Reference : Robot weed killers - no pain more gain
Scientific congresses and symposiums : Poster
Engineering, computing & technology : Computer science
Robot weed killers - no pain more gain
Krishna Moorthy Parvathi, Sruthi Moorthy mailto [Université de Liège - ULiège > > Gembloux Agro-Bio Tech >]
Mercatoris, Benoît mailto [Université de Liège - ULiège > Sciences et technologie de l'environnement > Agriculture de précision >]
Boigelot, Bernard mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Informatique >]
19th National Symposium on Applied Biological Sciences
Thematic Doctoral School SCAIB
Gembloux Agro-Bio Tech (Liège University), Faculty of Bioscience Engineering.
[en] Computer vision ; Robots ; Weed destruction ; Crop row recognition
[en] Weed destruction plays a significant role in crop production, and its automation has both economic and environmental benefits by minimizing the usage of chemicals in the fields. Our aim is to design a small low-cost versatile robot allowing the destruction of weeds that lie between the crop rows by navigating in the field autonomously. Major challenges foreseen are: mapping the unknown geometry of the field, high-level planning of efficient and complete coverage of the field, and controlling the low-level operations of the robot. Traditionally, sensors like odometer have been used for localisation of robots but without much success in real-world scenarios. Specialized sensors like cameras will therefore be investigated and the plethora of image recognition algorithms will be explored and fine-tuned to enable Simultaneous Localisation And Mapping (SLAM) even on resource constrained robotic platforms. Vision-based localisation is not always viable because of the varying weather conditions of the environment and to overcome that, intelligent stochastic data fusion and machine learning algorithms will be utilized to combine data from heterogenous sensor. The image sensors for localisation will be re-used to differentiate crop rows from the weeds, which are cut when they grow. Finally, logics and reinforcement learning techniques will be explored, to exploit the generated map of the field and other sensorial information, to efficiently plan and execute weed elimination.

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