Recent Advances and Challenges in Agricultural Robotics, Unmanned Agricultural Machinery and Autonomous Farming Technologies

Dear colleagues,

With the development of artificial intelligence (AI), agricultural Internet of Things (IoT) and big data technology, agricultural robotics (ARs), and unmanned agricultural machinery (UAM) as well as autonomous farming (AF) technologies have developed rapidly, such that traditional agricultural machinery has been gradually replaced in many agricultural areas. Robotics will play an essential role in improving productivity, increasing crop quality, and even enabling individualized weed and crop treatment. All these significant advancements will serve as the driving force for providing food to a growing worldwide population, expected to reach 9 billion by 2050, which requires agricultural production to double in order to meet such food demands. The increasing demand in food production should be achieved regardless of challenges such as climate change, limited supply of new arable land, and difficulties in sourcing skilled farm labor. Robotics and automation are considered to be a power option in meeting these challenges over the coming decades by helping to improve farm productivity.

However, considering the complex and changeable farmland operation environment, the operation performance of ARs and UAM is seriously disturbed by the external environment, such as soil and crops, which leads to the degradation and high failure frequency of operating unit components/systems. In the past few decades, with the development of advanced control and state monitoring theory, many new methods have exhibited strong potential to further improve the performance of actuator-based ARs (driving, walking and unit operation quality, etc.) and enhance their robustness and reliability. Additionally, the development of robust and accurate perception systems in future autonomous agricultural systems has consistently helped to perceive the agricultural environment, such as multi- and hyperspectral imaging, high-resolution cameras, radar, LiDAR, centimeter-precision GPS, etc.

In an effort to disseminate the current advances and challenges in AR, UAM, and AF technologies and to stimulate a discussion on future research directions in the field, we are pleased to announce a Special Issue on “Recent Advances and Challenges in Agricultural Robotics, Unmanned Agricultural Machines, and Autonomous Farming Technologies”. Researchers are invited to submit papers on the foundational, algorithmic, and experimental aspects of design, modeling, control, and validation of actuator-based AR, UAM, and AF technologies. We strongly encourage contributions that focus on agriculture, covering different fields of robotics, robotic vision, autonomous vehicles, manipulation, control, path planning, human–robot interaction, machine learning, etc. We particularly encourage papers with a thorough experimental evaluation. Review papers in these areas are also welcome.

This Special Issue will bring together original, high-quality articles through an international standard peer-review process, including (but not limited to) the following main topics:

• Modeling, estimation, and control of actuator-based ARs/UAM;
• Vision and action for ARs/UAM dealing with changes in appearance and geometry of the environment;
• Sensors and vision systems and yield-estimation systems for agricultural ARs/UAM including passive and active methods;
• Manipulators and platforms for harvesting, mowing, pruning, seeding, spraying, soil preparation, and crop protection and management;
• Long-term autonomy and navigation in unstructured farming environments;
• Automation of vertical farming, protected cropping systems, and phenotyping;
• Theoretical and empirical data analytics and real-time decision making with robots-in-the-loop, such as machine learning, artificial intelligence, etc.;
• User interfaces and human–robot interaction for end users;
• Cooperative robots in agriculture;
• New standardized benchmarks and long-term datasets in changing agricultural environments.

We look forward to your valuable contributions.

Dr. Hai Wang
Prof. Dr. Liqing Chen
Prof. Dr. Jin He
Prof. Dr. Huanyu Jiang
Prof. Dr. Xiaoqiang Du
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 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. Actuators 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 2400 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.

• Modeling, control, and estimation
• Sensor and vision systems
• Manipulators and platforms
• Data analysis and real-time decision making
• Machine learning and artificial intelligence
• Cooperative robots