Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans

Kartal, Serkan and Choudhary, Sunita and Masner, Jan and Kholová, Jana and Stočes, Michal and Gattu, Priyanka and et al, . (2021) Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans. Sensors, 21 (23). pp. 1-20. ISSN 1424-8220

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Abstract

This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with PlantEye (R) laser scanners. Firstly, we segmented the crop canopies from the background in 3D space using the Region Growing Segmentation algorithm. Then, Convolutional Neural Network (CNN) based ML algorithms were fine-tuned for plant counting. Application of the CNN-based (Convolutional Neural Network) processing architecture was possible only after we reduced the dimensionality of the data to 2D. This allowed for the identification of individual plants and their counting with an accuracy of 93.18% and 92.87% for mung bean and chickpea plants, respectively. These steps were connected to the phenotyping pipeline, which can now replace manual counting operations that are inefficient, costly, and error-prone. The use of CNN in this study was innovatively solved with dimensionality reduction, addition of height information as color, and consequent application of a 2D CNN-based approach. We found there to be a wide gap in the use of ML on 3D information. This gap will have to be addressed, especially for more complex plant feature extractions, which we intend to implement through further research. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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IITH Creators:

IITH Creators	ORCiD

Item Type:

Article

Additional Information:

The results and knowledge included herein have been obtained owing to support from the following grants: Internal grant agency of the Faculty of Economics and Management, Czech University of Life Sciences Prague, grant no. 2019B0009–Life Sciences 4.0; Supporting the development of international mobility of research staff at CZU Prague, reg. no. CZ.02.2.69/0.0/0.0/16_027/0008366; Early Career Research Award from Department of Science and Technology, Government of India. Additionally, the Ministry of Electronics and Information Technology (MeitY) project led by Dr P Rajalakshmi brings active collaboration with the Indian Institute of Technology Hyderabad for student exchange.

Uncontrolled Keywords:

3D point clouds; Computer vision; Machine learning; Phenotyping; Plant detection

Subjects:

Electrical Engineering

Divisions:

Department of Electrical Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

05 Sep 2022 04:14

Last Modified:

05 Sep 2022 04:14