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Monitoring and Control for Precision and Smart Agriculture

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 8351

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Special Issue Editors

Dr. Olga V. Tutubalina

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Guest Editor

Department of Geography, M.V. Lomonosov Moscow State University, Moscow, Russia
Interests: remote sensing; terrestrial ecosystem; UAV; precision agriculture; vegetation monitoring

Prof. Dr. Hak-Jin Kim

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Guest Editor

Department of Biosystems & Biomaterials Science and Engineering, Seoul 08826, Korea
Interests: biosystems control; precision agriculture

Special Issue Information

Dear Colleagues,

Precision agriculture (PA) uses various remote and proximal sensing devices and data for farm management to optimize returns on inputs while preserving resources. Lots of advanced sensors and technologies are used in precision agriculture, making it possible to achieve crop growth monitoring and control. The proliferation of various unmanned agricultural machines and intelligent complex control systems for greenhouses makes monitoring and control for smart agriculture even more important.

This Special Issue aims to discuss various aspects of monitoring and control for precision and smart agriculture, mainly data processing and applications based on remote and proximal sensing technologies and methods, such as crop and soil sensors, satellite navigation and positioning technology, unmanned aerial vehicle (UAV) imagery, LiDAR monitoring, and others.

We invite you to contribute to this Special Issue by submitting comprehensive reviews, case studies, or research articles that focus on scientific methods, innovative technologies, and system development. Specific topics include but are not limited to the following:

Application of remote sensing for smart farming;
Applications and intelligent system development of various unmanned agricultural machines;
Applications of new sensors in intelligent complex control systems for greenhouses;
Crop growth monitoring;
Precise fertigation control.

Dr. Olga V. Tutubalina
Dr. Hak-Jin Kim
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. Remote Sensing 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 2700 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

UAV
cameras
crop monitoring
automated agricultural systems
greenhouses
vegetation index
farm management
irrigation
AI
CNN

Published Papers (3 papers)

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Research

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18 pages, 6609 KiB

Open AccessArticle

Multilayer Data and Artificial Intelligence for the Delineation of Homogeneous Management Zones in Maize Cultivation

by Diego José Gallardo-Romero, Orly Enrique Apolo-Apolo, Jorge Martínez-Guanter and Manuel Pérez-Ruiz

Remote Sens. 2023, 15(12), 3131; https://doi.org/10.3390/rs15123131 - 15 Jun 2023

Cited by 2 | Viewed by 1405

Abstract

Variable rate application (VRA) is a crucial tool in precision agriculture, utilizing platforms such as Google Earth Engine (GEE) to access vast satellite image datasets and employ machine learning (ML) techniques for data processing. This research investigates the feasibility of implementing supervised ML models (random forest (RF), the support vector machine (SVM), gradient boosting trees (GBT), classification and regression trees (CART)) and unsupervised k-means clustering in GEE to generate accurate management zones (MZs). By leveraging Sentinel-2 satellite imagery and yielding monitor data, these models calculate vegetation indices to monitor crop health and reveal hidden patterns. The achieved classification accuracy values (0.67 to 0.99) highlight the potential of GEE and ML models for creating precise MZs, enabling subsequent VRA implementation. This leads to enhanced farm profitability, improved natural resource efficiency, and reduced environmental impact. Full article

(This article belongs to the Special Issue Monitoring and Control for Precision and Smart Agriculture)

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Review

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40 pages, 3921 KiB

Open AccessReview

The Role of Remote Sensing in Olive Growing Farm Management: A Research Outlook from 2000 to the Present in the Framework of Precision Agriculture Applications

by Gaetano Messina and Giuseppe Modica

Remote Sens. 2022, 14(23), 5951; https://doi.org/10.3390/rs14235951 - 24 Nov 2022

Cited by 8 | Viewed by 3901

Abstract

Given the importance of olive growing, especially in Mediterranean countries, it is crucial that there is a constant process of modernization aimed at both environmental sustainability and the maintenance of high standards of production. The use of remote sensing (RS) allows intervention in a specific and differentiated way in olive groves, depending on their variability, in managing different agronomic aspects. The potentialities of the application of RS in olive growing are topics of great agronomic interest to olive growers. Using the tools provided by RS and the modernization of the olive sector can bring great future prospects by reducing costs, optimizing agronomic management, and improving production quantity and quality. This article is part of a review that aims to cover the past, from the 2000s onwards, and the most recent applications of aerial RS in olive growing in order to be able to include research and all topics related to the use of RS on olive trees. As far as the use of RS platforms such as satellites, aircraft, and unmanned aerial vehicles (UAVs) as olive growing is concerned, a literature review showed the presence of several works devoted to this topic. This article covers purely agronomic matters of interest to olive farms (and related research that includes the application of RS), such as yielding and managing diseases and pests, and detection and counting of olive trees. In addition to these topics, there are other relevant aspects concerning the characterization of the canopy structure of olive trees which is particularly interesting for mechanized pruning management and phenotyping. Full article

(This article belongs to the Special Issue Monitoring and Control for Precision and Smart Agriculture)

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33 pages, 2257 KiB

Open AccessReview

Twenty Years of Remote Sensing Applications Targeting Landscape Analysis and Environmental Issues in Olive Growing: A Review

by Gaetano Messina and Giuseppe Modica

Remote Sens. 2022, 14(21), 5430; https://doi.org/10.3390/rs14215430 - 28 Oct 2022

Cited by 4 | Viewed by 2322

Abstract

The olive (Olea europaea L.) is an iconic tree linked to the birth of some of the most ancient civilizations and one of the most important cultivated tree species in the Mediterranean basin. Over the last few decades, given the high socio-economic importance of the olive sector, there has been much research involving remote sensing (RS) applications in olive growing, especially in precision agriculture. This review article is part of a review that aims to cover the past, from the 2000s onwards, and the most recent applications of remote sensing (RS) in olive growing to be able to include research and all topics related to the use of RS on olive trees. As far as the use of RS platforms such as satellites, aircraft, and unmanned aerial vehicles (UAVs) in olive growing is concerned, a review of the literature showed the presence of several works devoted to it. A brief introduction on the history of the olive tree and its distribution and cultivation around the world, together with a summary of the leading RS platforms (a good portion of which are satellites) used in olive research, anticipates the discussion of four topics about olive growing that have as their common thread positive (and non-positive) impacts on the environment: preservation of olive landscape and soil erosion, identification of olive groves, olive oil mill wastewater (OOMW) and relative environmental risks, irrigation water management and the use of RS platforms for water stress monitoring. The preservation of olive groves as an element of Mediterranean identity and strategic economic resource in agriculture depends on sustainable environmental management alongside technological advances brought by precision agriculture. Full article

(This article belongs to the Special Issue Monitoring and Control for Precision and Smart Agriculture)

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