Hyperspectral imaging for chloroplast movement detection
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Abstract
We employed hyperspectral imaging to detect chloroplast positioning and assess its influence on common vegetation indices. In low blue light, chloroplasts move to cell walls perpendicular to the direction of the incident light. In high blue light, chloroplasts exhibit the avoidance response, moving to cell walls parallel to the light direction. Irradiation with high light resulted in significant changes in leaf reflectance and the shape of the reflectance spectrum. Using mutants with disrupted chloroplast movements, we found that blue light-induced changes in the reflectance spectrum are mostly due to chloroplast relocations. We trained machine learning methods in the classification of leaves according to the chloroplast positioning, based on the reflectance spectra. The convolutional network showed low levels of misclassification of leaves irradiated with high light even when different species were used for training and testing, suggesting that reflectance spectra may be used to detect chloroplast avoidance in heterogeneous vegetation. We also examined the correlation between chloroplast positioning and values of indices of normalized-difference type for various combinations of wavelengths and identified an index sensitive to chloroplast positioning. We found that values of some of the vegetation indices, including those sensitive to the carotenoid levels, may be altered due to chloroplast rearrangements.
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It serves as a proof of concept for a high throughput, remote technique applicable to field conditions.
This seems great for surveying plant physiology for ecological field work. I didn't see anywhere an estimation of the throughput though, can any numbers be assigned to this to help a researcher see how the technique would increase teh efficiency of their work?
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especially NDVI (Fig. 5 A, Fig. S5 A), SR (Fig. 5 B, Fig. S5 B), RENDVI (Fig. 5 F, Fig. S5 F), mRENDEVI (Fig. 5 G, Fig. S5 G), mRESR (Fig. 5 H, Fig. S5 H0, VOG1 (Fig. 5 I, Fig. S5 I), PRI (Fig. 5 L, Fig. S5 L), SIPI (Fig. 5 M, Fig. S5 M), RGRI (Fig. 5 N, Fig. S5 N), PSRI (Fig. 5 O, Fig. S5 O) CAR1 (Fig. 5 P, Fig. S5 P), and CAR2 (Fig. 5 Q, Fig. S5 Q).
Can you spell out these acronyms here so the reader can refer to this section when interpreting the y-axes on Fig. 5 and 6? I see that they're listed in the abbreviations, but it would handy to have them written out in this section of the results.
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Welch t-test.
It would be worth reporting the sample sizes for each treatment, as that would help the reader understand why you chose different tests (like Welch's t-test). Also for D, I am assuming you actually performed an ANOVA and then a Tukey's post-hoc test, so you should report the ANOVA statistics (F value, df, p value).
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Fig. 4
I see now that the label for Darkened should really be "Dark-adapted", is that true?
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Fig. 2.
Same comment as for Fig. 1 but also it would help to make the label "Dark" more parallel. Is that a "No light" treatment as control? Consider new labels to make it clearer as to whether Dark means a condition or an observation as in Fig 1, "Darkened" seems like an observation.
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ig. 1.
small note - but consider labeling the panels "low" and "high" blue light and keep the info on the values for each treatment in the caption. This would improve readability.
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may be observed with the naked eye
Interesting, what is observed by eye? Is it a darkening of a green hue?
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