Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

IoT for Smart Agriculture

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 52865

Share This Special Issue

Special Issue Editors

Dr. Dimitrios Piromalis

E-Mail Website
Guest Editor

Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece
Interests: wireless sensors networks; embedded systems; wearables; sensors; renewable resources systems; automotive; and autonomous vehicles design; Internet of Things; data acquisition; cyber–physical systems; autonomous vehicles; hardware design; smart agriculture; energy management
Special Issues, Collections and Topics in MDPI journals

Dr. Konstantinos G. Arvanitis

E-Mail Website
Guest Editor

Department of Natural Resources Development and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Street, 11855 Athens, Greece
Interests: process control; computational intelligence; automation in agriculture; wireless sensor networks; microgrids’ management
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Panagiotis Papageorgas

E-Mail Website
Guest Editor

Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece
Interests: embedded systems; wireless sensor networks with IoT applications in smart grids; smart cities; internet of energy; LPWAN technologies; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of the fourth industrial revolution (Industry 4.0), the Digital Transformation of nearly everything has been seen as the grand aim. As regards agriculture, Industry 4.0 has imposed its influence to such an extent that we have come to speak about digital agriculture, or, even better, Agriculture 4.0. In this context, smart agriculture can include and integrate all the challenges and innovations, respectively, for the sake of delivering new and improved solutions and services to all the stakeholders (experts, farmers, traders, consumers, etc.). The Internet of Things (IoT), the flagship of Industry 4.0 technologies, by ensuring the ability to acquire in situ measurements remotely and in real time, shifts the interest from just using agricultural wireless sensors networks for solving single problems to considering agriculture in the context of cyberphysical systems to support a better understanding and modeling of physical processes.

This Special Issue aims to gather contributions in the form of original research papers and a limited number of reviews exploring developments and advancements in the “IoT for Smart Agriculture”. This includes the optimization of any of the wireless sensing devices’ design aspects (design for hostile external agricultural environments, innovation in hardware, software and firmware, robustness and reliability, EDGE computing integration, integration of AI for in-situ analytics, energy autonomy, etc.), the networking of “Things” and services with an emphasis on the application for the agricultural domain (i.e. communication protocols, cellular communications, context-aware middleware, cloud computing, etc.), and new smart agriculture applications according to Agriculture 4.0 (i.e., the combination of real-time data from the field and AI-based analytics to improve decision support operations, use of the concept of Digital Twins for modeling and control, use of a cyberphysical system approach to manage and exert control over diversity and complexity of physical systems in the agricultural domain, etc.).

Dr. Dimitrios Piromalis
Prof. Dr. Konstantinos G. Arvanitis
Prof. Dr. Panagiotis Papageorgas
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

Smart agriculture
Precision agriculture
Agriculture 4.0
Digital agriculture
Wireless sensors networks (WSN)
Cyberphysical systems (CPS)
Agricultural Digital Twins
AI in situ
Wireless connectivity
Wireless networks
Low-power wide area networks
Hardware design
Context-aware middleware
Cloud computing
Battery energy management
Location and position tracing

Related Special Issue

IoT for Smart Agriculture—2nd Edition in Sensors

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

17 pages, 9590 KiB

Open AccessArticle

WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture

by Florian Kitzler, Norbert Barta, Reinhard W. Neugschwandtner, Andreas Gronauer and Viktoria Motsch

Sensors 2023, 23(5), 2713; https://doi.org/10.3390/s23052713 - 01 Mar 2023

Cited by 4 | Viewed by 3441

Abstract

Smart farming (SF) applications rely on robust and accurate computer vision systems. An important computer vision task in agriculture is semantic segmentation, which aims to classify each pixel of an image and can be used for selective weed removal. State-of-the-art implementations use convolutional neural networks (CNN) that are trained on large image datasets. In agriculture, publicly available RGB image datasets are scarce and often lack detailed ground-truth information. In contrast to agriculture, other research areas feature RGB-D datasets that combine color (RGB) with additional distance (D) information. Such results show that including distance as an additional modality can improve model performance further. Therefore, we introduce WE3DS as the first RGB-D image dataset for multi-class plant species semantic segmentation in crop farming. It contains 2568 RGB-D images (color image and distance map) and corresponding hand-annotated ground-truth masks. Images were taken under natural light conditions using an RGB-D sensor consisting of two RGB cameras in a stereo setup. Further, we provide a benchmark for RGB-D semantic segmentation on the WE3DS dataset and compare it with a solely RGB-based model. Our trained models achieve up to 70.7% mean Intersection over Union (mIoU) for discriminating between soil, seven crop species, and ten weed species. Finally, our work confirms the finding that additional distance information improves segmentation quality. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Figure 1

16 pages, 1205 KiB

Open AccessArticle

A Secure IoT-Based Irrigation System for Precision Agriculture Using the Expeditious Cipher

by Cherine Fathy and Hassan M. Ali

Sensors 2023, 23(4), 2091; https://doi.org/10.3390/s23042091 - 13 Feb 2023

Cited by 13 | Viewed by 3940

Abstract

Due to the recent advances in the domain of smart agriculture as a result of integrating traditional agriculture and the latest information technologies including the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), there is an urgent need to address the information security-related issues and challenges in this field. In this article, we propose the integration of lightweight cryptography techniques into the IoT ecosystem for smart agriculture to meet the requirements of resource-constrained IoT devices. Moreover, we investigate the adoption of a lightweight encryption protocol, namely, the Expeditious Cipher (X-cipher), to create a secure channel between the sensing layer and the broker in the Message Queue Telemetry Transport (MQTT) protocol as well as a secure channel between the broker and its subscribers. Our case study focuses on smart irrigation systems, and the MQTT protocol is deployed as the application messaging protocol in these systems. Smart irrigation strives to decrease the misuse of natural resources by enhancing the efficiency of agricultural irrigation. This secure channel is utilized to eliminate the main security threat in precision agriculture by protecting sensors’ published data from eavesdropping and theft, as well as from unauthorized changes to sensitive data that can negatively impact crops’ development. In addition, the secure channel protects the irrigation decisions made by the data analytics (DA) entity regarding the irrigation time and the quantity of water that is returned to actuators from any alteration. Performance evaluation of our chosen lightweight encryption protocol revealed an improvement in terms of power consumption, execution time, and required memory usage when compared with the Advanced Encryption Standard (AES). Moreover, the selected lightweight encryption protocol outperforms the PRESENT lightweight encryption protocol in terms of throughput and memory usage. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Figure 1

22 pages, 13536 KiB

Open AccessArticle

ThelR547v1—An Asymmetric Dilated Convolutional Neural Network for Real-time Semantic Segmentation of Horticultural Crops

by Md Parvez Islam, Kenji Hatou, Takanori Aihara, Masaki Kawahara, Soki Okamoto, Shuhei Senoo and Kirino Sumire

Sensors 2022, 22(22), 8807; https://doi.org/10.3390/s22228807 - 15 Nov 2022

Cited by 1 | Viewed by 1569

Abstract

Robust and automated image segmentation in high-throughput image-based plant phenotyping has received considerable attention in the last decade. The possibility of this approach has not been well studied due to the time-consuming manual segmentation and lack of appropriate datasets. Segmenting images of greenhouse and open-field grown crops from the background is a challenging task linked to various factors such as complex background (presence of humans, equipment, devices, and machinery for crop management practices), environmental conditions (humidity, cloudy/sunny, fog, rain), occlusion, low-contrast and variability in crops and pose over time. This paper presents a new ubiquitous deep learning architecture ThelR547v1 (Thermal RGB 547 layers version 1) that segmented each pixel as crop or crop canopy from the background (non-crop) in real time by abstracting multi-scale contextual information with reduced memory cost. By evaluating over 37,328 augmented images (aug1: thermal RGB and RGB), our method achieves mean IoU of 0.94 and 0.87 for leaves and background and mean Bf scores of 0.93 and 0.86, respectively. ThelR547v1 has a training accuracy of 96.27%, a training loss of 0.09, a validation accuracy of 96.15%, and a validation loss of 0.10. Qualitative analysis further shows that despite the low resolution of training data, ThelR547v1 successfully distinguishes leaf/canopy pixels from complex and noisy background pixels, enabling it to be used for real-time semantic segmentation of horticultural crops. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Figure 1

15 pages, 264 KiB

Open AccessArticle

Evaluating Brazilian Agriculturalists’ IoT Smart Agriculture Adoption Barriers: Understanding Stakeholder Salience Prior to Launching an Innovation

by Robert Strong, John Thomas Wynn II, James R. Lindner and Karissa Palmer

Sensors 2022, 22(18), 6833; https://doi.org/10.3390/s22186833 - 09 Sep 2022

Cited by 26 | Viewed by 3295

Abstract

The study sought to: (1) evaluate agriculturalists’ characteristics as adopters of IoT smart agriculture technologies, (2) evaluate traits fostering innovation adoption, (3) evaluate the cycle of IoT smart agriculture adoption, and, lastly, (4) discern attributes and barriers of information communication. Researchers utilized a survey design to develop an instrument composed of eight adoption constructs and one personal characteristic construct and distributed it to agriculturalists at an agricultural exposition in Rio Grande do Sul. Three-hundred-forty-four (n = 344) agriculturalists responded to the data collection instrument. Adopter characteristics of agriculturalists were educated, higher consciousness of social status, larger understanding of technology use, and more likely identified as opinion leaders in communities. Innovation traits advantageous to IoT adoption regarding smart agriculture innovations were: (a) simplistic, (b) easily communicated to a targeted audience, (c) socially accepted, and (d) larger degrees of functionality. Smart agriculture innovation’s elevated levels of observability and compatibility coupled with the innovation’s low complexity were the diffusion elements predicting agriculturalists’ adoption. Agriculturalists’ beliefs in barriers to adopting IoT innovations were excessive complexity and minimal compatibility. Practitioners or change agents should promote IoT smart agriculture technologies to opinion leaders, reduce the innovation’s complexity, and amplify educational opportunities for technologies. The existing sum of IoT smart agriculture adoption literature with stakeholders and actors is descriptive and limited, which constitutes this inquiry as unique. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

21 pages, 3940 KiB

Open AccessArticle

A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming

by Navod Neranjan Thilakarathne, Muhammad Saifullah Abu Bakar, Pg Emerolylariffion Abas and Hayati Yassin

Sensors 2022, 22(16), 6299; https://doi.org/10.3390/s22166299 - 22 Aug 2022

Cited by 22 | Viewed by 5084

Abstract

Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to overcome many of the challenges associated with their farming activities by enabling precise and timely decision making on the basis of data that are observed and subsequently converged. In this regard, Artificial Intelligence (AI) holds a key place, whereby it can assist key stakeholders in making precise decisions regarding the conditions on their farms. Machine Learning (ML), which is a branch of AI, enables systems to learn and improve from their experience without explicitly being programmed, by imitating intelligent behavior in solving tasks in a manner that requires low computational power. For the time being, ML is involved in a variety of aspects of farming, assisting ranchers in making smarter decisions on the basis of the observed data. In this study, we provide an overview of AI-driven precision farming/agriculture with related work and then propose a novel cloud-based ML-powered crop recommendation platform to assist farmers in deciding which crops need to be harvested based on a variety of known parameters. Moreover, in this paper, we compare five predictive ML algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM)—to identify the best-performing ML algorithm on which to build our recommendation platform as a cloud-based service with the intention of offering precision farming solutions that are free and open source, as will lead to the growth and adoption of precision farming solutions in the long run. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Figure 1

24 pages, 155272 KiB

Open AccessArticle

LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile

by Mohamed A. Ahmed, Jose Luis Gallardo, Marcos D. Zuniga, Manuel A. Pedraza, Gonzalo Carvajal, Nicolás Jara and Rodrigo Carvajal

Sensors 2022, 22(8), 2824; https://doi.org/10.3390/s22082824 - 07 Apr 2022

Cited by 18 | Viewed by 9497

Abstract

Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Figure 1

16 pages, 5260 KiB

Open AccessArticle

Narrow-Band Light-Emitting Diodes (LEDs) Effects on Sunflower (Helianthus annuus) Sprouts with Remote Monitoring and Recording by Internet of Things Device

by Thitiya Theparod and Supakorn Harnsoongnoen

Sensors 2022, 22(4), 1503; https://doi.org/10.3390/s22041503 - 15 Feb 2022

Cited by 4 | Viewed by 3073

Abstract

Previous studies have demonstrated that light quality critically affects plant development and growth; however, the response depends upon the plant species. This research aims to examine the effects of different light wavelengths on sunflower (Helianthus annuus) sprouts that were stimulated during the night. Natural light and narrow-band light-emitting diodes (LEDs) were used for an analysis of sunflower sprouts grown under full light and specific light wavelengths. Sunflower seeds were germinated under different light spectra including red, blue, white, and natural light. Luminosity, temperature, and humidity sensors were installed in the plant nursery and remotely monitored and recorded by an Internet of Things (IoT) device. The experiment examined seed germination for seven days. The results showed that the red light had the most influence on sunflower seed germination, while the natural light had the most influence on the increase in the root and hypocotyl lengths. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Figure 1

27 pages, 5945 KiB

Open AccessArticle

Irriman Platform: Enhancing Farming Sustainability through Cloud Computing Techniques for Irrigation Management

by Manuel Forcén-Muñoz, Nieves Pavón-Pulido, Juan Antonio López-Riquelme, Abdelmalek Temnani-Rajjaf, Pablo Berríos, Raul Morais and Alejandro Pérez-Pastor

Sensors 2022, 22(1), 228; https://doi.org/10.3390/s22010228 - 29 Dec 2021

Cited by 7 | Viewed by 2186

Abstract

Crop sustainability is essential for balancing economic development and environmental care, mainly in strong and very competitive regions in the agri-food sector, such as the Region of Murcia in Spain, considered to be the orchard of Europe, despite being a semi-arid area with an important scarcity of fresh water. In this region, farmers apply efficient techniques to minimize supplies and maximize quality and productivity; however, the effects of climate change and the degradation of significant natural environments, such as, the “Mar Menor”, the most extent saltwater lagoon of Europe, threatened by resources overexploitation, lead to the search of even better irrigation management techniques to avoid certain effects which could damage the quaternary aquifer connected to such lagoon. This paper describes the Irriman Platform, a system based on Cloud Computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and a novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists to optimize irrigation procedures through a usable web-based tool which allows them to elaborate irrigation plans and to evaluate their effectiveness over crops. The system has been deployed in a large number of representative crops, located along near 50,000 ha of the surface, during several phenological cycles. Results demonstrate that the system enables crop monitoring and irrigation optimization, and makes interaction between farmers and agronomists easier. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Figure 1

14 pages, 12529 KiB

Open AccessArticle

Real-Time Environmental Monitoring for Aquaculture Using a LoRaWAN-Based IoT Sensor Network

by Harvey Bates, Matthew Pierce and Allen Benter

Sensors 2021, 21(23), 7963; https://doi.org/10.3390/s21237963 - 29 Nov 2021

Cited by 10 | Viewed by 4793

Abstract

IoT-enabled devices are making it easier and cheaper than ever to capture in situ environmental data and deliver these data—in the form of graphical visualisations—to farmers in a matter of seconds. In this work we describe an aquaculture focused environmental monitoring network consisting of LoRaWAN-enabled atmospheric and marine sensors attached to buoys on Clyde River, located on the South Coast of New South Wales, Australia. This sensor network provides oyster farmers operating on the river with the capacity to make informed, accurate and rapid decisions that enhance their ability to respond to adverse environmental events—typically flooding and heat waves. The system represents an end-to-end approach that involves deploying a sensor network, analysing the data, creating visualisations in collaboration with farmers and delivering them to them in real-time via a website known as FarmDecisionTECH

^{®}

. We compared this network with previously available infrastructure, the results of which demonstrate that an in situ weather station was ∼5

^{\circ}

C hotter than the closest available real-time weather station (∼20 km away from Clyde River) during a summertime heat wave. Heat waves can result in oysters dying due to exposure if temperatures rise above 30

^{\circ}

C for extended periods of time (such as heat waves), which will mean a loss in income for the farmers; thus, this work stresses the need for accurate in situ monitoring to prevent the loss of oysters through informed farm management practices. Finally, an approach is proposed to present high-dimensional datasets captured from the sensor network to oyster farmers in a clear and informative manner. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Figure 1

28 pages, 5719 KiB

Open AccessArticle

Efficient IoT-Based Control for a Smart Subsurface Irrigation System to Enhance Irrigation Management of Date Palm

by Maged Mohammed, Khaled Riad and Nashi Alqahtani

Sensors 2021, 21(12), 3942; https://doi.org/10.3390/s21123942 - 08 Jun 2021

Cited by 35 | Viewed by 5222

Abstract

Drought is the most severe problem for agricultural production, and the intensity of this problem is increasing in most cultivated areas around the world. Hence improving water productivity is the primary purpose of sustainable agriculture. This study aimed to use cloud IoT solutions to control a modern subsurface irrigation system for improving irrigation management of date palms in arid regions. To achieve this goal, we designed, constructed, and validated the performance of a fully automated controlled subsurface irrigation system (CSIS) to monitor and control the irrigation water amount remotely. The CSIS is based on an autonomous sensors network to instantly collect the climatic parameters and volumetric soil water content in the study area. Therefore, we employed the ThingSpeak cloud platform to host sensor readings, perform algorithmic analysis, instant visualize the live data, create event-based alerts to the user, and send instructions to the IoT devices. The validation of the CSIS proved that automatically irrigating date palm trees controlled by the sensor-based irrigation scheduling (S-BIS) is more efficient than the time-based irrigation scheduling (T-BIS). The S-BIS provided the date palm with the optimum irrigation water amount at the opportune time directly in the functional root zone. Generally, the S-BIS and T-BIS of CSIS reduced the applied irrigation water amount by 64.1% and 61.2%, respectively, compared with traditional surface irrigation (TSI). The total annual amount of applied irrigation water for CSIS with S-BIS method, CSIS with T-BIS method, and TSI was 21.04, 22.76, and 58.71 m³ palm⁻¹, respectively. The water productivity at the CSIS with S-BIS (1.783 kg m⁻³) and T-BIS (1.44 kg m⁻³) methods was significantly higher compared to the TSI (0.531 kg m⁻³). The CSIS with the S-BIS method kept the volumetric water content in the functional root zone next to the field capacity compared to the T-BIS method. The deigned CSIS with the S-BIS method characterized by the positive impact on the irrigation water management and enhancement on fruit yield of the date palm is quite proper for date palm irrigation in the arid regions. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Figure 1

Review

Jump to: Research

38 pages, 3819 KiB

Open AccessReview

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review

by Nikolaos Peladarinos, Dimitrios Piromalis, Vasileios Cheimaras, Efthymios Tserepas, Radu Adrian Munteanu and Panagiotis Papageorgas

Sensors 2023, 23(16), 7128; https://doi.org/10.3390/s23167128 - 11 Aug 2023

Cited by 12 | Viewed by 6459

Abstract

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

► Show Figures

Journal Menu

Journal Browser

IoT for Smart Agriculture

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Related Special Issue

Published Papers (11 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI