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Special Issue "Sensors and Robotics for Digital Agriculture"

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

Deadline for manuscript submissions: 30 October 2021.

Special Issue Editors

Prof. Dr. Dionysis Bochtis
E-Mail Website
Guest Editor
Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH), 38333 Volos, Greece
Interests: operation management; supply chain automation; agri-robotics; ICT-agri
Dr. Aristotelis C. Tagarakis
E-Mail Website
Guest Editor
Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH), 38333 Volos, Greece
Interests: precision agriculture; remote sensing; sensor networks; IoT; digital farming; decision support systems; agricultural engineering; agricultural automations

Special Issue Information

Dear Colleagues,

In recent years, there has been a growing interest in sensors and robotic systems as part of the digitalization of agriculture, which has the potential to vastly increase agricultural systems’ efficiency and sustainability. Agricultural robots (including automation and amended intelligent IT systems) can accomplish various tasks which can lead to more efficient farm management and improved profitability. Sensors deployed on agricultural robots are an essential component for the robots’ autonomy and agronomical functions. These sensors include navigation sensors, context and situation awareness sensors, and sensors ensuring a safe execution of the operation as regards autonomy, as well as sensor technologies for yield mapping and measuring, soil sensing, nutrient and pesticide application, irrigation control, selective harvesting, etc. as regards agronomical functions, all in the framework of precision agriculture applications.

The purpose of this Special Issue is to publish research articles, as well as review articles, addressing recent advances in systems and processes in the field of sensors and robotics within the concept of precision agriculture. Original, high-quality contributions that have not yet been published and that are not currently under review by other journals or peer-reviewed conferences are sought.

Indicatively, research topics include:

  • Human–robot interaction;
  • Computer vision;
  • Robot sensing systems;
  • Artificial intelligence and machine learning;
  • Sensor fusion in agri-robotics;
  • Variable rate applications;
  • Farm management information systems;
  • Remote sensing;
  • ICT applications;
  • UAVs in agriculture;
  • Agri-robotics navigation and awareness;
  • SLAM—Simultaneous localization and mapping;
  • Resource-constrained navigation in agricultural environments;
  • Mapping and obstacle avoidance in agricultural environments.

Prof. Dr. Dionysis Bochtis
Dr. Aristotelis C. Tagarakis
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 2200 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.

Published Papers (3 papers)

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Research

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Article
Orchard Mapping with Deep Learning Semantic Segmentation
Sensors 2021, 21(11), 3813; https://doi.org/10.3390/s21113813 - 31 May 2021
Viewed by 459
Abstract
This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various [...] Read more.
This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach. Full article
(This article belongs to the Special Issue Sensors and Robotics for Digital Agriculture)
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Review

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Review
Machine Learning in Agriculture: A Comprehensive Updated Review
Sensors 2021, 21(11), 3758; https://doi.org/10.3390/s21113758 - 28 May 2021
Cited by 1 | Viewed by 572
Abstract
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle [...] Read more.
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic. Full article
(This article belongs to the Special Issue Sensors and Robotics for Digital Agriculture)
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Review
Soft Grippers for Automatic Crop Harvesting: A Review
Sensors 2021, 21(8), 2689; https://doi.org/10.3390/s21082689 - 11 Apr 2021
Cited by 1 | Viewed by 590
Abstract
Agriculture 4.0 is transforming farming livelihoods thanks to the development and adoption of technologies such as artificial intelligence, the Internet of Things and robotics, traditionally used in other productive sectors. Soft robotics and soft grippers in particular are promising approaches to lead to [...] Read more.
Agriculture 4.0 is transforming farming livelihoods thanks to the development and adoption of technologies such as artificial intelligence, the Internet of Things and robotics, traditionally used in other productive sectors. Soft robotics and soft grippers in particular are promising approaches to lead to new solutions in this field due to the need to meet hygiene and manipulation requirements in unstructured environments and in operation with delicate products. This review aims to provide an in-depth look at soft end-effectors for agricultural applications, with a special emphasis on robotic harvesting. To that end, the current state of automatic picking tasks for several crops is analysed, identifying which of them lack automatic solutions, and which methods are commonly used based on the botanical characteristics of the fruits. The latest advances in the design and implementation of soft grippers are also presented and discussed, studying the properties of their materials, their manufacturing processes, the gripping technologies and the proposed control methods. Finally, the challenges that have to be overcome to boost its definitive implementation in the real world are highlighted. Therefore, this review intends to serve as a guide for those researchers working in the field of soft robotics for Agriculture 4.0, and more specifically, in the design of soft grippers for fruit harvesting robots. Full article
(This article belongs to the Special Issue Sensors and Robotics for Digital Agriculture)
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