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The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0

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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 40448

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Special Issue Editors

Dr. Jorge García Morillo

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Guest Editor

Department of Agronomy, University of Córdoba, International Campus of Excellence ceiA3, 14005 Córdoba, Spain
Interests: irrigation engineering; water and energy nexus in agriculture; water footprint in the agroindustry; TICs in agriculture

Prof. Dr. Emilio Camacho Poyato

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Guest Editor

Dr. Juan Manuel Díaz-Cabrera

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Guest Editor

Department of Electrical Engineering and Automatic, University of Córdoba, International Campus of Excellence ceiA3, 14005 Córdoba, Spain
Interests: data acquisition; data processing; open data; virtual instruments; IoT agriculture sensors; communications protocols

Special Issue Information

Dear Colleagues,

Crop information is converted into profitable decisions, only when it is managed efficiently. Current advances in open data management are making smart agriculture grow exponentially, as data have become the key element of modern agriculture. The wide variety of sensors that are being used in agriculture generate a huge volume of data. The analysis of these data is done using a special technique known as big data, a new discipline for processing massive data. This discipline uses artificial intelligence (AI) techniques, predictive systems, models, neural networks, and other tools designed to support the decision-making processes. The use of new sensors in agriculture, its communication protocols based on IoT technology (wifi, LoRa, Sigfox, NBIoT, etc,) supported by cloud database and cloud computing, together with these new data processing tools, have resulted in what is known as Agriculture 5.0. In brief, it is about considering agriculture as an industry in which new technological and scientific advances are used to achieve higher productivity with reduced inputs and the lowest possible impact on the environment.

Research articles, communications, case studies, and review articles are all welcome. More specifically, we invite you to contribute to this Issue by submitting original research related to everything concerning the technological aspects and digitisation processes involved in Agriculture 5.0, such as: sensors development and its conectivity, IoT communication protocols, open data, cloud database and computing, big data, AI, predictive systems, and decision-making tools.

Dr. Jorge García Morillo
Prof. Dr. Emilio Camacho Poyato
Dr. Juan Manuel Díaz-Cabrera
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. Agronomy is an international peer-reviewed open access monthly 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

smart agriculture
Agriculture 5.0
sensors
communication protocols
open data
big data
AI
predictive systems
decision-making tools
web platforms and mobile apps
IoT technology
cloud database and computing

Published Papers (14 papers)

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14 pages, 4509 KiB

Open AccessArticle

Time Series Feature Extraction Using Transfer Learning Technology for Crop Pest Prediction

by Ming-Fong Tsai, Chun-Ying Lan, Neng-Chung Wang and Lien-Wu Chen

Agronomy 2023, 13(3), 792; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13030792 - 09 Mar 2023

Viewed by 1802

Abstract

Following the rapid development of information and communication technology, and the huge amounts of data that have undergone explosive growth, artificial intelligence and machine learning have been used for predictive analysis in many fields. However, the prediction accuracy of these machine learning recognition models depends on the quality of the features selected for training. It is therefore very important to analyse characteristics that are meaningful and in line with the target variables as the training conditions for machine learning recognition models. In this paper, we analyse the correlation between features and target variables using the Pearson product-moment correlation coefficient, and integrate transfer learning technology for sequential feature extraction to enhance the prediction accuracy of a machine learning recognition model for the prediction of multiple crop pests and diseases as the performance verification target of the proposed method. The performance of our machine learning recognition model is compared with schemes in related work, and our approach is shown to increase the prediction accuracy by between 3% and 15%. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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24 pages, 3851 KiB

Open AccessArticle

Design and Evaluation of a Smart Ex Vitro Acclimatization System for Tissue Culture Plantlets

by Maged Mohammed, Muhammad Munir and Hesham S. Ghazzawy

Agronomy 2023, 13(1), 78; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13010078 - 26 Dec 2022

Cited by 5 | Viewed by 3271

Abstract

One of the technological advancements in agricultural production is the tissue culture propagation technique, commonly used for mass multiplication and disease-free plants. The necessity for date palm tissue culture emerged from the inability of traditional propagation methods’ offshoots to meet the immediate demands for significant amounts of planting material for commercial cultivars. Tissue culture plantlets are produced in a protected aseptic in vitro environment where all growth variables are strictly controlled. The challenges occur when these plantlets are transferred to an ex vitro climate for acclimatization. Traditional glasshouses are frequently used; however, this has substantial mortality consequences. In the present study, a novel IoT-based automated ex vitro acclimatization system (E-VAS) was designed and evaluated for the acclimatization of date palm plantlets (cv. Khalas) to enhance their morpho-physiological attributes and reduce the mortality rate and the contamination risk through minimal human contact. The experimental findings showed that the morpho-physiological parameters of 6- and 12-month-old plants were higher when acclimatized in the prototype E-VAS compared to the traditional glasshouse acclimatization system (TGAS). The maximum plant mortality percentage occurred within the first month of the transfer from the in vitro to ex vitro environment in both systems, which gradually declined up to six months; after that, no significant plant mortality was observed. About 6% mortality was recorded in E-VAS, whereas 18% in TGAS within the first month of acclimatization. After six months of study, an overall 14% mortality was recorded in E-VAS compared to 41% in TGAS. The proposed automated system has a significant potential to address the growing demand for the rapid multiplication of tissue culture-produced planting materials since the plant survival rate and phenotype quality were much higher in E-VAS than in the conventional manual system that the present industry follows for commercial production. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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19 pages, 6160 KiB

Open AccessArticle

Development of a Low-Cost Open-Source Platform for Smart Irrigation Systems

by Francisco Puig, Juan Antonio Rodríguez Díaz and María Auxiliadora Soriano

Agronomy 2022, 12(12), 2909; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12122909 - 22 Nov 2022

Cited by 7 | Viewed by 2341

Abstract

Nowadays, smart irrigation is becoming an essential goal in agriculture, where water and energy are increasingly limited resources. Its importance will grow in the coming years in the agricultural sector where the optimal use of resources and environmental sustainability are becoming more important every day. However, implementing smart irrigation is not an easy task for most farmers since it is based on knowledge of the different processes and factors that determine the crop water requirements. Thanks to technological developments, it is possible to design new tools such as sensors or platforms that can be connected to soil-water-plant-atmosphere models to assist in the optimization and automation of irrigation. In this work, a low-cost, open-source IoT system for smart irrigation has been developed that can be easily integrated with other platforms and supports a large number of sensors. The platform uses the FIWARE framework together with customized components and can be deployed using edge computing and/or cloud computing systems. To improve decision-making, the platform integrates an irrigation model that calculates soil water balance and wet bulb dimensions to determine the best irrigation strategy for drip irrigation systems. In addition, an energy efficient open-source datalogger has been designed. The datalogger supports a wide range of communications and is compatible with analog sensors, SDI-12 and RS-485 protocols. The IoT system has been deployed on an olive farm and has been in operation for one irrigation season. Based on the results obtained, advantages of using these technologies over traditional methods are discussed. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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18 pages, 2618 KiB

Open AccessFeature PaperArticle

Spatial Distribution Model of Solar Radiation for Agrivoltaic Land Use in Fixed PV Plants

by José S. Pulido-Mancebo, Rafael López-Luque, Luis Manuel Fernández-Ahumada, José C. Ramírez-Faz, Francisco Javier Gómez-Uceda and Marta Varo-Martínez

Agronomy 2022, 12(11), 2799; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12112799 - 10 Nov 2022

Cited by 8 | Viewed by 1976

Abstract

Agrivoltaics is currently presented as a possible effective solution to one of society’s greatest challenges: responding to the increasing demand for energy and food in an efficient and sustainable manner. To this end, agrivoltaics proposes to combine agricultural and renewable energy production on the same land using photovoltaic technology. The performance of this new production model strongly depends on the interaction between the two systems, agricultural and photovoltaic. In that sense, one of the most important aspects to consider are the effects of the shadows of the photovoltaic panels on the crop land. Therefore, further study of crop behavior under agrivoltaic conditions requires exhaustive knowledge of the spatial distribution of solar radiation within the portion of land between collectors and crops. This study presents a valid methodology to estimate this distribution of solar irradiance in agrivoltaic installations as a function of the photovoltaic installation geometry and the levels of diffuse and direct solar irradiance incident on the crop land. As an example, this methodology was applied to simulate the radiative capture potential of possible photovoltaic plants located in Cordoba, Spain by systematically varying the design variables of the photovoltaic plants. Based on the results obtained, a model correlating the agrivoltaic potential of a photovoltaic plant with its design variables is proposed. Likewise, for the “Alcolea 1” photovoltaic plant (Cordoba, Spain), the solar radiation decay profiles were simulated in the lanes between the photovoltaic collectors where the crops would be planted in the event of converting this plant into an agrivoltaic facility. Thus, the methodology proposed represents an interesting way to determine the agrivoltaic potential of existing grid-connected photovoltaic installations that could be converted into agrivoltaic installations, contributing to the implementation of this new agricultural production model that is more sustainable and environmentally committed to the future. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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24 pages, 11960 KiB

Open AccessArticle

Postharvest Geometric Characterization of Table Olive Bruising from 3D Digitalization

by Ramón González-Merino, Rafael E. Hidalgo-Fernández, Jesús Rodero, Rafael R. Sola-Guirado and Elena Sánchez-López

Agronomy 2022, 12(11), 2732; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12112732 - 03 Nov 2022

Cited by 2 | Viewed by 1353

Abstract

The physical properties of table olive fruit are an important factor in the design of harvesting, transport, classification, and commercialization. The visual quality of the fruits harvested is the most important factor limiting the commercialization of table olives. The mechanical damage during harvesting consists of local tissue degradation, resulting in bruising of the fruits. In recent years, several studies have been carried out to identify physical properties and to calculate indices that characterize the damage to olives. However, all of them are based on 2D techniques. The aim of this work is the determination of new geometric parameters based on a 3D analysis of the scanned olives. The 3D shape parameters have been collated with those obtained by standard 2D shape analysis methods. From the results, it is observed that the use of high-resolution, medium-cost 3D technologies allows a more precise characterization of the shape of damages observed in table olives. To carry out three-dimensional analysis, Boolean operations of the solid and parametric surfaces of the meshes obtained by a 3D scanner have been used. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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15 pages, 5725 KiB

Open AccessArticle

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

by Cristina Martínez-Ruedas, Samuel Yanes-Luis, Juan Manuel Díaz-Cabrera, Daniel Gutiérrez-Reina, Rafael Linares-Burgos and Isabel Luisa Castillejo-González

Agronomy 2022, 12(11), 2700; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12112700 - 31 Oct 2022

Cited by 1 | Viewed by 1608

Abstract

This paper aims to evaluate whether an automatic analysis with deep learning convolutional neural networks techniques offer the ability to efficiently identify olive groves with different intensification patterns by using very high-resolution aerial orthophotographs. First, a sub-image crop classification was carried out. To standardize the size and increase the number of samples of the data training (DT), the crop images were divided into mini-crops (sub-images) using segmentation techniques, which used a different threshold and stride size to consider the mini-crop as suitable for the analysis. The four scenarios evaluated discriminated the sub-images efficiently (accuracies higher than 0.8), obtaining the largest sub-images (H = 120, W = 120) for the highest average accuracy (0.957). The super-intensive olive plantings were the easiest to classify for most of the sub-image sizes. Nevertheless, although traditional olive groves were discriminated accurately, too, the most difficult task was to distinguish between the intensive plantings and the traditional ones. A second phase of the proposed system was to predict the crop at farm-level based on the most frequent class detected in the sub-images of each crop. The results obtained at farm level were slightly lower than at the sub-images level, reaching the highest accuracy (0.826) with an intermediate size image (H = 80, W = 80). Thus, the convolutional neural networks proposed made it possible to automate the classification and discriminate accurately among traditional, intensive, and super-intensive planting systems. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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26 pages, 9488 KiB

Open AccessArticle

Design and Implementation of Artificial Intelligence of Things for Tea (Camellia sinensis L.) Grown in a Plant Factory

by Chung-Liang Chang, Cheng-Chieh Huang and Hung-Wen Chen

Agronomy 2022, 12(10), 2384; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12102384 - 01 Oct 2022

Cited by 2 | Viewed by 2345

Abstract

An innovative plant growth monitoring and environmental control platform is designed and implemented in this study. In addition to using multi-band artificial light sources for plant growth and development, an artificial intelligence of things (AIoT) approach is also utilised for environmental parameter monitoring, control, and the recording of plant growth traits and diseases. The five LED bands are white (5000 K), cool white (5500 K), blue (peak: 450 nm), red (660 nm), and light red (630 nm). The tea plant (Camellia sinensis f. formosana) is irradiated using lighting-emitting diodes (LED) composed of bands of different wavelengths. In addition, the number of leaves, contour area of the leaves, and leaf colour during the growth period of two varieties of tea plants (Taicha No. 18 and Taicha No. 8) under different irradiation intensities are analysed. Morphological image processing and deep learning models are simultaneously used to obtain plant growth characterization traits and diseases. The effect of the spectral distribution of the light source on the growth response of tea leaves and the effect of disease suppression are not fully understood. This study depicts how light quality affects the lighting formula changes in tea plants under controlled environments. The experimental results show that in three wavelength ranges (360–500 nm, 500–600 nm, and 600–760 nm), the light intensity ratio was 2.5:2.0:5.5 when the illuminance intensity was about 150 µmol∙m⁻²∙s⁻¹ with a photoperiod of 20:4 (dark); this enabled more leaves, a smaller contour area of the leaves, and a light green colour of the leaves of the tea plant (Taicha No. 18). In addition, during the lighting treatment, when the ratio of the band with an irradiation intensity of 360–500 nm to that with an irradiation intensity of 500–600 nm was 2:1.5, it resulted in a better leaf disease inhibition effect. When the light intensity was increased to more than 400 µmol∙m⁻²∙s⁻¹, it had little effect on the growth and development of the tea plants and the inhibition of diseases. The results of the study also found that there was a significant difference between the colour of the leaves and the relative chlorophyll content of the tea trees. Finally, the tea plant growth response data obtained from manual records and automatic records are compared and discussed. The accuracy rates of leaf number and disease were 94% and 87%, respectively. Compared with the results of manual measurement and recording, the errors were about 3–15%, which verified the effectiveness and practicability of the proposed solution. The innovative platform provides a data-driven crop modeling application for plant factories. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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15 pages, 2807 KiB

Open AccessEditor’s ChoiceArticle

Prediction of the Effect of Nutrients on Plant Parameters of Rice by Artificial Neural Network

by Tanmoy Shankar, Ganesh Chandra Malik, Mahua Banerjee, Sudarshan Dutta, Subhashisa Praharaj, Sagar Lalichetti, Sahasransu Mohanty, Dipankar Bhattacharyay, Sagar Maitra, Ahmed Gaber, Ashok K. Das, Ayushi Sharma and Akbar Hossain

Agronomy 2022, 12(9), 2123; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12092123 - 07 Sep 2022

Cited by 12 | Viewed by 2108

Abstract

Rice holds key importance in food and nutritional security across the globe. Nutrient management involving rice has been a matter of interest for a long time owing to the unique production environment of rice. In this research, an artificial neural network-based prediction model was developed to understand the role of individual nutrients (N, P, K, Zn, and S) on different plant parameters (plant height, tiller number, dry matter production, leaf area index, grain yield, and straw yield) of rice. A feed-forward neural network with back-propagation training was developed using the neural network (nnet) toolbox available in Matlab. For the training of the model, data obtained from two consecutive crop seasons over two years (a total of four crops of rice) were used. Nutrients interact with each other, and the resulting effect is an outcome of such interaction; hence, understanding the role of individual nutrients under field conditions becomes difficult. In the present study, an attempt was made to understand the role of individual nutrients in achieving crop growth and yield using an artificial neural network-based prediction model. The model predicts that growth parameters such as plant height, tiller number, and leaf area index often achieve their maximum performance at below the maximum applied dose, while the maximum yield in most cases is achieved at 100% N, P, K, Zn, and S dose. In addition, the present study attempted to understand the impact of individual nutrients on both plant growth and yield in order to optimize nutrient recommendation and nutrient management, thereby minimizing environmental pollution and wastage of nutrients. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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20 pages, 7967 KiB

Open AccessArticle

Methodology for the Automatic Inventory of Olive Groves at the Plot and Polygon Level

by Cristina Martínez-Ruedas, José Emilio Guerrero-Ginel and Elvira Fernández-Ahumada

Agronomy 2022, 12(8), 1735; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12081735 - 22 Jul 2022

Cited by 2 | Viewed by 1255

Abstract

The aim of this study was to develop and validate a methodology to carry out olive grove inventories based on open data sources and automatic photogrammetric and satellite image analysis techniques. To do so, tools and protocols have been developed that have made it possible to automate the capture of images of different characteristics and origins, enable the use of open data sources, as well as integrating and metadating them. They can then be used for the development and validation of algorithms that allow for improving the characterization of olive grove surfaces at the plot and cadastral polygon scales. With the proposed system, an inventory of the Andalusian olive grove has been automatically carried out at the level of cadastral polygons and provinces, which has accounted for a total of 1,519,438 hectares and 171,980,593 olive trees. These data have been contrasted with various official statistical sources, thus ensuring their reliability and even identifying some inconsistencies or errors of some sources. Likewise, the capacity of the Sentinel 2 satellite images to estimate the FCC at the cadastral polygon, parcel and 10 × 10 m pixel level has been demonstrated and quantified, as well as the opportunity to carry out inventories with temporal resolutions of approximately up to 5 days. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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24 pages, 2524 KiB

Open AccessArticle

Solar Fertigation: A Sustainable and Smart IoT-Based Irrigation and Fertilization System for Efficient Water and Nutrient Management

by Uzair Ahmad, Arturo Alvino and Stefano Marino

Agronomy 2022, 12(5), 1012; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12051012 - 23 Apr 2022

Cited by 15 | Viewed by 6932

Abstract

The agricultural sector is one of the major users of water resources. Water is an important asset that needs to be preserved using the latest available technologies. Modern technologies and digital tools can transform the agricultural domain from being manual and static to intelligent and dynamic leading to higher production with lesser human supervision. This study describe the agronomic models that should be integrated with the intelligent system which schedule the irrigation and fertilization according to the plant needs, and monitors and maintains the desired soil moisture content via automatic watering. Solar fertigation is a fertigation support system based on photovoltaic solar power energy and an IoT system for precision irrigation purposes. The system monitors the temperature, radiation, humidity, soil moisture, and other physical parameters. An agronomic DSS platform based on the integration of soil, weather, and plant data and sensors was described. Furthermore, a three-year study on seven ETo models, such as three temperature-, three radiation-, and a combination-based models were tested to evaluate the sustainable ETo estimation and irrigation scheduling in a Mediterranean environment. Results showed that solar fertigation and Hargreaves–Samani (H-S) equation represented a nearby correlation to the standard FAO P–M and does offer a small increase in accuracy of ETo estimates. Furthermore, the hybrid agronomic DSS is suitable for smart fertigation scheduling. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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27 pages, 4119 KiB

Open AccessArticle

Methodological Advances in the Design of Photovoltaic Irrigation

by Martín Calero-Lara, Rafael López-Luque and Francisco José Casares

Agronomy 2021, 11(11), 2313; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11112313 - 16 Nov 2021

Cited by 7 | Viewed by 2028

Abstract

In this study, an algorithm has been developed that manages photovoltaic solar energy in such a manner that all generated power is delivered to the system formed by a pump and irrigation network with compensated emitters. The algorithm is based on the daily work matrix that is updated daily by considering water and energy balances. The algorithm determines an irrigation priority for the sectors of irrigation of the farm based on programmed irrigation time and water deficits in the soil and synchronises the energy produced with the energy requirement of the hydraulic system according to the priority set for each day, obtaining the combinations of irrigation sectors appropriate to the photovoltaic power available. It takes into account the increment/decrease in the pressure of the water distribution network in response to increases/decreases in photovoltaic energy by increasing/decreasing the rotational speed of the pump, thus increasing/decreasing the power transferred to the system. The application to a real case of a 10-hectare farm divided into four sectors implies an efficient use of the energy of 26.15% per year and savings in CO₂ emissions of 6.29 tonnes per year. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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18 pages, 2087 KiB

Open AccessEditor’s ChoiceArticle

Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture

by Gabriela Cáceres, Pablo Millán, Mario Pereira and David Lozano

Agronomy 2021, 11(9), 1810; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11091810 - 09 Sep 2021

Cited by 20 | Viewed by 4644

Abstract

The growth of the global population, together with climate change and water scarcity, has made the shift towards efficient and sustainable agriculture increasingly important. Undoubtedly, the recent development of low-cost IoT-based sensors and actuators offers great opportunities in this direction since these devices can be easily deployed to implement advanced monitoring and irrigation control techniques at a farm scale, saving energy and water and decreasing costs. This paper proposes an economic and periodic predictive controller taking advantage of the irrigation periodicity. The goal of the controller is to find an irrigation technique that optimizes water and energy consumption while ensuring adequate levels of soil moisture for crops, achieving the maximum crop yield. For this purpose, the developed predictive controller makes use of soil moisture data at different depths, and it formulates a constrained optimization problem that considers energy and water costs, crop transpiration, and an accurate dynamical nonlinear model of the water dynamics in the soil, reflecting the reality. This controller strategy is compared with a classical irrigation strategy adopted by a human expert in a specific case study, demonstrating that it is possible to obtain significant reductions in water and energy consumption without compromising crop yields. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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Review

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23 pages, 4097 KiB

Open AccessEditor’s ChoiceReview

Challenges and Opportunities of Agriculture Digitalization in Spain

by Ebrahim Navid Sadjadi and Roemi Fernández

Agronomy 2023, 13(1), 259; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13010259 - 14 Jan 2023

Cited by 5 | Viewed by 4251

Abstract

Motivated by the ongoing debate on food security and the global trend of adopting new emerging technologies in the aftermath of COVID-19, this research focuses on the challenges and opportunities of agriculture digitalization in Spain. This process of digital transformation of the agricultural sector is expected to significantly affect productivity, product quality, production costs, sustainability and environmental protection. For this reason, our study reviews the legal, technical, infrastructural, educational, financial and market challenges that can hinder or impose barriers to the digitalization of agriculture in Spain. In addition, the opportunities that digitalization can bring are identified, with the intention of contributing to provide insights that helps strengthen the Spanish agricultural model and make the necessary decision so that professionals in the sector are prepared to adapt to this intense change. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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15 pages, 611 KiB

Open AccessReview

The Role of Radiation in the Modelling of Crop Evapotranspiration from Open Field to Indoor Crops

by Jorge Flores-Velazquez, Mohammad Akrami and Edwin Villagrán

Agronomy 2022, 12(11), 2593; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12112593 - 22 Oct 2022

Cited by 4 | Viewed by 1802

Abstract

The agricultural sector continues to be the largest consumer of useful water. Despite knowing the volume of water required by plants (evapotranspiration), methodologies must be adapted to current production systems. Based on the energy balance (radiation), it is feasible to establish models to estimate evapotranspiration depending on the production system: extensive crops, closed, and interior systems. The objective of this work was to present related research to measure and model the evapotranspiration of crops under current production techniques, based on the energy balance. The original FAO Penman–Monteith model is considered to be the model that best describes the evapotranspiration process, and with advances in instrumentation, there are sensors capable of measuring each of the variables it contains. From this model, procedures have been approximated for its use in extensive crops through remote sensing to calculate evapotranspiration, which jointly integrates the climatic variables and the type and age of the crop, with which real evapotranspiration is obtained. The same Penman–Monteith model has been adapted for use in greenhouse crops, where given the reduced root space and being in a closed environment, it is possible to know the variables specifically. Keeping the root container saturated, crop transpiration will basically depend on the physiology of the plant (LAI, stomatal resistance, etc.) and the characteristics of the air (radiation, VPD, wind speed, etc.). Models based on computational fluid dynamics (CFD) have been developed, which predict the real evapotranspiration of the crop by activating the discrete ordinate (DO) radiation sub-model. For indoor crops, in the absence of solar radiation, and replaced with artificial lights (LEDs)—although it is true that they are hydroponic crops and water can be estimated through a balance of levels—it would be possible to use CFD to estimate transpiration by transforming flux units (Mmol) into radiation (W m⁻²). The transpiration of indoor crops works as a cooling system and stabilizes the environment of the plant factory or vertical farm. In each crop production system (from open field to indoor crops) models have been developed to manage water and microclimate. The result is reports that more than 90% of the water is saved. Full article

(This article belongs to the Special Issue The New Agricultural Revolution: From Traditional Farms to Smart Agriculture—New Technologies in Agriculture 5.0)

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