Tracking plant growth using image sequence analysis

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 one-hour 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. • Automated plant phenotyping reduces the cost of traditional manual monitoring. • Object detection handles challenges caused by leaf movement and occlusion. • Hidden node locations are predicted using clustering and linear interpolation. The proposed methods achieve an average prediction error of less than 4 centimeters.

Plant phenotyping relevance

植物ノードを画像から検出・追跡し、遮蔽時の位置を推定する手法の開発と比較検証が中心であり、再利用可能な植物表現型取得ワークフローに該当する。

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