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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: 30 April 2025 | Viewed by 1700

Special Issue Editors


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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

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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

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Guest Editor
Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria
Interests: modern agriculture technologies; smart greenhouses; smart vegetable growing; crop monitoring; precision farming; farm automation; remote monitoring; 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 100 words) can be sent to the Editorial Office for announcement on this website.

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 (1 paper)

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Research

28 pages, 1573 KiB  
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
Viewed by 1371
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
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