Plantation monitoring and yield estimation using autonomous quadcopter for precision agriculture
Vishakh Duggal, Mohak Sukhwani, Kumar Bipin, G. Syamasundar Reddy, K. Madhava Krishna
Abstract
Recently, quadcopters with their advance sensors and imaging capabilities have become an imperative part of the precision agriculture. In this work, we have described a framework which performs plantation monitoring and yield estimation using the supervised learning approach, while autonomously navigating through an inter-row path of the plantation. The proposed navigation framework assists the quadcopter to follow a sequence of collision-free GPS way points and has been integrated with ROS (Robot Operating System). The trajectory planning and control module of the navigation framework employ convex programming techniques to generate minimum time trajectory between way-points and produces appropriate control inputs for the quadcopter. A new ‘pomegranate dataset’ comprising of plantation surveillance video and annotated frames capturing the varied stages of pomegranate growth along with the navigation framework are being delivered as a part of this work.
BibTeX
@inproceedings{icra2016_plantationmonito,
title = {Plantation monitoring and yield estimation using autonomous quadcopter for precision agriculture},
author = {Vishakh Duggal and Mohak Sukhwani and Kumar Bipin and G. Syamasundar Reddy and K. Madhava Krishna},
booktitle = {ICRA 2016},
year = {2016}
}