Back

Dim Green Light Enables Day-and-Night Monitoring of Leaf Movements

Herrero, E.; Gill, A. R.; Wijeweera, S.; Ginzburg, D.; Stamford, J. D.; Antoniades, A.; Bromley, J. R.; Mortimer, J.; Gilliham, M.; Millar, H.; Webb, A. A.

2026-05-09 plant biology
10.64898/2026.05.08.723725 bioRxiv
Show abstract

Understanding plant growth dynamics requires imaging across day-and-night cycles to quantify growth, movement and development in the aerial plant body and to capture the rhythmic nature of these processes. This requires imaging in light during the day and in darkness at night without perturbing plant physiology. Nighttime imaging has typically depended on infrared (IR) illumination, producing monochrome datasets that require specialised hardware and separate analysis pipelines when combined with daytime RGB imaging. Here, we evaluated very low-intensity green (dimG) illumination from standard LEDs as a practical alternative for colour-consistent nighttime imaging and assessed its physiological impact in Arabidopsis thaliana and Lactuca sativa (lettuce). We show that high resolution colour images can be obtained under dimG using low- cost cameras, with sufficient consistency between full-spectrum and dimG images to allow direct comparison and unified image analysis. We show that very low-fluence green light (<0.5 mol m-2 s-1) does not sustain circadian oscillations of gene activity under continuous exposure and does not perturb rhythms when applied during the dark phase of diel cycles. DimG imaging enabled accurate detection of diel leaf movement profiles in Arabidopsis circadian mutants, revealing genotype-specific phase differences under varying photoperiods. In lettuce, dimG pulses and continuous dimG enabled accurate quantification of diel leaf movement without affecting growth, stomatal opening, electron transport rate or chlorophyll content. Motion profiles under continuous dimG mirrored those under darkness. Our findings establish dim green illumination as a cost-effective solution for night-time imaging, simplifying phenotyping workflows with minimal impact on physiology.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Plant Methods
39 papers in training set
Top 0.1%
14.0%
2
The Plant Journal
197 papers in training set
Top 0.2%
14.0%
3
Plant Physiology
217 papers in training set
Top 0.3%
12.0%
4
PLOS ONE
4510 papers in training set
Top 18%
10.2%
50% of probability mass above
5
New Phytologist
309 papers in training set
Top 1%
6.2%
6
Scientific Reports
3102 papers in training set
Top 25%
4.7%
7
Nature Communications
4913 papers in training set
Top 36%
4.2%
8
Journal of Experimental Botany
195 papers in training set
Top 1%
3.8%
9
Frontiers in Plant Science
240 papers in training set
Top 3%
3.2%
10
The Plant Phenome Journal
14 papers in training set
Top 0.1%
1.8%
11
Plant Direct
81 papers in training set
Top 1%
1.6%
12
HardwareX
16 papers in training set
Top 0.1%
1.6%
13
Plant Biotechnology Journal
56 papers in training set
Top 0.8%
1.4%
14
Plant Phenomics
17 papers in training set
Top 0.2%
1.2%
15
eLife
5422 papers in training set
Top 50%
1.2%
16
Neurophotonics
37 papers in training set
Top 0.5%
0.9%
17
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 44%
0.8%
18
Advanced Science
249 papers in training set
Top 20%
0.7%
19
Applications in Plant Sciences
21 papers in training set
Top 0.3%
0.7%
20
Scientific Data
174 papers in training set
Top 3%
0.7%
21
The Journal of Physical Chemistry Letters
58 papers in training set
Top 2%
0.7%
22
Development
440 papers in training set
Top 4%
0.7%
23
Open Research Europe
14 papers in training set
Top 0.2%
0.7%
24
Nature Biotechnology
147 papers in training set
Top 8%
0.7%
25
Journal of Experimental Biology
249 papers in training set
Top 2%
0.7%
26
Analytical Chemistry
205 papers in training set
Top 3%
0.6%