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Tracking plant growth using image sequence analysis

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Bibliographic Details
Title: Tracking plant growth using image sequence analysis
Authors: Yiftah Szoke, Guy Shani
Source: Agriculture Communications, Vol 3, Iss 4, Pp 100110- (2025)
Publisher Information: Elsevier, 2025.
Publication Year: 2025
Collection: LCC:Agriculture
Subject Terms: Phenotyping, Object detection, Clustering, Linear interpolation, Growth monitoring, Plant nodes, Agriculture
Description: Automated plant phenotyping can help to monitor the growth process of crops, eliminating the high costs associated with traditional manual approaches. Using low-cost devices (e.g., digital cameras), RGB images can be captured under field or greenhouse conditions to track various phenotypes. In this paper, we focused on a particular task – tracking plant growth by identifying and monitoring plant nodes in greenhouse-grown crops. We used a setup where a digital camera captured images at 1-h intervals, with object detection algorithms employed to facilitate rapid and cost-effective tracking of nodes. The main challenge addressed in this paper involved tracking nodes that were hidden temporarily caused by diurnal leaf movements – leaves obscure some nodes at different times throughout the day. Because a node may be hidden for a few hours but visible at other times during the day, one can predict its location while it is hidden. We proposed two approaches, clustering and linear interpolation, for estimating hidden node locations. We collected a set of greenhouse datasets for different crops and conducted empirical comparisons of our methods. Results showed that our approach predicted the node location with an average error of less than 4 ​cm.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2949-7981
Relation: http://www.sciencedirect.com/science/article/pii/S2949798125000407; https://doaj.org/toc/2949-7981
DOI: 10.1016/j.agrcom.2025.100110
Access URL: https://doaj.org/article/0f3d8f3faaea4e38a65715611f452a14
Accession Number: edsdoj.0f3d8f3faaea4e38a65715611f452a14
Database: Directory of Open Access Journals
Description
Abstract:Automated plant phenotyping can help to monitor the growth process of crops, eliminating the high costs associated with traditional manual approaches. Using low-cost devices (e.g., digital cameras), RGB images can be captured under field or greenhouse conditions to track various phenotypes. In this paper, we focused on a particular task – tracking plant growth by identifying and monitoring plant nodes in greenhouse-grown crops. We used a setup where a digital camera captured images at 1-h intervals, with object detection algorithms employed to facilitate rapid and cost-effective tracking of nodes. The main challenge addressed in this paper involved tracking nodes that were hidden temporarily caused by diurnal leaf movements – leaves obscure some nodes at different times throughout the day. Because a node may be hidden for a few hours but visible at other times during the day, one can predict its location while it is hidden. We proposed two approaches, clustering and linear interpolation, for estimating hidden node locations. We collected a set of greenhouse datasets for different crops and conducted empirical comparisons of our methods. Results showed that our approach predicted the node location with an average error of less than 4 ​cm.
ISSN:29497981
DOI:10.1016/j.agrcom.2025.100110