OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis

Abstract

The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome).High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, commercial HTPP platforms remain unaffordable. Here we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.OPEN leaf, coupled with the SMART imaging processing package was able to consistently document and quantify dynamic morphological changes over time at the whole rosette level and also at leaf-specific resolution when plants experienced changes in nutrient availability.The modular design of OPEN leaf allows for additional sensor integration. Notably, our data demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify characterize previously unidentified phenotypes in a leaf-specific manner.

LeTra: a leaf tracking workflow based on convolutional neural networks and intersection over union

“…This could be addressed by adding an adaptative confidence threshold depending on the stage of the plant, to better balance the trade-off between precision and recall, or by increasing the training dataset size. Recent work has proposed the tracking of dynamic changes at leaf-specific resolution in A. thaliana [ 21 ] but the proper individualization of different leaves, without recurring to neural networks, has proven to remain a challenge. Through this method, the real challenge lies more on the tracking side than on the individualization of the different leaves.…”

The Early Dodder Gets the Host: Decoding the Coiling Patterns ofCuscuta campestriswith Automated Image Processing

“…The copyright holder for this preprint this version posted March 4, 2024. ; https://doi.org/10.1101/2024.02. 29.582789 doi: bioRxiv preprint synchronize its internal rhythm with the external environment. Notably, plant circadian rhythms are self-sustaining (free running) and have an intrinsic, endogenous component that allows it to maintain a 24-hour cycle even in the absence of external cues.…”

“…In a digital data-dominated era, open-sourced high-throughput plant phenotyping (HTPP) pipelines are crucial to gain better and more profound insights from imagebased data (Araus and Cairns 2014;Fahlgren, et al 2015). For example, 2D image-processing HTTP pipelines can automatically track size changes in Arabidopsis leaves (Swartz, et al 2023), detect plant-pathogen interactions in excavated maize roots (Pierz, et al 2023), characterize leaf venation patterns for grasses (Robil, et al 2021), and even trace circumnutation movements made by Arabidopsis stems (Mao, et al 2023). However, adapting existing HTPP pipelines for C. campestris phenotyping presents challenges due to several unique features of the organism.…”