limited to, the following:

  • Internet of Things (IoT);
  • Remote sensing (aerial, satellite, and UAV);
  • Data processing and transformation techniques;
  • Image processing and computer vision applications;
  • Mathematical and statistical methods for data filtering, simplification, and pattern recognition;
  • Artificial intelligence, machine learning, and deep learning;
  • Convolutional neural networks (CNNs);
  • Vision transformers (ViTs);
  • Data classification and object detection;
  • Time-series analysis;
  • Decision support systems and platforms.

Dr. Telmo Adão
Dr. Emanuel Peres
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. Algorithms 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 1800 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

  • Internet of Things (IoT)
  • remote sensing (aerial, satellite, UAV)
  • data processing and transformation techniques
  • image processing and computer vision applications
  • mathematical and statistical methods for data filtering, simplification, and pattern recognition
  • artificial intelligence, machine learning, and deep learning
  • convolutional neural networks (CNNs)
  • vision transformers (ViTs)
  • data classification and object detection
  • time-series analysis
  • decision support systems and platforms

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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

32 pages, 999 KB  
Article
A Robust Hybrid Metaheuristic Framework for Training Support Vector Machines
by Khalid Nejjar, Khalid Jebari and Siham Rekiek
Algorithms 2026, 19(1), 70; https://doi.org/10.3390/a19010070 - 13 Jan 2026
Viewed by 166
Abstract
Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the [...] Read more.
Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the efficiency of the optimization algorithm used to solve their underlying dual problem, which is often complex and constrained. Classical solvers, such as Sequential Minimal Optimization (SMO) and Stochastic Gradient Descent (SGD), present inherent limitations: SMO ensures numerical stability but lacks scalability and is sensitive to heuristics, while SGD scales well but suffers from unstable convergence and limited suitability for nonlinear kernels. To address these challenges, this study proposes a novel hybrid optimization framework based on Open Competency Optimization and Particle Swarm Optimization (OCO–PSO) to enhance the training of SVMs. The proposed approach combines the global exploration capability of PSO with the adaptive competency-based learning mechanism of OCO, enabling efficient exploration of the solution space, avoidance of local minima, and strict enforcement of dual constraints on the Lagrange multipliers. Across multiple datasets spanning medical (diabetes), agricultural yield, signal processing (sonar and ionosphere), and imbalanced synthetic data, the proposed OCO-PSO–SVM consistently outperforms classical SVM solvers (SMO and SGD) as well as widely used classifiers, including decision trees and random forests, in terms of accuracy, macro-F1-score, Matthews correlation coefficient (MCC), and ROC-AUC. On the Ionosphere dataset, OCO-PSO achieves an accuracy of 95.71%, an F1-score of 0.954, and an MCC of 0.908, matching the accuracy of random forest while offering superior interpretability through its kernel-based structure. In addition, the proposed method yields a sparser model with only 66 support vectors compared to 71 for standard SVC (a reduction of approximately 7%), while strictly satisfying the dual constraints with a near-zero violation of 1.3×10−3. Notably, the optimal hyperparameters identified by OCO-PSO (C=2, γ≈0.062) differ substantially from those obtained via Bayesian optimization for SVC (C=10, γ≈0.012), indicating that the proposed approach explores alternative yet equally effective regions of the hypothesis space. The statistical significance and robustness of these improvements are confirmed through extensive validation using 1000 bootstrap replications, paired Student’s t-tests, Wilcoxon signed-rank tests, and Holm–Bonferroni correction. These results demonstrate that the proposed metaheuristic hybrid optimization framework constitutes a reliable, interpretable, and scalable alternative for training SVMs in complex and high-dimensional classification tasks. Full article
(This article belongs to the Special Issue Data Sensing Techniques and Processing Algorithms for Smart and Sustainable Agriculture)
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19 pages, 7913 KB  
Article
Integrated Satellite Driven Machine Learning Framework for Precision Irrigation and Sustainable Cotton Production
by Syeda Faiza Nasim and Muhammad Khurram
Algorithms 2025, 18(12), 740; https://doi.org/10.3390/a18120740 - 25 Nov 2025
Viewed by 623
Abstract
This study develops a satellite-based, machine-learning-based prediction algorithm to predict optimal irrigation scheduling for cotton cultivation within Rahim Yar Khan, Pakistan. The framework leverages multispectral satellite imagery (Landsat 8 and Sentinel-2), GIS-derived climatic, land surface data and real-time weather information obtained from a [...] Read more.
This study develops a satellite-based, machine-learning-based prediction algorithm to predict optimal irrigation scheduling for cotton cultivation within Rahim Yar Khan, Pakistan. The framework leverages multispectral satellite imagery (Landsat 8 and Sentinel-2), GIS-derived climatic, land surface data and real-time weather information obtained from a freely accessible weather API, eliminating the need for ground-based IoT sensors. The proposed algorithm integrates FAO-56 evapotranspiration principles and water stress indices to accurately forecast irrigation requirements across the four critical growth stages of cotton. Supervised learning algorithms, including Gradient Boosting, Random Forest, and Logistic Regression, were evaluated, with Random Forest indicating better predictive accuracy with a coefficient of determination (R2) exceeding 0.92 and a root mean square error (RMSE) of approximately 415 kg/ha, owed its capacity to handle complex, non-linear relations, and feature interactions. The model was trained on data collected during 2023 and 2024, and its predictions for 2025 were validated against observed irrigation requirements. The proposed model enabled an average 12–18% reduction in total water application between 2023 and 2025, optimizing water use deprived of compromising crop yield. By merging satellite imagery, GIS data, and weather API information, this approach provides a cost-effective, scalable solution that enables precise, stage-specific irrigation scheduling. Cloud masking was executed by applying the built-in QA bands with the Fmask algorithm to eliminate cloud and cloud-shadow pixels in satellite imagery statistics. Time series were generated by compositing monthly median values to ensure consistency across images. The novelty of our study primarily focuses on its end-to-end integration framework, its application within semi-arid agronomic conditions, and its empirical validation and accuracy calculation over direct association of multi-source statistics with FAO-guided irrigation scheduling to support sustainable cotton cultivation. The quantification of irrigation capacity, determining how much water to apply, is identified as a focus for future research. Full article
(This article belongs to the Special Issue Data Sensing Techniques and Processing Algorithms for Smart and Sustainable Agriculture)
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28 pages, 45524 KB  
Article
A Comparative Analysis of U-Net Architectures with Dimensionality Reduction for Agricultural Crop Classification Using Hyperspectral Data
by Georgios Dimitrios Gkologkinas, Konstantinos Ntouros, Eftychios Protopapadakis and Ioannis Rallis
Algorithms 2025, 18(9), 588; https://doi.org/10.3390/a18090588 - 17 Sep 2025
Cited by 1 | Viewed by 1852
Abstract
The inherent high dimensionality of hyperspectral imagery presents both opportunities and challenges for agricultural crop classification. This study offers a rigorous comparative evaluation of three U-Net-based architectures, i.e., U-Net, U-Net++, and Atrous U-Net, applied to EnMAP hyperspectral data over the heterogeneous agricultural region [...] Read more.
The inherent high dimensionality of hyperspectral imagery presents both opportunities and challenges for agricultural crop classification. This study offers a rigorous comparative evaluation of three U-Net-based architectures, i.e., U-Net, U-Net++, and Atrous U-Net, applied to EnMAP hyperspectral data over the heterogeneous agricultural region of Lake Vegoritida, Greece. To address the spectral redundancy, we integrated multiple dimensionality-reduction strategies, including Linear Discriminant Analysis, SHAP-based model-driven feature selection, and unsupervised clustering approaches. Results reveal that model performance is contingent on (a) the network’s architecture and (b) the features’ space provided by band selection. While U-Net++ consistently excels when the full spectrum or ACS-derived subsets are employed, standard U-Net achieves great performance under LDA reduction, and Atrous U-Net benefits from SHAP-driven compact representations. Importantly, band selection methods such as ACS and SHAP substantially reduce spectral dimensionality without sacrificing accuracy, with the U-Net++–ACS configuration delivering the highest F1-score (0.77). These findings demonstrate that effective hyperspectral crop classification requires a joint optimization of architecture and spectral representation, underscoring the potential of compact, interpretable pipelines for scalable and operational precision agriculture. Full article
(This article belongs to the Special Issue Data Sensing Techniques and Processing Algorithms for Smart and Sustainable Agriculture)
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28 pages, 8417 KB  
Article
Democratizing IoT for Smart Irrigation: A Cost-Effective DIY Solution Proposal Evaluated in an Actinidia Orchard
by David Pascoal, Telmo Adão, Agnieszka Chojka, Nuno Silva, Sandra Rodrigues, Emanuel Peres and Raul Morais
Algorithms 2025, 18(9), 563; https://doi.org/10.3390/a18090563 - 5 Sep 2025
Cited by 2 | Viewed by 1964
Abstract
Proper management of water resources in agriculture is of utmost importance for sustainable productivity, especially under the current context of climate change. However, many smart agriculture systems, including for managing irrigation, involve costly, complex tools for most farmers, especially small/medium-scale producers, despite the [...] Read more.
Proper management of water resources in agriculture is of utmost importance for sustainable productivity, especially under the current context of climate change. However, many smart agriculture systems, including for managing irrigation, involve costly, complex tools for most farmers, especially small/medium-scale producers, despite the availability of user-friendly and community-accessible tools supported by well-established providers (e.g., Google). Hence, this paper proposes an irrigation management system integrating low-cost Internet of Things (IoT) sensors with community-accessible cloud-based data management tools. Specifically, it resorts to sensors managed by an ESP32 development board to monitor several agroclimatic parameters and employs Google Sheets for data handling, visualization, and decision support, assisting operators in carrying out proper irrigation procedures. To ensure reproducibility for both digital experts but mainly non-technical professionals, a comprehensive set of guidelines is provided for the assembly and configuration of the proposed irrigation management system, aiming to promote a democratized dissemination of key technical knowledge within a do-it-yourself (DIY) paradigm. As part of this contribution, a market survey identified numerous e-commerce platforms that offer the required components at competitive prices, enabling the system to be affordably replicated. Furthermore, an irrigation management prototype was tested in a real production environment, consisting of a 2.4-hectare yellow kiwi orchard managed by an association of producers from July to September 2021. Significant resource reductions were achieved by using low-cost IoT devices for data acquisition and the capabilities of accessible online tools like Google Sheets. Specifically, for this study, irrigation periods were reduced by 62.50% without causing water deficits detrimental to the crops’ development. Full article
(This article belongs to the Special Issue Data Sensing Techniques and Processing Algorithms for Smart and Sustainable Agriculture)
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