Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans
Autor: | Sunita Choudhary, Jan Masner, Stefan Schwartz, Serkan Kartal, Jana Kholova, Priyanka Gattu, Michal Stočes, Ewaut Kissel |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Canopy
phenotyping Computer science Pipeline (computing) TP1-1185 Machine learning computer.software_genre Biochemistry Convolutional neural network Article computer vision Analytical Chemistry Machine Learning plant detection Electrical and Electronic Engineering Region growing segmentation Instrumentation business.industry Chemical technology Dimensionality reduction 3D point clouds Atomic and Molecular Physics and Optics Identification (information) machine learning Feature (computer vision) Artificial intelligence Neural Networks Computer business Algorithm computer Algorithms Curse of dimensionality |
Zdroj: | Sensors; Volume 21; Issue 23; Pages: 8022 Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 8022, p 8022 (2021) |
ISSN: | 1424-8220 |
DOI: | 10.3390/s21238022 |
Popis: | 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. |
Databáze: | OpenAIRE |
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