International Journal of Advanced Networking and Applications

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Agricultural Mobile Robots in Weed Management and Control


Affiliations
1 Hobby Researcher in Robotics, Artificial Intelligence, IoT, United States
2 Cambridge Institute of Technology, Bangalore, India
3 Faculty, Facilitator, LeenaBOT Robotics, Nigeria
4 Student, LeenaBOT Robotics, Bangalore, India
 

The introduction of various robotics technology has made it easier to apply these approaches to agricultural procedures. However, due to the enormous differences in shape, size, rate and type of growth, kind of yield, and environmental needs for different types of crops, implementing this technology on farms has proven difficult. Agricultural processes are a series of time-dependent, methodical, repeated actions. Tilling, soil analysis, seeding, transplanting, crop scouting, insect management, weed removal, and harvesting are all major processes in open arable farming, and robots can help with all of them. By shrinking the range of the search grayscale range, the new method efficiently shortens the algorithm's search speed and reduces computation processing time. The edge contour picture of the corn and weed targets is used as the study object, and we built an algorithm to achieve an accurate selection of the 2D coordinate points of the corn and weed targets in the field crop image. A quadratic traversal algorithm is proposed in this paper for selecting target 2D coordinate points in the pixel coordinate system, as well as the related traversal search box. To achieve real-time target recognition and complete automatic cut classification of targets, the Faster R-CNN deep network model based on the VGG-16 feature extraction network is deployed. The use and implementation of our ideas in this study can help intelligent weeding robots perform more precise weeding operations and increase their efficiency.



Keywords

Agricultural Robotics, Deep Learning, LeenaBOT, Weeding Robot.
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  • Agricultural Mobile Robots in Weed Management and Control

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Authors

Prakash Kanade
Hobby Researcher in Robotics, Artificial Intelligence, IoT, United States
Monis Akhtar
Cambridge Institute of Technology, Bangalore, India
Fortune David
Faculty, Facilitator, LeenaBOT Robotics, Nigeria
Sunay Kanade
Student, LeenaBOT Robotics, Bangalore, India

Abstract


The introduction of various robotics technology has made it easier to apply these approaches to agricultural procedures. However, due to the enormous differences in shape, size, rate and type of growth, kind of yield, and environmental needs for different types of crops, implementing this technology on farms has proven difficult. Agricultural processes are a series of time-dependent, methodical, repeated actions. Tilling, soil analysis, seeding, transplanting, crop scouting, insect management, weed removal, and harvesting are all major processes in open arable farming, and robots can help with all of them. By shrinking the range of the search grayscale range, the new method efficiently shortens the algorithm's search speed and reduces computation processing time. The edge contour picture of the corn and weed targets is used as the study object, and we built an algorithm to achieve an accurate selection of the 2D coordinate points of the corn and weed targets in the field crop image. A quadratic traversal algorithm is proposed in this paper for selecting target 2D coordinate points in the pixel coordinate system, as well as the related traversal search box. To achieve real-time target recognition and complete automatic cut classification of targets, the Faster R-CNN deep network model based on the VGG-16 feature extraction network is deployed. The use and implementation of our ideas in this study can help intelligent weeding robots perform more precise weeding operations and increase their efficiency.



Keywords


Agricultural Robotics, Deep Learning, LeenaBOT, Weeding Robot.

References