Detecting water in agricultural landscapes from remote sensing imagery: Methodological choices, sensor constraints and performance metrics D.A. Merchán, J.M. Manzano, C. Ierardi Agricultural Water Management, 2026 Monitoring surface water in agricultural landscapes is a key requirement for irrigation management, leak detection, and sustainable water use. Although remote sensing literature extensively addresses water detection, most studies focus on large-scale Surface Water Mapping (SWM) in heterogeneous landscapes, where extensive water bodies such as lakes, rivers, or coastal zones occupy a substantial portion of the scene. In contrast, fine-scale water detection in agricultural environments typically involves small, fragmented, and highly imbalanced targets embedded within predominantly vegetated or cultivated areas. As a result, methods and performance metrics developed for large-scale mapping cannot be directly transferred to fine-scale agricultural scenarios without adaptation. This paper analyses 49 peer-reviewed studies published between 2020 and 2025 that address water detection in agricultural and rural contexts using multispectral, thermal, and Synthetic Aperture Radar (SAR) imagery from satellite and Unmanned Aerial Vehicles (UAV) platforms. Rather than providing a purely descriptive review, the work examines how methodological choices — ranging from spectral indices and decision trees to machine learning, deep learning, and foundation models — interact with sensor characteristics, processing levels, and evaluation metrics. The analysis highlights systematic trade-offs among model complexity, data availability, and robustness, identifies recurrent limitations in multiple accuracy metrics in scenarios where land pixels vastly outnumber water pixels, and synthesizes the practical implications of spectral band selection (VNIR, SWIR, TIR) and platform resolution. A central contribution of this review is the demonstration that, in agricultural water detection, preprocessing choices, sensor characteristics, and the use of appropriate evaluation metrics often have a greater influence on reported performance than the complexity of the detection algorithm itself. Based on these findings, the paper offers comparative insights and methodological recommendations to guide the selection and validation of water-detection approaches in agricultural remote sensing applications. • Review (2020–2025) of fine-scale water detection using remote sensing imagery. • Comparative synthesis of spectral, machine and deep learning models under real-world constraints. • F1-Score and IoU outperform Overall Accuracy for robust evaluation under class im- balance. • Analysis of preprocessing, sharpening, augmentation, and multi-sensor fusion impacts. • Identifies gaps in spatial refinement and hybrid segmentation–decision frameworks.
Methodological Insights Into the Acceleration–Speed Profile: Optimizing Data Analysis for Reliable Application in Elite Female and Male Football Antonio Alonso-Callejo, Jose Maria Manzano, Jorge Garcia-Unanue, Marc Guitart, Berta Carles, Leonor Gallardo, José Luis Felipe International Journal of Sports Physiology and Performance, 2026 Purpose : This study aimed to evaluate the reliability of the acceleration–speed profile in elite male and female football players across 3 competitive seasons. Specifically, we assessed how the number and type of microcycles influence the reliability of theoretical maximal acceleration (A 0 ) and speed (S 0 ). Methods : GPS-derived acceleration and speed data were collected from 181 women’s and 146 men’s microcycles. Acceleration–speed profiles were constructed using overlapping windows of 1 to 5 consecutive microcycles, classified as competitive, including match day (MD) or post-MD (MD + 1) or noncompetitive, not including MD or MD + 1. Linear regressions were applied to estimate A 0 and S 0 . Results : Theoretical maximal acceleration and S 0 increased with longer microcycles but plateaued beyond 5 days. Grouping by 2 microcycles showed the highest reliability for both A 0 and S 0 in male and female players. Competitive profiles consistently outperformed noncompetitive profiles in terms of stability and signal quality across both sexes. Male players demonstrated lower variability and higher signal-to-noise ratios than female players. Conclusions : The most reliable acceleration–speed profiles can be obtained from 2 consecutive microcycles, each including 5 or more sessions and at least 1 MD or MD + 1. These findings support the integration of in situ sprint profiling into applied performance monitoring without the need for isolated testing.
Reducing flow heterogeneity in drip irrigation networks using genetic algorithms Alejandro Tapia Córdoba, Jose Maria Manzano Irrigation and Drainage, 2025 Irrigation based on non‐compensated drip emitters is extremely common in agriculture, especially due to its simplicity, robustness and competitive cost. Nevertheless, because of friction losses in the pipe, together with irregular terrain, these systems often suffer from uneven water distribution in the drip emitters, which not only results in inefficient use of water resources but also might lead to inadequate irrigation in certain parts of the field. This work proposes to design the topology of the irrigation network to compensate for these discharge differences. To this end, a graph‐based mathematical model is developed to determine the discharge flows at different emitters for any network topology. This model is employed to formulate an integer nonlinear optimization problem, for which a messy genetic algorithm is proposed. The methodology is validated on an example problem, which is based on a rectangular agricultural crop of 49 fruit trees. The results revealed a 70% reduction in the coefficient of variation of the irrigation discharge rates, which was employed as a metric of irrigation uniformity. This caused a 75% reduction in the water excess. The results demonstrate that the uniformity can be improved simply by choosing a proper connectivity layout to build the pipe network.
Input-to-state stable predictive control based on continuous projected kinky inference Jose Maria Manzano, David Muñoz de la Peña, Daniel Limon International Journal of Robust and Nonlinear Control, 2025 Abstract In this article, the authors propose a novel continuous projected kinky inference algorithm, which inherits the good properties of projected kinky inference in terms of prediction error bound and computational time while ensuring Lipschitz continuity. Based on this, a learning based MPC is presented which is demonstrated to be input‐to‐state stable by design. Illustrative examples are shown in a learning‐based MPC framework.
Hybrid Modeling of the Insulin-Glucose System: Combining Linear and Data-Driven Models for Artificial Pancreas Beatrice Sonzogni, Jose Maria Manzano, Fabio Previdi, Antonio Ferramosca IFAC Papersonline, 2025 Type 1 diabetes mellitus is a chronic condition that requires insulin delivery to maintain blood glucose levels within a desired range. The artificial pancreas (AP), which integrates a continuous glucose sensor, an insulin pump, and a control algorithm, is a promising solution for automating insulin delivery. Designing optimal controllers for the AP is crucial to its effectiveness. Existing approaches often rely on advanced controllers based on models of the insulin-glucose system. However, this system is highly complex, nonlinear, and subject to time-varying dynamics and inter-patient variability, which pose significant challenges for model accuracy and control design. Hence, data-driven and machine learning-based models are emerging as powerful alternatives. This paper presents a novel data-driven modeling approach that combines two components: a linear model and a machine learning-based model. This latter is computed with the CHoKI learning method, to capture the nonlinear deviations of the actual system from the linear model, enabling the combined model to better represent the insulin-glucose system. This hybrid modeling approach offers improved prediction accuracy compared to previously proposed models in the literature. The improved model accuracy can lead to better controllers for the AP. The proposed approach is validated using the virtual patients of the FDA-accepted UVA/Padova simulator. The results outperform state-of-the-art models in prediction errors, demonstrating its potential as a step forward in AP control system design.
CHoKI-based MPC for blood glucose regulation in Artificial Pancreas Beatrice Sonzogni, José María Manzano, Marco Polver, Fabio Previdi, Antonio Ferramosca IFAC Journal of Systems and Control, 2025 This work presents a Model Predictive Control (MPC) for the artificial pancreas, which is able to autonomously manage basal insulin injections in type 1 diabetic patients. Specifically, the MPC goal is to maintain the patients’ blood glucose level inside the safe range of 70-180 mg/dL, acting on the insulin amount and respecting all the imposed constraints, taking into consideration also the Insulin On Board (IOB), to avoid excess of insulin infusion. MPC uses a model to make predictions of the system behaviour. In this work, due to the complexity of the diabetes disease that complicates the identification of a general physiological model, a data-driven learning method is employed instead. The Componentwise Hölder Kinky Inference (CHoKI) method is adopted, to have a customized controller for each patient. For the data collection phase and also to test the proposed controller, the virtual patients of the FDA-accepted UVA/Padova simulator are exploited. The MPC is also tested on simulations with variability of the insulin sensitivity and with physical activity sessions. The final results are satisfying since the proposed controller is conservative and reduces the time in hypoglycemia (which is more dangerous) if compared to the outcomes obtained without the IOB constraints.
Deployment of a Smart Irrigation Control System with Capacity-Based Moisture Sensors on a Production Farm Luis Orihuela, Erid Pacheco, Jorge Bareiro, Alejandro Tapia, Jose M. Manzano Journal of Irrigation and Drainage Engineering, 2025 This paper demonstrates a smart irrigation system prototype, based on Internet-of-Things (IoT) devices and cloud computing, in a fully operating environment. The system architecture was developed carefully with a special focus on robustness against both environmental and human external factors. The platform, which is deployed in the cloud and connected to the edge-layer via a bidirectional LoRa wireless network, was based on data gathering from the field using a set of cost-effective capacity-based moisture sensors. A hysteresis-based control structure implemented in the cloud send the control commands. The demonstration was performed on a strawberry production farm in Itaguá, Paraguay, during a 2-month period. Details of the implementation are provided, as well as an assessment of the irrigation system performance. It was found that the automated irrigation systems consumed slightly less water than manual irrigation by the farmer, but the efficiency of the automated system reached 91.4%, compared with 62.1% for manual irrigation. Finally, real-life issues encountered during the operation are discussed to illustrate the robustness of the prototype. In spite of these issues, the irrigation system was able to keep the moisture within the prescribed band most of the time, about 71.1% of all the samples, and 87.9% under normal operation.
Insulin on Board safety constraint effect in a CHoKI-based MPC for Artificial Pancreas Beatrice Sonzogni, José María Manzano, Fabio Previdi, Antonio Ferramosca IFAC Papersonline, 2024 This work presents a learning-based Model Predictive Control (MPC) algorithm for the artificial pancreas able to autonomously manage basal insulin injections in type 1 diabetic patients. The main goal is to keep the blood glucose levels within the euglycemic range (70-180 mg/dL), trying to avoid hypoglycemia. To prevent this event, additional constraints are added that consider the Insulin On Board (IOB). The data collection and the testing of the proposal are performed on the virtual patients of the FDA-accepted UVA/Padova simulator. The final results seem promising since the proposed controller reduces the time in hypoglycemia with respect to the standard constant basal insulin therapy.
Stochastic Model Predictive Control for Irrigation: Addressing Solar and Rain Uncertainties to Enhance Sustainable Productivity P. Velarde, G.B. Caceres, J.M. Manzano 2024 European Control Conference Ecc 2024, 2024 This work addresses a challenging agricultural control problem: to take into account environmental uncertainties (precipitation and solar radiance) in irrigation policies. To tackle these uncertainties, a stochastic model predictive control approach is designed, wherein each type of uncertainty is addressed using two different techniques tailored to effectively counteract them. Simulation experiments were conducted using real-world data spanning various types of days to validate the efficacy of the proposed approach. The results were benchmarked against other methods, showcasing the significant advantages of the proposed approach in terms of accuracy and robustness in agricultural irrigation control in the face of uncertainties. Therefore, this probabilistic approach also offers an effective solution to manage uncertainties and water resources, enhancing the productivity and sustainability of the sector.
Oracle-Based Economic Predictive Control J.M. Manzano, J.M. Nadales, D. Munoz de la Pena, D. Limon Proceedings of the IEEE Conference on Decision and Control, 2019
Localised kinky inference A. Blaas, J.M. Manzano, D. Limon, J. Calliess 2019 18th European Control Conference Ecc 2019, 2019
Detecting water in agricultural landscapes from remote sensing imagery: Methodological choices, sensor constraints and performance metrics DA Merchán, JM Manzano, C Ierardi Agricultural Water Management 327, 110264 , 2026 2026
State-of-the-art in multispectral remote sensing for water body identification in agriculture DA Merchan, JM Manzano, C Ierardi Authorea Preprints , 2025 2025
Methodological Insights Into the Acceleration–Speed Profile: Optimizing Data Analysis for Reliable Application in Elite Female and Male Football A Alonso-Callejo, JM Manzano, J Garcia-Unanue, M Guitart, B Carles, ... International Journal of Sports Physiology and Performance 21 (1), 33-40 , 2025 2025
Systematic design of predictive control for autonomous surface vehicles in path following with obstacle avoidance JM Manzano, G Bejarano, JR Salvador Ocean Engineering 330, 121142 , 2025 2025 Citations: 1
An´ alisis estad´ ıstico de factibilidad para sistemas autom´ aticos de localizaci´ on de fugas mediante sensores de presi´ on FMN Pérez, L Orihuela, JM Manzano Simposios del Comité Español de Automática (CEA) 1 (2) , 2025 2025
Design and Deployment of a Cloud Platform for Data-Driven Leak Detection in Agricultural Irrigation Systems JM Manzano, FM Neto, A Tapia, L Orihuela 2nd BrIAS Conference on Smart Agriculture, 67-68 , 2025 2025
Application and assessment of model-based leak localization methods in an irrigation network FM Neto, L Orihuela, JM Manzano 2nd BrIAS Conference on Smart Agriculture, 63-64 , 2025 2025 Citations: 1
Deployment of a smart irrigation control system with capacity-based moisture sensors on a production farm L Orihuela, E Pacheco, J Bareiro, A Tapia, JM Manzano Journal of Irrigation and Drainage Engineering 151 (1), 04024039 , 2025 2025 Citations: 3
Análisis estadístico de factibilidad para sistemas automáticos de localización de fugas mediante sensores de presión FM Neto Pérez, L Orihuela, JM Manzano Simposios del Comité Español de Automática 1 (2) , 2025 2025
Hybrid Modeling of the Insulin-Glucose System: Combining Linear and Data-Driven Models for Artificial Pancreas B Sonzogni, JM Manzano, F Previdi, A Ferramosca IFAC-PapersOnLine 59 (2), 109-114 , 2025 2025 Citations: 1
Data-driven spatio-temporal estimation of soil moisture and temperature based on Lipschitz interpolation JM Manzano, L Orihuela, E Pacheco, M Pereira ISA transactions 156, 535-550 , 2025 2025 Citations: 5
Reducing flow heterogeneity in drip irrigation networks using genetic algorithms AT Córdoba, JM Manzano Irrigation and Drainage , 2024 2024 Citations: 2
Deployment and verification of custom autonomous low-budget IoT devices for image feature extraction in wheat F Martinez, JB Romaine, JM Manzano, C Ierardi, PM Gata IEEE Access 12, 124636-124657 , 2024 2024 Citations: 9
Aproximación a la identificación no paramétrica de sistemas muestreados asíncronamente mediante interpolación de Lipschitz L Orihuela, JM Manzano Jornadas de Automática , 2024 2024
Stochastic Model Predictive Control for Irrigation: addressing solar and rain uncertainties to enhance sustainable productivity P Velarde, GB Caceres, JM Manzano 2024 European Control Conference (ECC), 388-393 , 2024 2024 Citations: 1
IFAC Journal of Systems and Control B Sonzogni, JM Manzano, M Polver, F Previdi, A Ferramosca 2024
Insulin on Board safety constraint effect in a CHoKI-based MPC for Artificial Pancreas B Sonzogni, JM Manzano, F Previdi, A Ferramosca IFAC-PapersOnLine 58 (24), 257-262 , 2024 2024 Citations: 1
INNOVATING ENGINEERING EDUCATION: A CASE STUDY ON PROJECT-BASED LEARNING IN HOME AUTOMATION JM Manzano, JM Barroso EDULEARN24 Proceedings, 2647-2652 , 2024 2024
CHoKI-Based MPC for Blood Glucose Regulation in Artificial Pancreas with Probabilistic Constraints B Sonzogni, JM Manzano, M Polver, F Previdi, A Ferramosca 2023 62nd IEEE Conference on Decision and Control (CDC), 1619-1624 , 2023 2023 Citations: 5
CHoKI-based MPC for blood glucose regulation in artificial Pancreas B Sonzogni, JM Manzano, M Polver, F Previdi, A Ferramosca IFAC-PapersOnLine 56 (2), 9672-9677 , 2023 2023 Citations: 17
MOST CITED SCHOLAR PUBLICATIONS
Robust learning-based MPC for nonlinear constrained systems JM Manzano, D Limon, DM de la Peña, JP Calliess Automatica 117, 108948 , 2020 2020 Citations: 118
Stability of Gaussian process learning based output feedback model predictive control M Maiworm, D Limon, JM Manzano, R Findeisen IFAC-PapersOnLine 51 (20), 455-461 , 2018 2018 Citations: 65
Nonlinear model predictive control-based guidance law for path following of unmanned surface vehicles G Bejarano, JM Manzano, JR Salvador, D Limon Ocean Engineering 258, 111764 , 2022 2022 Citations: 50
Output Feedback MPC based on Smoothed Projected Kinky Inference JM Manzano, D Limon, D Muñoz de la Peña, JP Calliess IET Control Theory & Applications 13 (6), 795-805 , 2019 2019 Citations: 47
Economic predictive control for isolated microgrids based on real world demand/renewable energy data and forecast errors JM Manzano, JR Salvador, JB Romaine, L Alvarado-Barrios Renewable Energy 194, 647-658 , 2022 2022 Citations: 25
Componentwise Hölder inference for robust learning-based MPC JM Manzano, D Muñoz de la Pena, JP Calliess, D Limon IEEE Transactions on Automatic Control , 2021 2021 Citations: 22
Robust design through probabilistic maximization T Alamo, JM Manzano, EF Camacho Uncertainty in Complex Networked Systems: In Honor of Roberto Tempo, 247-274 , 2018 2018 Citations: 22
Robust data-based model predictive control for nonlinear constrained systems JM Manzano, D Limon, DM de la Peñ, J Calliess IFAC-PapersOnLine 51 (20), 505-510 , 2018 2018 Citations: 19
CHoKI-based MPC for blood glucose regulation in artificial Pancreas B Sonzogni, JM Manzano, M Polver, F Previdi, A Ferramosca IFAC-PapersOnLine 56 (2), 9672-9677 , 2023 2023 Citations: 17
Localised kinky inference A Blaas, JM Manzano, D Limon, J Calliess 2019 18th European Control Conference (ECC), 985-992 , 2019 2019 Citations: 16
Modelling and Identification of an Autonomous Surface Vehicle: Technical Report TA Morel, JM Manzano, G Bejarano, L Orihuela 2022 Citations: 15
EEG—Single-Channel Envelope Synchronisation and Classification for Seizure Detection and Prediction M Pereira, JB Romaine, JR Salvador, Manzano, J María Brain Sciences 11 (4), 516 , 2021 2021 Citations: 15
Efficient FPGA parallelization of Lipschitz interpolation for real-time decision-making JM Nadales, JM Manzano, A Barriga, D Limón IEEE Transactions on Control Systems Technology 30 (5), 2163-2175 , 2022 2022 Citations: 13
Online learning constrained model predictive control based on double prediction JM Manzano, D Muñoz de la Peña, J Calliess, D Limon International Journal of Robust and Nonlinear Control , 2020 2020 Citations: 12
Online learning robust MPC: an exploration-exploitation approach JM Manzano, J Calliess, D Munoz de la Pena, D Limon IFAC-PapersOnLine 53 (2), 5292-5297 , 2020 2020 Citations: 10
Deployment and verification of custom autonomous low-budget IoT devices for image feature extraction in wheat F Martinez, JB Romaine, JM Manzano, C Ierardi, PM Gata IEEE Access 12, 124636-124657 , 2024 2024 Citations: 9
Control predictivo basado en datos JM Manzano, D Limón, T Álamo Cantarero, JP Callies Actas de las XXXVIII Jornadas de Automática , 2017 2017 Citations: 7
Experimental validation of robust non-linear state observers for autonomous surface vehicles equipped with position sensors TA Morel, G Bejarano, JM Manzano, L Orihuela 2022 IEEE Conference on Control Technology and Applications (CCTA), 357-362 , 2022 2022 Citations: 6
Oracle-based economic predictive control JM Manzano, DM de la Peña, D Limon Computers & Chemical Engineering, 107434 , 2021 2021 Citations: 6
Data-based Robust MPC with Componentwise Hölder Kinky Inference JM Manzano, D Limon, DM de la Peña, JP Calliess 2019 IEEE 58th Conference on Decision and Control (CDC), 6449-6454 , 2019 2019 Citations: 6
