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

Sensors

Serkan Kartal¹  Priyanka Gattu²  Jan Masner³  Michal Stočes³  Ewaut Kissel⁴  Stefan Schwartz⁴  Sunita Choudhary⁵  Jana Kholová⁵ 

[1] Department of Computer Engineering, Faculty of Engineering, Cukurova University, Adana 01330, Turkey;Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Sangareddy 502285, Telangana, India;Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Prague, Czech Republic;Phenospex B. V., Jan Campertstraat 11, 6416 SG Heerlen, The Netherlands;System Analysis for Climate Smart Agriculture (SACSA), ISD, International Crops Research Institute for the Semi-Arid Tropics, Patancheru 5023204, Telangana, India;

关键词: 3D point clouds;    plant detection;    machine learning;    computer vision;    phenotyping;   

DOI  :  10.3390/s21238022

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown