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Sensing and Perception Systems for Situational Awareness of Agricultural Robotic Vehicles

This special issue belongs to the section “Precision and Digital Agriculture“.

Special Issue Information

Dear Colleagues,

The last few years have seen an increasing technological transfer from robotics to agriculture to develop intelligent vehicles that can significantly enhance the sustainability of agricultural systems. Robotics technology has been recently proven to be especially valuable for the development of in-field high-throughput phenotyping platforms supporting precision farming applications, to improve natural resource-saving and health and safety conditions of the workforce, as well as to increase productivity and competitiveness of agricultural production. In this respect, accurate and robust sensing and perception systems, taking advantage of the latest research advances in the field of machine learning and deep learning techniques, play a critical role in addressing unsolved issues such as safe interaction with workers and animals, controlled traffic applications, crop row guidance, surveying for variable rate applications, and situation awareness in general.

This Special Issue invites original submissions addressing the study and development of sensing and perception systems to endow an agricultural vehicle with cognitive abilities to safely navigate and interact with its operational environment, with workers or other robots, as well as articles dealing with the study and development of novel data analysis techniques for crop assessment, recognition of condition using data acquired by agricultural robotic platforms, and any other relevant application of such technologies in crops and animal husbandry. Papers providing experimental evidence in the field and involving the integration of different disciplines such as engineering, mathematics/statistics, occupational safety and health, human–machine interface and ergonomics, and environmental and computer science are particularly encouraged. Contributions focusing on the development of agricultural robotic technologies to achieve the United Nations sustainable development goals, such as securing healthy and sustainable food and safe working conditions, and overcoming and recovering from the COVID-19 pandemic are welcome.

Dr. Eugenio Cavallo
Dr. Roberto Marani
Dr. Annalisa Milella
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 250 words) can be sent to the Editorial Office for assessment.

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. Agronomy is an international peer-reviewed open access monthly 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

  • Sensor and robot networks in agriculture
  • Multisensor and data fusion
  • Machine learning and deep learning from sensor data in agriculture
  • Environment mapping and classification
  • Robot localization and navigation
  • In-field phenotyping sensors and vehicles
  • Safe human–robot interaction in agricultural applications
  • Sensors and vehicles for in-field pest detection and management
  • Harvesting automation
  • Cooperative agricultural robotics
  • Safety and health in agriculture
  • Food security

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Agronomy - ISSN 2073-4395