Vision system for coconut farm cable robot

Titus, Anu B., Narayanan, Thejas and Das, Gautham (2017) Vision system for coconut farm cable robot. In: IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Chennai, India.

Full content URL: https://doi.org/10.1109/ICSTM.2017.8089201

Documents
Vision system for coconut farm cable robot
Published manuscript
[img] PDF
2017_ICSTM_Titus_et_al.pdf - Whole Document
Restricted to Repository staff only

1MB
Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive

Abstract

In many countries, robots and automation techniques are being introduced in agriculture farms to reduce the human labour and to improve the yield. However, such technological initiatives are still lacking in India, although it is the leading producer of many vegetables and fruits, for example, coconuts. Some of the activities carried out in a coconut farm that requires human labor are coconut dehusking, loading and unloading of coconuts. Automating these activities in a coconut farm would require a robotic system to pick and transport coconuts, for which the primary need would be to detect coconuts in those environments under natural lighting conditions. Towards this, the work in this paper tests for the applicability of three most used computer vision based object detection approaches namely, Local Binary Pattern (LBP) cascade, Histogram of Oriented Gradients (HOG) cascade and Haar - like cascade in coconut detection. This vision system would enable any field robot to automate the tasks in coconut farms without human assistance. A comparative analysis using confusion matrix is carried on these three approaches. It is observed that Haar-like features provided comparably better results among all the three features, in terms of hit rate and precision.

Keywords:LBP, HOG, Haar features, Cascade classifier, coconut detection, cable robot
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:40823
Deposited On:30 Sep 2020 10:48

Repository Staff Only: item control page