Record Details

Vision Based Automated Point-Cloud Processing Pipeline For High Throughput Phenotyping

OAR@ICRISAT

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Relation http://oar.icrisat.org/11518/
 
Title Vision Based Automated Point-Cloud Processing Pipeline For High Throughput Phenotyping
 
Creator Shreeshan, S
Bhattacherjee, S S
Gattu, P
Rajalakshmi, P
Kholova, J
Choudhary, S
 
Subject Crop Modelling
Crop Physiology
Food Security
 
Description High throughput phenotyping is rapidly gaining widespread
popularity due to its non-destructive approach for plant traits extraction. In this study, we focus on developing a vision based automated 3D point cloud processing pipeline for accurate estimation of plant traits, namely - plant height, leaf area index(LAI), and leaf inclination. Furthermore, the obtained estimates are validated by comparing the results with LeasyScan data in terms of coefficient of determination (R2), root mean squared error (RMSE), and correlation coefficient. These metrics are found to be around 0.90, 0.10, and 0.96, respectively, for each of the traits. Regression analysis has also been performed to gain some analytical insights on the data.
 
Date 2020-10
 
Type Conference or Workshop Item
PeerReviewed
 
Format application/pdf
 
Language en
 
Identifier http://oar.icrisat.org/11518/1/18.Point%20Cloud%20ICIP-IITH%20%282020%29.pdf
Shreeshan, S and Bhattacherjee, S S and Gattu, P and Rajalakshmi, P and Kholova, J and Choudhary, S (2020) Vision Based Automated Point-Cloud Processing Pipeline For High Throughput Phenotyping. In: IEEE International Conference on Image Processing, Oct 25–28, 2020, Abu Dhabi, United Arab Emirates. (Unpublished)