Reference : A vision-based autonomous inter-row weeder
Scientific congresses and symposiums : Unpublished conference/Abstract
Engineering, computing & technology : Computer science
A vision-based autonomous inter-row weeder
Krishna Moorthy Parvathi, Sruthi Moorthy mailto [Université de Liège - ULiège > > Gembloux Agro-Bio Tech >]
Detry, Renaud mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Boigelot, Bernard mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Informatique >]
Mercatoris, Benoît mailto [Université de Liège - ULiège > Sciences et technologie de l'environnement > Agriculture de précision >]
ENVITAM PhD Student Day
Graduate School on Environmental Sciences, Technologies and Management
Université catholique de Louvain, Louvain-la-Neuve
[en] Computer vision ; Robotics ; Machine learning ; Crop row detection ; autonomous navigation ; weed destruction
[en] Autonomous robotic weed destruction plays a significant role in crop production as it automates one of the few unmechanized and drudging tasks of agriculture i.e. manual weed destruction. Robotic technology also contributes to long-term sustainability with both economic and environmental benefits, by minimising the current dependency on chemicals. The aim of this study 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 and using a minimum of a priori information of the field. For the robot to navigate autonomously, necessary and sufficient information can be supplied by a machine vision system. One important issue with the application of machine vision is to develop a system that recognises the crop rows accurately and robustly which is tolerant to problems such as crops at varying growth stages, poor illumination conditions, missing crops, high weed pressure, etc. Aiming at accurate and robust real-time guidance of autonomous robot through the field, the plethora of image processing algorithms like Ostu’s threshold method and hough transform will be explored for two main processes namely the image segmentation and crop row detection respectively. In order to overcome the issue of large variabilities encountered in agriculture such as varying weather conditions, intelligent stochastic data fusion and machine learning algorithms will be used to combine data from heterogeneous sensors. Besides crop row detection, other major challenges foreseen are: mapping the unknown geometry of the field, high-level planning of efficient and complete coverage of the field, controlling the low-level op- erations of the robot, and ensuring security. Specialised sensors such as GPS will be considered to generate the map of the field enabling Simultaneous Localisation And Mapping (SLAM) in real time on a mobile platform. The generated map will be exploited along with the sensorial in- formation from crop row detection to efficiently plan and execute the guidance of the robot au- tonomously in the field, thereby enabling weed elimination.

File(s) associated to this reference

Fulltext file(s):

Open access
PPTEnvitam_Sruthi.pdfPublisher postprint9.42 MBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.