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Internet-of-Things for Precision Agriculture (IoAT)

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

Deadline for manuscript submissions: closed (3 June 2019) | Viewed by 87719

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Special Issue Editors

Prof. Dr. Ivan Andonovic

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Guest Editor

Department of Electrical and Electronic Engineering, University of Strathclyde, Glasgow G1 1RD, UK
Interests: wireless sensors; machine learning; sensor system; agriculture
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Craig Michie

E-Mail Website
Guest Editor

Department of Electronic & Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK
Interests: wireless sensor networks; Internet-of-Things; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Christos Tachtatzis

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Guest Editor

Department of Electronic and Electrical Engineering, University of Strathclyde, 16 Richmond Street, Glasgow G1 1XQ, UK
Interests: machine learning; partial discharge monitoring; wireless technologies; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture is core to satisfying the ever-increasing worldwide demand for food products of good quality, whilst allaying the societal concerns over animal welfare and reducing heavily the load on environmental resources. The principle is that if the needs of animals and crops are satisfied at the highest granularity, then farmers and the supply chain including consumers will benefit. Over the recent past, the sector has been subject to an increasing drive towards efficiency and performance enhancement to improve sustainability against a backdrop of increasing demand and the volatile trading environments that may emerge as a consequence of political change.

A direct consequence is that the farmers have less time to execute traditional practices and are becoming increasingly reliant on technology. Thus, there is a growing range of opportunities for the delivery of precision farming solutions through the integration of a mix of hardware and software technologies. In turn, the evolution to new business models based on provisioning a range of services to the agricultural community becomes possible, fueling further the ready uptake of technology for the benefit of all operating within the supply chain.

The solutions required to support this evolution harness a number of technologies that follow Internet of Things (IoT) principles. IoT is a platform that allows a network of devices to communicate, gather data and process information collaboratively in the service of individuals or processes. The solutions rely on engineering data driven by consideration of the impacts in order for the derived product/application/service to be not only fit-for-purpose but can also be easily deployed and maintained with a minimum of perturbation to a sector that has followed entrenched practices for many years. These platforms generate large amounts of data in a variety of formats and is ‘Big’ comprising many different streams of single stranded data, but markedly different from the ‘Big Science Data’ sets routinely encountered in drug development and oil exploration. The data can then be translated into actionable information through machine learning, artificial intelligence, statistical and other advanced techniques, models and methods, to create value for the spectrum of stakeholders across the agricultural supply chain, in so doing optimising production and sustaining the security of the food supply.

The Special Issue will capture the latest innovations from fundamental scientific concepts to commercially robust IoT-inspired solutions (Internet of Agricultural Things—IoAT) relevant to the development and adoption of precision agriculture methodologies. The Guest Editors invite submissions that range from new sensors through cloud-based computing to data-driven applications/services. Topics of interest include, but are not limited to, the following themes:

Intelligent Sensing Technologies
Data Architectures and Management
Edge Computing
Network and Communications Technologies
IoT Platform Integration
Machine Learning and Artificial Intelligence
Emerging Applications/Services and Cloud Analytics
Information Visualisation
Security, Privacy and Trust
Inter-Operability and Standards
Emerging Business Models

Prof. Ivan Andonovic
Prof. Craig Michie
Dr. Christos Tachtatzis
Guest Editors

Manuscript Submission Information

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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.

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Keywords

Precision Agriculture
Internet-of-Things Technologies
Data-Driven Applications/Services
Distributed Intelligent Sensor Networks and Applications
Low Power Wireless Connectivity
Edge Computing
Machine Learning
Artificial Intelligence

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Published Papers (5 papers)

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Research

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17 pages, 3688 KiB

Open AccessArticle

Extraction and Research of Crop Feature Points Based on Computer Vision

by Jingwen Cui, Jianping Zhang, Guiling Sun and Bowen Zheng

Sensors 2019, 19(11), 2553; https://doi.org/10.3390/s19112553 - 4 Jun 2019

Cited by 13 | Viewed by 3909

Abstract

Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective. Full article

(This article belongs to the Special Issue Internet-of-Things for Precision Agriculture (IoAT))

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22 pages, 8686 KiB

Open AccessArticle

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production

by Silke Hemming, Feije de Zwart, Anne Elings, Isabella Righini and Anna Petropoulou

Sensors 2019, 19(8), 1807; https://doi.org/10.3390/s19081807 - 16 Apr 2019

Cited by 103 | Viewed by 25640

Abstract

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous greenhouses” aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a greenhouse growing experiment. Each team had a 96 m² modern greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO₂ supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference. Full article

(This article belongs to the Special Issue Internet-of-Things for Precision Agriculture (IoAT))

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21 pages, 10318 KiB

Open AccessArticle

CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture

by Yang-Yang Zheng, Jian-Lei Kong, Xue-Bo Jin, Xiao-Yi Wang, Ting-Li Su and Min Zuo

Sensors 2019, 19(5), 1058; https://doi.org/10.3390/s19051058 - 1 Mar 2019

Cited by 319 | Viewed by 25236

Abstract

Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks. Full article

(This article belongs to the Special Issue Internet-of-Things for Precision Agriculture (IoAT))

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17 pages, 3941 KiB

Open AccessArticle

IoT-Based Strawberry Disease Prediction System for Smart Farming

by Sehan Kim, Meonghun Lee and Changsun Shin

Sensors 2018, 18(11), 4051; https://doi.org/10.3390/s18114051 - 20 Nov 2018

Cited by 127 | Viewed by 12474

Abstract

Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. Only through construction of a comprehensive analysis system can users be provided with predictions of highly probable diseases. In this study, cloud-based technology capable of handling the collection, analysis, and prediction of agricultural environment information in one common platform was developed. The proposed Farm as a Service (FaaS) integrated system supports high-level application services by operating and monitoring farms as well as managing associated devices, data, and models. This system registers, connects, and manages Internet of Things (IoT) devices and analyzes environmental and growth information. In addition, the IoT-Hub network model was constructed in this study. This model supports efficient data transfer for each IoT device as well as communication for non-standard products, and exhibits high communication reliability even in poor communication environments. Thus, IoT-Hub ensures the stability of technology specialized for agricultural environments. The integrated agriculture-specialized FaaS system implements specific systems at different levels. The proposed system was verified through design and analysis of a strawberry infection prediction system, which was compared with other infection models. Full article

(This article belongs to the Special Issue Internet-of-Things for Precision Agriculture (IoAT))

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Review

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24 pages, 2581 KiB

Open AccessReview

State-of-the-Art Internet of Things in Protected Agriculture

by Xiaojie Shi, Xingshuang An, Qingxue Zhao, Huimin Liu, Lianming Xia, Xia Sun and Yemin Guo

Sensors 2019, 19(8), 1833; https://doi.org/10.3390/s19081833 - 17 Apr 2019

Cited by 252 | Viewed by 18391

Abstract

The Internet of Things (IoT) has tremendous success in health care, smart city, industrial production and so on. Protected agriculture is one of the fields which has broad application prospects of IoT. Protected agriculture is a mode of highly efficient development of modern agriculture that uses artificial techniques to change climatic factors such as temperature, to create environmental conditions suitable for the growth of animals and plants. This review aims to gain insight into the state-of-the-art of IoT applications in protected agriculture and to identify the system structure and key technologies. Therefore, we completed a systematic literature review of IoT research and deployments in protected agriculture over the past 10 years and evaluated the contributions made by different academicians and organizations. Selected references were clustered into three application domains corresponding to plant management, animal farming and food/agricultural product supply traceability. Furthermore, we discussed the challenges along with future research prospects, to help new researchers of this domain understand the current research progress of IoT in protected agriculture and to propose more novel and innovative ideas in the future. Full article

(This article belongs to the Special Issue Internet-of-Things for Precision Agriculture (IoAT))

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