AI-Assisted Vision for Agricultural Robots

Robotics has been increasingly relevant over the years. The ever-increasing demand for productivity, the reduction of tedious labor, and safety for the operator and the environment have brought robotics to the forefront of technological innovation. The same principle applies to agricultural robots,...

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Bibliographic Details
Published in:AgriEngineering 2022-08, Vol.4 (3), p.674-694
Main Authors: Fountas, Spyros, Malounas, Ioannis, Athanasakos, Loukas, Avgoustakis, Ioannis, Espejo-Garcia, Borja
Format: Article
Language:English
Subjects:
Agriculture
agtech
Algorithms
Artificial intelligence
Cameras
computer vision
Crop diseases
Disease detection
Environmental impact
Harvest
Harvesting
Literature reviews
Pesticides
Phenotyping
Robotics
Robots
Sensors
Software
Spraying
State-of-the-art reviews
Technological change
Weeds
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Summary:Robotics has been increasingly relevant over the years. The ever-increasing demand for productivity, the reduction of tedious labor, and safety for the operator and the environment have brought robotics to the forefront of technological innovation. The same principle applies to agricultural robots, where such solutions can aid in making farming easier for the farmers, safer, and with greater margins for profit, while at the same time offering higher quality products with minimal environmental impact. This paper focuses on reviewing the existing state of the art for vision-based perception in agricultural robots across a variety of field operations; specifically: weed detection, crop scouting, phenotyping, disease detection, vision-based navigation, harvesting, and spraying. The review revealed a large interest in the uptake of vision-based solutions in agricultural robotics, with RGB cameras being the most popular sensor of choice. It also outlined that AI can achieve promising results and that there is not a single algorithm that outperforms all others; instead, different artificial intelligence techniques offer their unique advantages to address specific agronomic problems.
ISSN:2624-7402
2624-7402
DOI:10.3390/agriengineering4030043