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Advances in Control and Automation in Smart Agriculture

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 19349

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Special Issue Editors

Prof. Dr. Daniel García Fernández-Pacheco

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Guest Editor

Department of Structures, Construction and Graphic Expression, Universidad Politécnica de Cartagena, 30202 Cartagena, Murcia, Spain
Interests: computer vision; multiagent systems; agricultural and biological sciences; modelling of biological structures
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. José Miguel Molina Martínez

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Guest Editor

Department of Agricultural Engineering, Technical University of Cartagena, 30202 Cartagena, Murcia, Spain
Interests: water resources management; irrigation; energy efficiency; smart agriculture; agriculture automation and control; computers and electronics in agriculture
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Dr. Dolores Parras-Burgos

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Guest Editor

Department of Structures, Construction and Graphic Expression, Universidad Politécnica de Cartagena, 30202 Cartagena, Murcia, Spain
Interests: industrial design in agriculture; augmented/virtual reality; CAD
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart agriculture is a new revolution that involves the application of information and communication technologies (ICT) in the field of agriculture, with the aim of increasing the quantity and quality of production, maximising the use of resources and minimising environmental impact. Following the changes brought about by plant breeding and plant genetics, this new revolution is changing the landscape of the agricultural sector through the application of ICT solutions in the farming process, such as through precision farming, the Internet of Things (IoT), the use of sensors and actuators, geopositioning systems, Big Data, drones, robots, etc. Smart agriculture presents a real potential for increasing agricultural sustainability and productivity based on the more efficient and accurate use of different natural resources.

For this Special Issue, we invite the submission of manuscripts covering the main theme of automation and control in smart agriculture, whereby the main applications are focused on the use of technologies such as robotics, automated control and artificial intelligence at all levels of agricultural production, including the use of agricultural robots and drones. For example, drones that monitor hundreds of hectares to assess the health of crops and animals, intelligent sensors that favour the early detection of pests and automated systems that irrigate, fertilise and fumigate each plot according to its particularities and weather forecasts, among others.

Dr. Daniel G. Fernández-Pacheco
Prof. Dr. José Miguel Molina Martínez
Dr. Dolores Parras-Burgos
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. Sensors 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 2600 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

agricultural automation and control
artificial intelligence
agricultural robotics
precision farming
pest and disease management
greenhouse management
climate control
irrigation and fertigation management
software applications
agro-industrial automation

Published Papers (10 papers)

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Research

19 pages, 6406 KiB

Open AccessArticle

Control and Measurement Systems Supporting the Production of Haylage in Baler-Wrapper Machines

by Michał Zawada, Mateusz Nijak, Jarosław Mac, Jan Szczepaniak, Stanisław Legutko, Julia Gościańska-Łowińska, Sebastian Szymczyk, Michał Kaźmierczak, Mikołaj Zwierzyński, Jacek Wojciechowski, Tomasz Szulc and Roman Rogacki

Sensors 2023, 23(6), 2992; https://doi.org/10.3390/s23062992 - 09 Mar 2023

Cited by 1 | Viewed by 1665

Abstract

Baler-wrappers are machines designed to produce high-quality forage, in accordance with the requirements of sustainable agriculture. Their complicated structure, and significant loads occurring during operation, prompted the creation of systems for controlling the machines’ processes and measuring the most important work parameters, in this work. The compaction control system is based on a signal from the force sensors. It allows for detection differences in the compression of the bale and additionally protects against overload. The method of measuring the swath size, with the use of a 3D camera, was presented. Scanning the surface and travelled distance allows for estimating the volume of the collected material—making it possible to create yield maps (precision farming). It is also used to vary the dosage of ensilage agents, that control the fodder formation process, in relation to the moisture and temperature of the material. The paper also deals with the issue of measuring the weight of the bales—securing the machine against overload and collecting data for planning the bales’ transport. The machine, equipped with the above-mentioned systems, allows for safer and more efficient work, and provides information about the state of the crop in relation to a geographical position, which allows for further inferences. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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24 pages, 2325 KiB

Open AccessArticle

RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust

by Carlos Suarez, David Griol, Cristhian Figueroa, Juan Carlos Corrales and David Camilo Corrales

Sensors 2022, 22(24), 9598; https://doi.org/10.3390/s22249598 - 07 Dec 2022

Cited by 4 | Viewed by 1429

Abstract

Crop disease management in smart agriculture involves applying and using new technologies to reduce the impact of diseases on the quality of products. Coffee rust is a disease that factors such as poor agronomic management activities and climate conditions may favor. Therefore, it is crucial to identify the relationships between these factors and this disease to learn how to face its consequences and build intelligent systems to provide appropriate management or help farmers and experts make decisions accordingly. Nevertheless, there are no studies in the literature that propose ontologies to model these factors and coffee rust. This paper presents a new ontology called RustOnt to help experts more accurately model data, expressions, and samples related to coffee rust and apply it whilst taking into account the geographical location where the ontology is adopted. Consequently, this ontology is crucial for coffee rust monitoring and management by means of smart agriculture systems. RustOnt was successfully evaluated considering quality criteria such as clarity, consistency, modularity, and competence against a set of initial requirements for which it was built. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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16 pages, 6916 KiB

Open AccessArticle

Evaluation of Nitrate Soil Probes for a More Sustainable Agriculture

by Amelia Bellosta-Diest, Miguel Á. Campo-Bescós, Jesús Zapatería-Miranda, Javier Casalí and Luis M. Arregui

Sensors 2022, 22(23), 9288; https://doi.org/10.3390/s22239288 - 29 Nov 2022

Cited by 2 | Viewed by 3358

Abstract

Synthetic nitrogen (N) fertilizers and their increased production and utilization have played a great role in increasing crop yield and in meeting the food demands resulting from population growth. Nitrate (NO₃⁻) is the common form of nitrogen absorbed by plants. It has high water solubility and low retention by soil particles, making it prone to leaching and mobilization by surface water, which can seriously contaminate biological environments and affect human health. Few methods exist to measure nitrate in the soil. The development of ion selective sensors provides knowledge about the dynamics of nitrate in the soil in real time, which can be very useful for nitrate management. The objective of this study is to analyze the performance of three commercial probes (Nutrisens, RIKA and JXCT) under the same conditions. The performance was analyzed with respect to electrical conductivity (EC) (0–50 mS/cm) and nitrate concentration in aqueous solution and in sand (0–180 ppm NO₃⁻) at 35% volumetric soil moisture. Differences were shown among probes when studying their response to variations of the EC and, notably, only the Nutrisens probe provided coherent accurate measurements. In the evaluation of nitrate concentration in liquid solution, all probes proved to be highly sensitive. Finally, in the evaluation of all probes’ response to modifications in nitrate concentration in sand, the sensitivity decreased for all probes, with the Nutrisens probe the most sensitive and the other two probes almost insensitive. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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17 pages, 3626 KiB

Open AccessArticle

Electric Impedance Spectroscopy in Trees Condition Analysis: Theory and Experiment

by Maxim E. Astashev, Evgeny M. Konchekov, Leonid V. Kolik and Sergey V. Gudkov

Sensors 2022, 22(21), 8310; https://doi.org/10.3390/s22218310 - 29 Oct 2022

Cited by 9 | Viewed by 1761

Abstract

Electric impedance spectroscopy is an alternative technology to existing methods that shows promising results in the agro-food industry and plant physiology research. For example, this technology makes it possible to monitor the condition of plants, even in the early stages of development, and to control the quality of finished products. However, the use of electric impedance spectroscopy is often associated with the need to organize special laboratory conditions for measurements. Our aim is to extract information about the state of health of the internal tissues of a plant’s branches from impedance measurements. Therefore, we propose a new technique using the device and model developed by us that makes it possible to monitor the condition of tree branch tissues in situ. An apple tree was chosen as the object under study, and the dependence of the impedance of the apple tree branch on the signal frequency and branch length was analyzed. The change in the impedance of an apple tree branch during drying was also analyzed. It was shown that, when a branch dries out, the conductivity of the xylem mainly decreases. The developed technique was also applied to determine the development of the vascular system of an apple tree after grafting. It was shown that the processing of the scion and rootstock sections with the help of cold atmospheric plasma and a plasma-treated solution contributes to a better formation of graft unions. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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14 pages, 969 KiB

Open AccessArticle

Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain

by Francisco J. Diez, Ouiam F. Boukharta, Luis M. Navas-Gracia, Leticia Chico-Santamarta, Andrés Martínez-Rodríguez and Adriana Correa-Guimaraes

Sensors 2022, 22(20), 7772; https://doi.org/10.3390/s22207772 - 13 Oct 2022

Cited by 2 | Viewed by 1222

Abstract

In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m²) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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37 pages, 14586 KiB

Open AccessArticle

Monitoring System of the Mar Menor Coastal Lagoon (Spain) and Its Watershed Basin Using the Integration of Massive Heterogeneous Data

by Francisco Javier López-Andreu, Juan Antonio López-Morales, Joaquín Francisco Atenza Juárez, Rosa Alcaraz, María Dolores Hernández, Manuel Erena, Jose Antonio Domínguez-Gómez and Sandra García Galiano

Sensors 2022, 22(17), 6507; https://doi.org/10.3390/s22176507 - 29 Aug 2022

Cited by 2 | Viewed by 2015

Abstract

The tool created aims at the environmental monitoring of the Mar Menor coastal lagoon (Spain) and the monitoring of the land use of its watershed. It integrates heterogeneous data sources ranging from ecological data obtained from a multiparametric oceanographic sonde to agro-meteorological data from IMIDA’s network of stations or hydrological data from the SAIH network as multispectral satellite images from Sentinel and Landsat space missions. The system is based on free and open source software and has been designed to guarantee maximum levels of flexibility and scalability and minimum coupling so that the incorporation of new components does not affect the existing ones. The platform is designed to handle a data volume of more than 12 million records, experiencing exponential growth in the last six months. The tool allows the transformation of a large volume of data into information, offering them through microservices with optimal response times. As practical applications, the platform created allows us to know the ecological state of the Mar Menor with a very high level of detail, both at biophysical and nutrient levels, being able to detect periods of oxygen deficit and delimit the affected area. In addition, it facilitates the detailed monitoring of the cultivated areas of the watershed, detecting the agricultural use and crop cycles at the plot level. It also makes it possible to calculate the amount of water precipitated on the watershed and to monitor the runoff produced and the amount of water entering the Mar Menor in extreme events. The information is offered in different ways depending on the user profile, offering a very high level of detail for research or data analysis profiles, concrete and direct information to support decision-making for users with managerial profiles and validated and concise information for citizens. It is an integrated and distributed system that will provide data and services for the Mar Menor Observatory. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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16 pages, 9996 KiB

Open AccessArticle

In-Field Automatic Identification of Pomegranates Using a Farmer Robot

by Rosa Pia Devanna, Annalisa Milella, Roberto Marani, Simone Pietro Garofalo, Gaetano Alessandro Vivaldi, Simone Pascuzzi, Rocco Galati and Giulio Reina

Sensors 2022, 22(15), 5821; https://doi.org/10.3390/s22155821 - 04 Aug 2022

Cited by 9 | Viewed by 1783

Abstract

Ground vehicles equipped with vision-based perception systems can provide a rich source of information for precision agriculture tasks in orchards, including fruit detection and counting, phenotyping, plant growth and health monitoring. This paper presents a semi-supervised deep learning framework for automatic pomegranate detection using a farmer robot equipped with a consumer-grade camera. In contrast to standard deep-learning methods that require time-consuming and labor-intensive image labeling, the proposed system relies on a novel multi-stage transfer learning approach, whereby a pre-trained network is fine-tuned for the target task using images of fruits in controlled conditions, and then it is progressively extended to more complex scenarios towards accurate and efficient segmentation of field images. Results of experimental tests, performed in a commercial pomegranate orchard in southern Italy, are presented using the DeepLabv3+ (Resnet18) architecture, and they are compared with those that were obtained based on conventional manual image annotation. The proposed framework allows for accurate segmentation results, achieving an F1-score of 86.42% and IoU of 97.94%, while relieving the burden of manual labeling. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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12 pages, 1246 KiB

Open AccessArticle

Application of Digital Olfaction for Table Olive Industry

by Ramiro Sánchez, Antonio Fernández, Elisabet Martín-Tornero, Félix Meléndez, Jesús Lozano and Daniel Martín-Vertedor

Sensors 2022, 22(15), 5702; https://doi.org/10.3390/s22155702 - 29 Jul 2022

Cited by 7 | Viewed by 1532

Abstract

The International Olive Council (IOC) established that olives must be free of odors, off-flavors, and absent of abnormal ongoing alterations or fermentations. The use of electronic devices could help when classifying defects in a fast, non-destructive, cheap, and environmentally friendly way. For all of that, table olives were evaluated according to IOC regulation in order to classify the defect predominant perceiving (DPP) of the table olives and their intensity. Abnormal fermentation defects of Spanish-style table olives were assessed previously by an IOC-validated tasting panel. ‘Zapateria’, ‘Putrid’, and ‘Butyric’ were the defects found at different concentrations. Different volatile compounds were identified by gas chromatography in altered table olives. The same samples were measured with an electronic nose device (E-nose). E-nose data combined with chemometrics algorithms, such as PCA and PLS-DA, were able to successfully discriminate between healthy and non-healthy table olives, being this last one also separated between the first and second categories. Volatile compounds obtained with gas chromatography could be related to the E-nose measuring and sensory analysis, being capable of matching the different defects with their correspondents’ volatile compounds. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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12 pages, 1084 KiB

Open AccessArticle

Preliminary Evaluation of a Blast Sprayer Controlled by Pulse-Width-Modulated Nozzles

by Enrique Ortí, Andrés Cuenca, Montano Pérez, Antonio Torregrosa, Coral Ortiz and Francisco Rovira-Más

Sensors 2022, 22(13), 4924; https://doi.org/10.3390/s22134924 - 29 Jun 2022

Cited by 6 | Viewed by 1504

Abstract

Precision spraying relies on the response of the spraying equipment to the features of the targeted canopy. PWM technology manages the flow rate using a set of electronically actuated solenoid valves to regulate flow rate at the nozzle level. Previous studies have found that PWM systems may deliver incorrect flow rates. The objective of the present study was to characterize the performance of a commercial blast sprayer modified with pulse-width-modulated nozzles under laboratory conditions, as a preliminary step before its further field validation. Four different duty cycles (25 percent, 50 percent, 75 percent and 100 percent) and four different pressures (400 kPa, 500 kPa, 600 kPa and 700 kPa) were combined to experimentally measure the flow rate of each nozzle. Results showed that the PWM nozzles mounted in the commercial blast sprayer, under static conditions, were capable of modulating flow rate according to the duty cycle. However, the reduction of flow rates for the tested duty cycles according to pressure was lower than the percentage expected. A good linear relation was found between the pressure registered by the control system feedback sensor and the pressure measured by a reference conventional manometer located after the pump. High-speed video recordings confirmed the accurate opening and closing of the nozzles according to the duty cycle; however, substantial pressure variations were found at nozzle level. Further research to establish the general suitability of PWM systems for regulating nozzle flow rates in blast sprayers without modifying the system pressure still remains to be addressed. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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30 pages, 26333 KiB

Open AccessArticle

Microclimatic Evaluation of Five Types of Colombian Greenhouses Using Geostatistical Techniques

by Edwin Villagrán, Jorge Flores-Velazquez, Mohammad Akrami and Carlos Bojacá

Sensors 2022, 22(10), 3925; https://doi.org/10.3390/s22103925 - 22 May 2022

Cited by 2 | Viewed by 1652

Abstract

In Colombia, the second-largest exporter of cut flowers worldwide and one of the South American countries with the largest area of crops under cover, passive or naturally ventilated greenhouses predominate. Locally, there are several types of greenhouses that differ in architecture, size, height, shape of roof and ventilation surfaces, of which many characteristics of the microclimate generated in their interior environment are unknown. This generates productive limitations that in some way may be limiting the yield, quality and health of the final products harvested; in addition, Colombian producers do not have the ability to monitor the microclimate of their farms, much less to correlate microclimate data with data on crop production and yield. Therefore, there is a need for the Colombian grower to know the most relevant microclimate characteristics generated in the main greenhouses used locally. The objective of this work was to carry out a microclimatic characterization of the five most used types of greenhouses in Colombia. The main results allowed determining that in these structures, there are conditions of high humidity and low vapor pressure for several hours of the day, which affects the physiological processes of growth and development of the plants. It was also identified that for each type of greenhouse, depending on the level of radiation, there is a significant microclimatic heterogeneity that may be the cause of the heterogeneity in plant growth, which is a common characteristic observed by the technical cultivation personnel. Therefore, it can be concluded that it is urgent to propose microclimatic optimization strategies to help ensure the sustainability of the most important production systems in the country. Full article

(This article belongs to the Special Issue Advances in Control and Automation in Smart Agriculture)

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