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Research Status, Progress, and Applications of Agricultural Robot and Agriculture 4.0 Technologies in Field Operation—Volume II

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

Special Issue Information

Dear Colleagues,

With the rapid increase in the world population, agriculture first moved on from manual operation to mechanization and is now moving toward the implementation of automated/robotic field operations to meet the growing demand for food. However, in contrast to industrial operations, agricultural field operations are complex, unstructured, ill defined, and subject to a high degree of variation in illumination, atmospheric, and landscape conditions in addition to the dynamic biological nature of both field and specialty crops. These challenges make it extremely difficult to implement automated/robotic solutions in agricultural field operations. However, with the technological advancements in GPS (global positioning systems), smart sensors, UAVs (unmanned aerial vehicles), GIS (geographic information systems), the IoT (Internet of Things), machine vision, artificial intelligence, blockchain, big data, cybernetics, nanotechnology, digital agriculture, precision agriculture, smart decision support systems, advanced control system, etc., the use of automated/robotic operations in agriculture is becoming a reality.

The aim of this issue is to collect outstanding articles focusing on (but not limited to) robot solutions for various field operations (e.g., planting, irrigation, path planning and navigation, fertilization, spraying, canopy management, pollination, thinning, pruning, weed removal, precision crop load management, harvesting, postharvest transportation, and storage) for both field and specialty crops, precision agriculture applications, advanced in-field sensing and decision support systems, machine vision, artificial intelligence, deep learning, machine learning, big data, IoT, cybernetics, nanotechnology, digital agriculture, UAVs (unmanned aerial vehicles), mechatronics, smart sensors, swarm robotics, and nanorobotics applications in agriculture. 

Dr. Chao Chen
Dr. Satoru Sakai
Dr. Yaqoob Majeed
Prof. Dr. Longsheng Fu
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

  • robotics for field and specialty crops
  • agricultural automation
  • machine vision
  • deep learning
  • machine learning
  • advanced in-field sensing and decision support systems
  • swarm robotics
  • nanorobotics
  • smart sensors
  • precision agriculture
  • digital agriculture
  • agriculture 4.0
  • instrumentation
  • big data
  • cybernetics
  • SLAM (simultaneous localization and mapping)
  • ICT applications
  • IoT in agriculture

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Agronomy - ISSN 2073-4395