Research on Control Strategy of Light and CO2 in Blueberry Greenhouse Based on Coordinated Optimization Model
Round 1
Reviewer 1 Report
The authors have clearly explained the algorithm through flowcharts and the references are accurate. The experimental work is well designed and all parameters influencing photosynthesis such as light, temperature and CO2 effect are individually isolated. However, one drawback of this work is that the energy cost objective function does not consider greenhouse ventilation or heating costs which can change considerably depending on supplemental lighting intensities. Furthermore, crop yield is explicitly not considered as a function parameter to optimize energy costs. The authors should address these two factors briefly. A reference to consider to assess pareto frontiers as a function of greenhouse energy and yield for lettuce and tomato is provided here (doi: 10.1039/D1EE03474J). Furthermore, it is recommended to attach the MATLAB code to aid improved understanding for readers.
Other comments:
1. Line 15: There is a typo here. Mode should be model.
2. Line 82: Define semi-closed greenhouse for broader understanding.
3. Line 90: For broader understanding, what type of glazing dud the greenhouse have? What was the target day and night temperature for growing the plants.
4. Line 92: Does the greenhouse have any evaporative cooling pads for ventilation? Does it have heating systems?
5. Line 96: Siesta phenomenon depends on the start of the photoperiod for plants. So please specify the approximate sun-rise/supplemental lighting start time somewhere in this work.
6. Line 205: What is ρPAR specified in equation 13?
7. Line 342: In general, light seems to go down and CO2 value goes up as temperature rises. Can this observation be justified biologically? How does increase in CO2 impact transpiration? In general, we want to reduce transpiration water loss? Has this been considered in the optimization exercise?
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Reviewer 2 Report
This paper proposed a coordinated control strategy for blueberry greenhouse light and CO2 based on a multi-objective optimization model, which I think is interesting. However, the current form suffers some weakness and limitations that need to be addressed before publication.
First, in the data collection stage. The authors did not state clear how the measurements are classified as invalid, and if they are removed what are the corresponding techniques to deal with this issue?
Second, even in a control environment, the photosynthetic rate is not only corelated to air temperature, photon flux density and carbon dioxide concentration, but also could vary with soil water pressure. The SVR regression gives R^2 as 0.99, I am really surprised that this model works so well without considering the water effects on photosynthesis. I hope the authors could explain that.
Third, since the authors stated that a multi-objective optimization algorithm, specifically NSGA-II, was employed to find the optimal result for the model. How do you pick the best result from the pareto set given by NSGA-II?
Author Response
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Reviewer 3 Report
The paper proposes an innovative approach for greenhouse control strategies.
The work is well structured overall and the results are interesting.
The only note concerns the discussion and conclusions, which do not effectively emphasise the usefulness of the work in relation to the literature. Through what measurable parameters can we state that the control strategy developed is better than, for example, those reported in [21-28]?
Other minor issues:
Line 82: what do you mean by "semi-closed" greenhouse?
Line 257: please use the word "significantly" only in a statistical sense, otherwise use a synonym.
line 276. Table 1 and not 16
Translated with www.DeepL.com/Translator (free version)
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Round 2
Reviewer 2 Report
The authors addressed all of my concerns. It looks much better!
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