sensors-logo

Journal Browser

Journal Browser

Special Issue "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).

Special Issue Editors

Dr. Juha-Pekka Soininen
Website
Guest Editor
Technical Research Centre of Finland
Interests: situational awareness; cyber-physical systems; Internet of Things; smart agriculture
Prof. Dr. Carlos Kamienski
Website
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

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 papers will be 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 2000 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

  • Internet of Things
  • Precision agriculture
  • Sensors.

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Design, Construction and Testing of IoT Based Automated Indoor Vertical Hydroponics Farming Test-Bed in Qatar
Sensors 2020, 20(19), 5637; https://doi.org/10.3390/s20195637 - 02 Oct 2020
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)
Show Figures

Figure 1

Open AccessArticle
Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions
Sensors 2020, 20(19), 5569; https://doi.org/10.3390/s20195569 - 28 Sep 2020
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)
Show Figures

Figure 1

Open AccessArticle
IoT Sensing Platform as a Driver for Digital Farming in Rural Africa
Sensors 2020, 20(12), 3511; https://doi.org/10.3390/s20123511 - 21 Jun 2020
Cited by 2
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)
Show Figures

Figure 1

Open AccessFeature PaperArticle
An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture
Sensors 2020, 20(7), 2081; https://doi.org/10.3390/s20072081 - 07 Apr 2020
Cited by 6
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)
Show Figures

Figure 1

Back to TopTop