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Research Article | Open Access

Automatic Root Length Estimation from Images Acquired In Situ without Segmentation

Faina Khoroshevsky1,†( ), Kaining Zhou2,8,†, Sharon Chemweno3,8, Yael Edan1, Aharon Bar-Hillel1, Ofer Hadar4, Boris Rewald5,6, Pavel Baykalov5,7, Jhonathan E. Ephrath8, Naftali Lazarovitch8
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel
The Jacob Blaustein Center for Scientific Cooperation, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
The Albert Katz International School for Desert Studies, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
Department of Communication Systems Engineering, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
Institute of Forest Ecology, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic
Vienna Scientific Instruments GmbH, Alland, Austria
French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel

†These authors contributed equally to this work.

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Abstract

Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination (R2), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models’ performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.

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Plant Phenomics
Volume 6,
2024
Article number: 0132
DOI: 10.34133/plantphenomics.0132
Cite this article:
Khoroshevsky F, Zhou K, Chemweno S, et al. Automatic Root Length Estimation from Images Acquired In Situ without Segmentation. Plant Phenomics, 2024, 6: 0132. https://doi.org/10.34133/plantphenomics.0132

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Received: 12 July 2023
Accepted: 12 December 2023
Published: 12 January 2024
© 2024 Faina Khoroshevsky et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).
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