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IoT-Based Precision Agriculture

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

Deadline for manuscript submissions: closed (20 October 2020) | Viewed by 80364

Special Issue Editors


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Guest Editor
Technical Research Centre of Finland
Interests: situational awareness; cyber-physical systems; Internet of Things; smart agriculture

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Guest Editor
Federal University of ABC (UFABC)
Interests: Internet of Things; smart agriculture; smart cities; Future Internet

Special Issue Information

Dear colleagues,

The Internet of Things (IoT) is pushing its way into all domains, including precision agriculture. It provides a means for more in-depth awareness of the situation in the farms and fields; a means for increasing automation even in more complex, process-related farming such as irrigation; and data for obtaining a broad understanding of the whole food production value chain that can guide stakeholders in their strategic decision-making. The key elements in IoT are that it decouples the data collection from its use, and that it uses standard and low-cost worldwide Internet as a basic data transfer mechanism.  This presents new possibilities for more focused uses, leading to more precise and justified decisions that will benefit farmers and food production.  

This Special Issue is dedicated to publishing articles that tackle the use and development of IoT in precision agriculture. We are especially interested in papers that deal with the following:

Impacts of IoT-based Precision Agriculture

  • Expanding the possibilities of IoT in the precision agriculture domain;
  • New innovations in understanding the situation of crops and soil in farms using IoT;
  • Savings of water, energy, or costs on farms by using IoT;
  • Automation solutions obtained through the help of IoT in agriculture;
  • Increasing environmental awareness in agriculture; and
  • The benefits of opening agriculture-related IoT-based data for large-scale analyses, either for private or public use.

Technologies for IoT-based Precision Agriculture

  • IoT Platforms for smart applications in precision agriculture;
  • Big data techniques for IoT-based precision agriculture;
  • Sensors and actuators for precision agriculture;
  • The use of LPWAN wireless technologies in precision agriculture;
  • Machine learning for precision agriculture;
  • Management of IoT-based precision agriculture applications.

However, other papers focusing on the benefits of IoT-based solutions will be considered as well.

Dr. Juha-Pekka Soininen
Prof. Dr. Carlos Kamienski
Guest Editors

Manuscript Submission Information

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Keywords

  • Internet of Things
  • Precision agriculture
  • Sensors.

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Published Papers (8 papers)

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Research

21 pages, 9943 KiB  
Article
IRRISENS: An IoT Platform Based on Microservices Applied in Commercial-Scale Crops Working in a Multi-Cloud Environment
by Rodrigo Filev Maia, Carlos Ballester Lurbe, Arbind Agrahari Baniya and John Hornbuckle
Sensors 2020, 20(24), 7163; https://doi.org/10.3390/s20247163 - 14 Dec 2020
Cited by 14 | Viewed by 3985
Abstract
Research has shown the multitude of applications that Internet of Things (IoT), cloud computing, and forecast technologies present in every sector. In agriculture, one application is the monitoring of factors that influence crop development to assist in making crop management decisions. Research on [...] Read more.
Research has shown the multitude of applications that Internet of Things (IoT), cloud computing, and forecast technologies present in every sector. In agriculture, one application is the monitoring of factors that influence crop development to assist in making crop management decisions. Research on the application of such technologies in agriculture has been mainly conducted at small experimental sites or under controlled conditions. This research has provided relevant insights and guidelines for the use of different types of sensors, application of a multitude of algorithms to forecast relevant parameters as well as architectural approaches of IoT platforms. However, research on the implementation of IoT platforms at the commercial scale is needed to identify platform requirements to properly function under such conditions. This article evaluates an IoT platform (IRRISENS) based on fully replicable microservices used to sense soil, crop, and atmosphere parameters, interact with third-party cloud services for scheduling irrigation and, potentially, control irrigation automatically. The proposed IoT platform was evaluated during one growing season at four commercial-scale farms on two broadacre irrigated crops with very different water management requirements (rice and cotton). Five main requirements for IoT platforms to be used in agriculture at commercial scale were identified from implementing IRRISENS as an irrigation support tool for rice and cotton production: scalability, flexibility, heterogeneity, robustness to failure, and security. The platform addressed all these requirements. The results showed that the microservice-based approach used is robust against both intermittent and critical failures in the field that could occur in any of the monitored sites. Further, processing or storage overload caused by datalogger malfunctioning or other reasons at one farm did not affect the platform’s performance. The platform was able to deal with different types of data heterogeneity. Since there are no shared microservices among farms, the IoT platform proposed here also provides data isolation, maintaining data confidentiality for each user, which is relevant in a commercial farm scenario. Full article
(This article belongs to the Special Issue IoT-Based Precision Agriculture)
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32 pages, 3027 KiB  
Article
Exploring the Adoption of Precision Agriculture for Irrigation in the Context of Agriculture 4.0: The Key Role of Internet of Things
by Sergio Monteleone, Edmilson Alves de Moraes, Brenno Tondato de Faria, Plinio Thomaz Aquino Junior, Rodrigo Filev Maia, André Torre Neto and Attilio Toscano
Sensors 2020, 20(24), 7091; https://doi.org/10.3390/s20247091 - 11 Dec 2020
Cited by 48 | Viewed by 8831
Abstract
In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little [...] Read more.
In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little research has been carried out about Agriculture 4.0, as well as to farmer behavior and operations management. This work explores what drives the adoption of PA in the Agriculture 4.0 context, focusing on farmer behavior and operations management. As a result of a multimethod approach, the factors explaining the PA adoption in the Agriculture 4.0 context and a model of irrigation operations management are proposed. Six simulation scenarios are performed to study the relationships among the factors involved in irrigation planning. Empirical findings contribute to a better understanding of what Agriculture 4.0 is and to expand the possibilities of IoT in the PA domain. This work also contributes to the discussion on Agriculture 4.0, thanks to multidisciplinary research bringing together the different perspectives of PA, IoT and operations management. Moreover, this research highlights the key role of IoT, considering the farmer’s possible choice to adopt several IoT sensing technologies for data collection. Full article
(This article belongs to the Special Issue IoT-Based Precision Agriculture)
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16 pages, 10985 KiB  
Article
Whole-Field Reinforcement Learning: A Fully Autonomous Aerial Scouting Method for Precision Agriculture
by Zichen Zhang, Jayson Boubin, Christopher Stewart and Sami Khanal
Sensors 2020, 20(22), 6585; https://doi.org/10.3390/s20226585 - 18 Nov 2020
Cited by 35 | Viewed by 4009
Abstract
Unmanned aerial systems (UAS) are increasingly used in precision agriculture to collect crop health related data. UAS can capture data more often and more cost-effectively than sending human scouts into the field. However, in large crop fields, flight time, and hence data collection, [...] Read more.
Unmanned aerial systems (UAS) are increasingly used in precision agriculture to collect crop health related data. UAS can capture data more often and more cost-effectively than sending human scouts into the field. However, in large crop fields, flight time, and hence data collection, is limited by battery life. In a conventional UAS approach, human operators are required to exchange depleted batteries many times, which can be costly and time consuming. In this study, we developed a novel, fully autonomous aerial scouting approach that preserves battery life by sampling sections of a field for sensing and predicting crop health for the whole field. Our approach uses reinforcement learning (RL) and convolutional neural networks (CNN) to accurately and autonomously sample the field. To develop and test the approach, we ran flight simulations on an aerial image dataset collected from an 80-acre corn field. The excess green vegetation Index was used as a proxy for crop health condition. Compared to the conventional UAS scouting approach, the proposed scouting approach sampled 40% of the field, predicted crop health with 89.8% accuracy, reduced labor cost by 4.8× and increased agricultural profits by 1.36×. Full article
(This article belongs to the Special Issue IoT-Based Precision Agriculture)
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21 pages, 982 KiB  
Article
AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things
by Kamran Ahmad Awan, Ikram Ud Din, Ahmad Almogren and Hisham Almajed
Sensors 2020, 20(21), 6174; https://doi.org/10.3390/s20216174 - 29 Oct 2020
Cited by 37 | Viewed by 4448
Abstract
Internet of Things (IoT) provides a diverse platform to automate things where smart agriculture is one of the most promising concepts in the field of Internet of Agriculture Things (IoAT). Due to the requirements of more processing power for computations and predictions, the [...] Read more.
Internet of Things (IoT) provides a diverse platform to automate things where smart agriculture is one of the most promising concepts in the field of Internet of Agriculture Things (IoAT). Due to the requirements of more processing power for computations and predictions, the concept of Cloud-based smart agriculture is proposed for autonomic systems. This is where digital innovation and technology helps to improve the quality of life in the area of urbanization expansion. For the integration of cloud in smart agriculture, the system is shown to have security and privacy challenges, and most significantly, the identification of malicious and compromised nodes along with a secure transmission of information between sensors, cloud, and base station (BS). The identification of malicious and compromised node among soil sensors communicating with the BS is a notable challenge in the BS to cloud communications. The trust management mechanism is proposed as one of the solutions providing a lightweight approach to identify these nodes. In this article, we have proposed a novel trust management mechanism to identify malicious and compromised nodes by utilizing trust parameters. The trust mechanism is an event-driven process that computes trust based on the pre-defined time interval and utilizes the previous trust degree to develop an absolute trust degree. The system also maintains the trust degree of a BS and cloud service providers using distinct approaches. We have also performed extensive simulations to evaluate the performance of the proposed mechanism against several potential attacks. In addition, this research helps to create friendlier environments and efficient agricultural productions for the migration of people to the cities. Full article
(This article belongs to the Special Issue IoT-Based Precision Agriculture)
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24 pages, 9080 KiB  
Article
Design, Construction and Testing of IoT Based Automated Indoor Vertical Hydroponics Farming Test-Bed in Qatar
by Muhammad E. H. Chowdhury, Amith Khandakar, Saba Ahmed, Fatima Al-Khuzaei, Jalaa Hamdalla, Fahmida Haque, Mamun Bin Ibne Reaz, Ahmed Al Shafei and Nasser Al-Emadi
Sensors 2020, 20(19), 5637; https://doi.org/10.3390/s20195637 - 2 Oct 2020
Cited by 73 | Viewed by 29521
Abstract
Growing plants in the gulf region can be challenging as it is mostly desert, and the climate is dry. A few species of plants have the capability to grow in such a climate. However, those plants are not suitable as a food source. [...] Read more.
Growing plants in the gulf region can be challenging as it is mostly desert, and the climate is dry. A few species of plants have the capability to grow in such a climate. However, those plants are not suitable as a food source. The aim of this work is to design and construct an indoor automatic vertical hydroponic system that does not depend on the outside climate. The designed system is capable to grow common type of crops that can be used as a food source inside homes without the need of large space. The design of the system was made after studying different types of vertical hydroponic systems in terms of price, power consumption and suitability to be built as an indoor automated system. A microcontroller was working as a brain of the system, which communicates with different types of sensors to control all the system parameters and to minimize the human intervention. An open internet of things (IoT) platform was used to store and display the system parameters and graphical interface for remote access. The designed system is capable of maintaining healthy growing parameters for the plants with minimal input from the user. The functionality of the overall system was confirmed by evaluating the response from individual system components and monitoring them in the IoT platform. The system was consuming 120.59 and 230.59 kWh respectively without and with air conditioning control during peak summer, which is equivalent to the system running cost of 13.26 and 25.36 Qatari Riyal (QAR) respectively. This system was circulating around 104 k gallons of nutrient solution monthly however, only 8–10 L water was consumed by the system. This system offers real-time notifications to alert the hydroponic system user when the conditions are not favorable. So, the user can monitor several parameters without using laboratory instruments, which will allow to control the entire system remotely. Moreover, the system also provides a wide range of information, which could be essential for plant researchers and provides a greater understanding of how the key parameters of hydroponic system correlate with plant growth. The proposed platform can be used both for quantitatively optimizing the setup of the indoor farming and for automating some of the most labor-intensive maintenance activities. Moreover, such a monitoring system can also potentially be used for high-level decision making, once enough data will be collected. This work presents significant opportunities for the people who live in the gulf region to produce food as per their requirements. Full article
(This article belongs to the Special Issue IoT-Based Precision Agriculture)
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18 pages, 5709 KiB  
Article
Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions
by Jamil Ahmad, Bilal Jan, Haleem Farman, Wakeel Ahmad and Atta Ullah
Sensors 2020, 20(19), 5569; https://doi.org/10.3390/s20195569 - 28 Sep 2020
Cited by 42 | Viewed by 4926
Abstract
The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of [...] Read more.
The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of mobile computing devices and the advancements in artificial intelligence have created opportunities for developing technologies to assist farmers in plant disease detection and treatment. To this end, deep learning has been widely used for disease detection in plants with highly favorable outcomes. In this paper, we propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices. As opposed to the publicly available datasets, images used in this study were collected in the field by considering important parameters of image-capturing devices such as angle, scale, orientation, and environmental conditions. Furthermore, extensive data augmentation was used to expand the dataset and make it more challenging to enable robust training. Investigations of recent architectures revealed that transfer learning of scale-sensitive models like Inception yield results much better with such challenging datasets with extensive data augmentation. Through parameter quantization, we optimized the Inception-v3 model for deployment on resource-constrained devices. The optimized model successfully classified healthy and diseased fruits and leaves with more than 92% accuracy on mobile devices. Full article
(This article belongs to the Special Issue IoT-Based Precision Agriculture)
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25 pages, 18358 KiB  
Article
IoT Sensing Platform as a Driver for Digital Farming in Rural Africa
by Antonio Oliveira-Jr, Carlos Resende, André Pereira, Pedro Madureira, João Gonçalves, Ruben Moutinho, Filipe Soares and Waldir Moreira
Sensors 2020, 20(12), 3511; https://doi.org/10.3390/s20123511 - 21 Jun 2020
Cited by 23 | Viewed by 7988
Abstract
Small-scale farming can benefit from the usage of information and communication technology (ICT) to improve crop and soil management and increase yield. However, in order to introduce digital farming in rural areas, related ICT solutions must be viable, seamless and easy to use, [...] Read more.
Small-scale farming can benefit from the usage of information and communication technology (ICT) to improve crop and soil management and increase yield. However, in order to introduce digital farming in rural areas, related ICT solutions must be viable, seamless and easy to use, since most farmers are not acquainted with technology. With that in mind, this paper proposes an Internet of Things (IoT) sensing platform that provides information on the state of the soil and surrounding environment in terms of pH, moisture, texture, colour, air temperature, and light. This platform is coupled with computer vision to further analyze and understand soil characteristics. Moreover, the platform hardware is housed in a specifically designed robust casing to allow easy assembly, transport, and protection from the deployment environment. To achieve requirements of usability and reproducibility, the architecture of the IoT sensing platform is based on low-cost, off-the-shelf hardware and software modularity, following a do-it-yourself approach and supporting further extension. In-lab validations of the platform were carried out to finetune its components, showing the platform’s potential for application in rural areas by introducing digital farming to small-scale farmers, and help them delivering better produce and increasing income. Full article
(This article belongs to the Special Issue IoT-Based Precision Agriculture)
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14 pages, 2401 KiB  
Article
An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture
by Khalid Haseeb, Ikram Ud Din, Ahmad Almogren and Naveed Islam
Sensors 2020, 20(7), 2081; https://doi.org/10.3390/s20072081 - 7 Apr 2020
Cited by 233 | Viewed by 13393
Abstract
Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using [...] Read more.
Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions. Full article
(This article belongs to the Special Issue IoT-Based Precision Agriculture)
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