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Precision Agriculture Techniques for Sustainable Water and Soil Management
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Precision Agriculture Techniques for Sustainable Water and Soil Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 5851

Special Issue Editors

Dr. Iulian Voicea

E-Mail Website
Guest Editor
National Institute of Research—Development for Machines and Installations Designed to Agriculture and Food Industry—INMA, Bucharest, Romania
Interests: soil analysis; agriculture; environment; soil conservation; organic agriculture; biofertilizers; crop management; precision agriculture; protected cultivation
Dr. Florin Nenciu

E-Mail Website
Guest Editor
Researcher, The National Institute of Research—Development for Machines and Installations Designed for Agriculture and Food Industry—INMA, Bucharest, Romania
Interests: sustainability in agriculture; biofertilizers production; food safety
Dr. Atanas Atanasov

E-Mail Website
Guest Editor
Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, Ruse 7004, Bulgaria
Interests: modern agriculture technologies; smart greenhouses; smart vegetable growing; crop monitoring; precision farming; farm automation; remote sensing; data-driven farming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The scope of this Special Issue, "Precision Agriculture Techniques for Sustainable Water and Soil Management", is to explore a range of innovative methodologies, technologies, and agricultural equipment, designed to increase productivity, optimize resource management, and chemical inputs utilization in agriculture. This includes precision irrigation systems, AI-based crop treatment equipment, soil monitoring technologies using drones and remote sensing, data analytics for resource management, and sustainable farming practices. The aim is to consolidate cutting-edge research and various practical applications that promote efficiency, sustainability, and environmental protection in agricultural practices. We invite submissions of articles and review papers covering a broad spectrum of topics, encompassing various sustainability practices, technological innovations, case studies, and field trials. These submissions should approach diverse aspects of sustainable agriculture, explore pioneering technologies, and present empirical evidence through detailed research studies.

We propose establishing clean agroecosystems that enhance sustainable food production, promote soil health in marginal lands, and protect soils from erosion and landslides.

Some non-limiting domain recommendations for the proposed papers are as follows:

  • Integration of IOT and sensor-based technologies in precision agriculture;
  • Production of highly efficient biofertilizers;
  • Applications of artificial intelligence in agriculture;
  • Advanced aquaponic systems;
  • Soil health assessment methods and strategies;
  • Case studies of sustainable farming practices.

Dr. Iulian Voicea
Dr. Florin Nenciu
Dr. Atanas Atanasov
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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • IOT and sensor-based technologies
  • artificial intelligence in agriculture
  • highly efficient biofertilizers

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Published Papers (3 papers)

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Research

22 pages, 8737 KB  
Article
Remote Sensing of Soil Moisture in Bare Chernozems on Flat and Sloping Terrains
by Zlatomir Dimitrov, Atanas Z. Atanasov, Dessislava Ganeva, Milena Kercheva, Gergana Kuncheva, Viktor Kolchakov and Martin Nenov
Sustainability 2026, 18(7), 3373; https://doi.org/10.3390/su18073373 - 31 Mar 2026
Viewed by 247
Abstract
The aim of the current study was to select and test the appropriate model and input parameters for remote sensing retrieval of surface soil moisture (SSM) in the case of bare Chernozems on flat and sloping terrains in northern Bulgaria under different tillage [...] Read more.
The aim of the current study was to select and test the appropriate model and input parameters for remote sensing retrieval of surface soil moisture (SSM) in the case of bare Chernozems on flat and sloping terrains in northern Bulgaria under different tillage systems. Normalized synthetic aperture radar (SAR) measurements from Sentinel-1 C-band dual-pol products (Gamma-Nought in VV, ratio) were utilized in two ways to delineate SSM from environmental factors that bias determination. The accuracy of the obtained SSM prediction was evaluated against ground-based volumetric water content (VWC) measured in the 0–3.8 cm soil layer at multiple points using a TDR meter. The TDR VWC data were preliminarily calibrated against gravimetric measurements in the 0–5 cm soil layer. The obtained data for soil water retention curves in all studied variants were used to determine the range of soil moisture variation. The measured ground-based data for surface roughness generally correlate with the co-pol Gamma-Nought in VV. The data modeled with the surface soil moisture script in Sentinel Hub (SSM-SH) was calibrated using the ground-based data. Incidence angle normalization of Sentinel-1 products improved the relationship between SAR observables and SSM, when expressed as the ratio of soil moisture to total porosity (rVWC). The modeling indicated the highest importance of the optical indices, together with the temporal differences of radar descriptors sensitive to variations in soil moisture over time. Although the applied Random Forest Regression (RFR) model achieved higher accuracy during training (nRMSE of 7.27%, R2 of 0.86), the Gaussian Process Regression (GPR) model provided better generalization performance on the independent validation dataset. The results proved the advantages of the joint utilization of temporal Sentinel-1 SAR measurements with Sentinel-2 optical acquisitions to determine SSM in different bare soil conditions for achieving high accuracy. Full article
(This article belongs to the Special Issue Precision Agriculture Techniques for Sustainable Water and Soil Management)
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16 pages, 1477 KB  
Article
Machine Learning-Based Modeling of Tractor Fuel and Energy Efficiency During Chisel Plough Tillage
by Ergün Çıtıl, Kazım Çarman, Muhammet Furkan Atalay, Nicoleta Ungureanu and Nicolae-Valentin Vlăduț
Sustainability 2026, 18(2), 855; https://doi.org/10.3390/su18020855 - 14 Jan 2026
Cited by 1 | Viewed by 476
Abstract
Improving fuel and energy efficiency in agricultural tillage is critical for sustainable farming and reducing environmental impacts. In this study, the effects of forward speed and tillage depth on the fuel efficiency parameters of a tractor–chisel plough combination were investigated under controlled field [...] Read more.
Improving fuel and energy efficiency in agricultural tillage is critical for sustainable farming and reducing environmental impacts. In this study, the effects of forward speed and tillage depth on the fuel efficiency parameters of a tractor–chisel plough combination were investigated under controlled field conditions on clay soil. Specific fuel consumption (SFC), fuel consumption per unit area (FCPA), and overall energy efficiency (OEE) were evaluated at four forward speeds (0.6, 0.95, 1.2 and 1.4 m·s−1) and four tillage depths (15, 19.5, 23 and 26.5 cm). SFC ranged from 0.519 to 1.237 L·kW−1·h−1, while OEE varied between 7.918 and 18.854%. Higher forward speeds significantly reduced fuel consumption and improved energy efficiency, whereas deeper tillage increased fuel use and reduced efficiency. Optimal operation occurred at speeds of 1.2–1.4 m·s−1 and shallow to medium depths. Five machine learning algorithms: Polynomial Regression (PL), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR), were applied to model fuel efficiency parameters. RFR achieved the highest accuracy for predicting SFC, while PL performed best for FCPA and OEE, with the mean absolute percentage error (MAPE) below 2%. Models such as PL and RFR excel in data structures dominated by nonlinear relationships. These results highlight the potential of machine learning to guide data-driven decisions for fuel and energy optimization in tillage, promoting more sustainable mechanization strategies and resource-efficient agricultural production. Full article
(This article belongs to the Special Issue Precision Agriculture Techniques for Sustainable Water and Soil Management)
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28 pages, 1573 KB  
Article
Soil Properties Classification in Sustainable Agriculture Using Genetic Algorithm-Optimized and Deep Neural Networks
by Yadviga Tynchenko, Vadim Tynchenko, Vladislav Kukartsev, Tatyana Panfilova, Oksana Kukartseva, Ksenia Degtyareva, Van Nguyen and Ivan Malashin
Sustainability 2024, 16(19), 8598; https://doi.org/10.3390/su16198598 - 3 Oct 2024
Cited by 13 | Viewed by 4153
Abstract
Optimization of land management and agricultural practices require precise classification of soil properties. This study presents a method to fine-tune deep neural network (DNN) hyperparameters for multiclass classification of soil properties using genetic algorithms (GAs) with knowledge-based generation of hyperparameters. The focus is [...] Read more.
Optimization of land management and agricultural practices require precise classification of soil properties. This study presents a method to fine-tune deep neural network (DNN) hyperparameters for multiclass classification of soil properties using genetic algorithms (GAs) with knowledge-based generation of hyperparameters. The focus is on classifying soil attributes, including nutrient availability (0.78 ± 0.11), nutrient retention capacity (0.86 ± 0.05), rooting conditions (0.85 ± 0.07), oxygen availability to roots (0.84 ± 0.05), excess salts (0.96 ± 0.02), toxicity (0.96 ± 0.01), and soil workability (0.84 ± 0.09), with these accuracies representing the results from classification with variations from cross-validation. A dataset from the USA, which includes land-use distribution, aspect distribution, slope distribution, and climate data for each plot, is utilized. A GA is applied to explore a wide range of hyperparameters, such as the number of layers, neurons per layer, activation functions, optimizers, learning rates, and loss functions. Additionally, ensemble methods such as random forest and gradient boosting machines were employed, demonstrating comparable accuracy to the DNN approach. This research contributes to the advancement of precision agriculture by providing a robust machine learning (ML) framework for accurate soil property classification. By enabling more informed and efficient land management decisions, it promotes sustainable agricultural practices that optimize resource use and enhance soil health for long-term ecological balance. Full article
(This article belongs to the Special Issue Precision Agriculture Techniques for Sustainable Water and Soil Management)
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