Sensing Technologies for Agricultural Automation and Robotics

in Special Issue   Posted on December 18, 2020 

Information for the Special Issue

Submission Deadline: Wed 30 Jun 2021
Journal Impact Factor : 3.275
Journal Name : Sensors
Journal Publisher:
Website for the Special Issue: https://www.mdpi.com/journal/sensors/special_issues/Sensors_Agricultural_Automation_Robotics
Journal & Submission Website: https://www.mdpi.com/journal/sensors

Special Issue Call for Papers:

Dear Colleagues,

The world is facing increasingly critical challenges in producing sufficient, quality food, feed, fiber, and fuel with depleting farming resources such as water, chemicals, and labor. To address these challenges, scientists and engineers around the world have, in recent years, been exploring opportunities in utilizing new innovations in artificial intelligence, Internet of things, Big Data analytics, and robotics in farming. Widespread research and development in this area are expected to lead the farming industry towards Ag 4.0 supported by smart, automated/autonomous machines and agricultural systems that will increase consistency and reliability of farming decisions and operations, reduce input (including labor), and optimize crop yield and quality. Novel sensing and machine vision systems, sensor and data fusion techniques, and efficient data analytics are crucial to support all aspects of automated/robotic and smart farming operations including, but not limited to; i) perceiving and understand the farming environment; ii) understanding stresses/status and needs of crops; iii) assessing crop growth and maturity, iv) localizing various objects of interest and obstacles in the field environment for automated/autonomous operations; v) guiding robotic machines through fields and for performing specific tasks; vi) collaborating with human and other robotic machines; and vii) providing operation, supervision, diagnostics, and maintenance capabilities to farmers/operators.

In this context, there has been rapid advancement in sensing technologies and data analytics techniques in recent years leading to more cost-effective, powerful, lighter, and reliable sensing systems for agricultural applications around the world, including small UAV-based sensing systems. Some of the areas of innovation and advancement include multi- and hyper-spectral imaging, thermal imaging, and color and 3D imaging (including RGB-D sensing). Novel techniques are being investigated to expand the sensing systems available for agricultural automation and robotics from vision to hearing, touch/feel, taste, and smell. Powerful computational infrastructure and associated data analytics techniques, including deep learning, have also played an instrumental role in improving the robustness and reliability and widening practical applications of sensing technologies in all aspects of production agriculture. The objective of this Special Issue is, therefore, to promote a deeper understanding of major conceptual and technical challenges and facilitate the spread of recent breakthroughs in sensing technologies for smart farming, in general, and agricultural automation and robotics, in particular. This Special Issue is expected to help realize safe, efficient, and economical agricultural production, and to advance the state-of-the-art in sensing, machine vision, sensor and data fusion, and data analytics techniques as applied to agricultural automation and robotics.

Topics of interest include (but are not limited to):

Novel sensing techniques for taste, smell (electronic noses), touch/feel, and hearing
Sensor fusion techniques
UASs-based sensing and crop monitoring
Crop scouting with ground-vehicle-based sensing
Sensing technologies for situation awareness in agricultural applications
Sensors and systems for crop phenotyping
Sensing and machine vision system for crop monitoring
Sensing and machine vision system for automation and robotics in agriculture
Sensing system for guidance in agricultural fields
Sensor applications in swarm robotics
Sensing for management and maintenance of agricultural robots
Sensing and machine vision for remote supervision and operation of machines
Sensing and machine vision for automating green-houses, plant factories, and vertical farms
Sensing and machine vision in animal production
Machine learning and arterial intelligence in sensing and data analytics
IoT, Big Data and data analytics for smart agriculture
Sensing and data analytics for post-harvest monitoring
Sensing and data analytics for crop quality assessment

Prof. Dr. Manoj Karkee
Guest Editor

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