Enhancing Chinese Cabbage Production and Quality through IoT-Based Smart Farming in NFT-Hydroponics

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The research employing an IoT-based hydroponic system, with those of a conventional hydroponic farm, using Chinese cabbage as the experimental crop, evaluated plant growth and crop quality, finding superior results with the smart hydroponic system, which will lead to a significant step towards revolutionizing traditional agricultural practices for more efficient crop production systems. This research work has important research significance and application value.

Overall, the research work was full, the research methods were feasible, and the research conclusions were credible. However, some minor issues still need to be improved:

1. Chinese cabbage production and quality is influenced by many factors, not just the role of IoT, nutrition is one aspect, and the influence of light conditions is one of the key factors. How does this research ensure better growth light conditions?

2. The research mentioned that the IoT system consists of 7 sensors, including an Electrical Conductivity sensor, a pH sensor, 2 air temperature sensors, a water temperature sensor, an air humidity sensor, and a light intensity sensor. For intelligent systems, the number and position of the sensor layout is very critical. How is the sensor layout in the designed system?

3. The research results only list the curve figures of the test results, without modeling analysis of the test results, and lack of in-depth theoretical research, I suggested to add theoretical research results.

4. Real-time adjustment of nutrient solution composition and state according to growth characteristics should be the advantage of the system. If possible, I suggested to add the relevant research content.

Comments on the Quality of English Language

No

Author Response

Thank you for your valuable feedback and suggestions for improving our manuscript. We appreciate your time and effort in reviewing our research. We have carefully considered your comments and are committed to addressing them to enhance the clarity, quality, and scientific rigor of our work.

Reviewer 1: Comments and Suggestions for Authors

The research employing an IoT-based hydroponic system, with those of a conventional hydroponic farm, using Chinese cabbage as the experimental crop, evaluated plant growth and crop quality, finding superior results with the smart hydroponic system, which will lead to a significant step towards revolutionizing traditional agricultural practices for more efficient crop production systems. This research work has important research significance and application value.

Overall, the research work was full, the research methods were feasible, and the research conclusions were credible. However, some minor issues still need to be improved:

Comments 1: Chinese cabbage production and quality is influenced by many factors, not just the role of IoT, nutrition is one aspect, and the influence of light conditions is one of the key factors. How does this research ensure better growth light conditions?

Response 1: Thank you for pointing this out. We agree with this comment. Nevertheless, the growth and quality of Chinese cabbage in conventional hydroponics and IoT hydroponics were less affected by the concentrated light. It was found that there was no distinction within the light concentration values for the two groups. Both systems (treatments) were conducted inside the same greenhouse. In experimental planting, Chinese cabbage in traditional hydroponic frameworks must feed the nutrient solution and acid-base to optimize the EC and pH once a week. Whereas developing with an IoT hydroponic framework will degree EC and pH values every 5 seconds all through the development, when the framework identifies that the measured values are not fitting as modified, it will quickly add the arrangement fittingly. Therefore, the IoT hydroponic developing framework gives plants adequate supplements and pH all through the developing period to develop more completely than conventional hydroponics.

Comments 2: The research mentioned that the IoT system consists of 7 sensors, including an Electrical Conductivity sensor, a pH sensor, 2 air temperature sensors, a water temperature sensor, an air humidity sensor, and a light intensity sensor. For intelligent systems, the number and position of the sensor layout is very critical. How is the sensor layout in the designed system?

Response 2: Agree. We have revised to emphasize this point. The number and position of the sensor layout are shown in a diagram of a prototype of an intelligent farm system for NFT-hydroponic plant cultivation, controlled using IoT technology (Figure 1). Sensor placement plays a key role in accurate greenhouse data. Air temperature and humidity sensors should be high and away from sunlight and moisture, while the water temperature sensor goes directly in the nutrient tank. Similarly, pH and EC sensors are submerged into the tank, and the light intensity sensor needs strategic positioning near the plant canopy to reflect the average light the plants receive. The hardware design of a hydroponics system monitoring and control system is shown in Figure 2. Measured data from a total of 7 sensors. Signals from the EC sensor, pH sensor, light sensor, and water temperature sensor are sent to the microcontroller unit (A, B, C, D, G), which acts as a receiver before the signal is sent to the receiving board (E, H). The information from the light sensor, air temperature sensors 1 and 2, and humidity sensor is sent directly to the receiver (H). Then there is a receiver (H), which acts as a gateway in the IoT network. The gateway connects to a server within the system. This makes it possible to convert analog data signals to be available on web interfaces and smartphones, with a real-time clock (RTC) used to track time. When the command is given to release the solution into the system, commands are sent to the main board (H) and send signals to the control board (F), which is connected to four relay modules, instructing the motor to work to feed the solution into the system.

Comments 3: The research results only list the curve figures of the test results, without modeling analysis of the test results, and lack of in-depth theoretical research, I suggested to add theoretical research results.

Response 3: We have added the relationship between parameters and plant growth in Chinese cabbage cultivation (Table 4.) and the growth comparison and quality assessment of Chinese cabbage plants grown in NFT-hydroponics with and without IoT systems, highlighting the significant differences observed between the two cultivation methods (Table 5) in the discussion section.

Comments 4: Real-time adjustment of nutrient solution composition and state according to growth characteristics should be the advantage of the system. If possible, I suggested to add the relevant research content.

Response 4: The ability to adjust nutrient solution composition and state based on real-time plant growth characteristics is a key advantage of IoT systems in hydroponics. This study demonstrates that the IoT system continuously monitors various environmental parameters, like pH and EC that could be used to inform real-time adjustments to the nutrient solution. The discussion section has been improved according to the comments of the reviewer.

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This paper introduces an IoT-based monitoring and control system for hydroponic systems. The text is well written and easy to follow and the conclusions fit the presented data. 
However, the system design lacks details on the technical realization (e.g. which sensors are used and how the data exchange is handled between the components). Also how ThingsBoard and AWS fit together is not explained. The authors mention ML techniques to analyze the data but give no details on algorithms or deployment. Please clarify this.  In addition, a clear statement whether existing approaches where merely combined to build the system or if the authors did build some parts themselves. 

Author Response

Thank you for your valuable feedback and suggestions for improving our manuscript. We appreciate your time and effort in reviewing our research. We have carefully considered your comments and are committed to addressing them to enhance the clarity, quality, and scientific rigor of our work.

Reviewer 2: Comments and Suggestions for Authors

This paper introduces an IoT-based monitoring and control system for hydroponic systems. The text is well written and easy to follow and the conclusions fit the presented data. 
However, the system design lacks details on the technical realization (e.g. which sensors are used and how the data exchange is handled between the components). Also how ThingsBoard and AWS fit together is not explained. The authors mention ML techniques to analyze the data but give no details on algorithms or deployment. Please clarify this.  In addition, a clear statement whether existing approaches where merely combined to build the system or if the authors did build some parts themselves. 

Response: We have included a detailed sensor list with specifications and their role in data collection. We have clarified how data travels between sensors, the control unit, and cloud platforms (ThingsBoard or AWS) by explaining the communication protocols. Additionally, to better illustrate the system architecture, we have provided a diagram (Figure 1) explaining platform interaction and data flow, referencing the prototype design in Figure 2 that showcases the hardware for the hydroponics monitoring and control system.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript entitled „Enhancing Chinese cabbage production and quality through IoT-based smart farming in NFT-Hydroponics” presents comparison of two cabbage production systems, i.e. IoT technology for cultivation management with the conventional hydroponic cultivation method.

The study is interesting and quite well prepared, however contains some drawbacks:

Section “Data analysis” is very short and should be better described. I have doubts how the sample data were collected and if the assumption of the independence observation is fulfilled. Could you explain what were the replications and how the independence of them was achieved?

In the results are presented various comparisons between the two systems (IoT vs. conventional). Please add more information how these comparisons were performed, using data for all the growing cycle or only for the final day? How many replications were used for these comparisons? The t-test for independent samples or for paired samples was used?

What is presented in Fig. 8-16 as an error bars? It is standard deviation or other statistical parameter of variability? It should be explained.

In the Fig. 8-16 it is not necessary to present decimal places for the values on vertical axis.

Conclusions are quite long. I suggest to shorten the Conclusions  and to be more specific, ie. provide information which parameters of the growth production are adjusted in the larger degree and which of them have the highest influence on the yield of cabbage.

Author Response

Thank you for your valuable feedback and suggestions for improving our manuscript. We appreciate your time and effort in reviewing our research. We have carefully considered your comments and are committed to addressing them to enhance the clarity, quality, and scientific rigor of our work.

Reviewer 3: Comments and Suggestions for Authors

The manuscript entitled „Enhancing Chinese cabbage production and quality through IoT-based smart farming in NFT-Hydroponics” presents comparison of two cabbage production systems, i.e. IoT technology for cultivation management with the conventional hydroponic cultivation method.

The study is interesting and quite well prepared, however contains some drawbacks:

Section “Data analysis” is very short and should be better described. I have doubts how the sample data were collected and if the assumption of the independence observation is fulfilled. Could you explain what were the replications and how the independence of them was achieved?

In the results are presented various comparisons between the two systems (IoT vs. conventional). Please add more information how these comparisons were performed, using data for all the growing cycle or only for the final day? How many replications were used for these comparisons? The t-test for independent samples or for paired samples was used?

What is presented in Fig. 8-16 as an error bars? It is standard deviation or other statistical parameter of variability? It should be explained.

In the Fig. 8-16 it is not necessary to present decimal places for the values on vertical axis.

Conclusions are quite long. I suggest to shorten the Conclusions  and to be more specific, ie. provide information which parameters of the growth production are adjusted in the larger degree and which of them have the highest influence on the yield of cabbage.

Response:

The type of t-test employed in this experiment is an independent two-sample T-test (T-group). We add the information in Section 2.4: Experimental Design, Greenhouse, and Planting Tables.

We have described the sampling strategy, replication scheme, and measures taken to ensure the independence of observations.

We have clearly defined the error bars in Figs. 8–16, specifying whether they represent the standard deviation. We have removed unnecessary decimal places from the vertical axis labels in Figs. 8–16 to improve readability.

We appreciate the suggestion to condense and focus on the conclusions. We have revised this section by briefly highlighting the most significant parameters influenced by the IoT system and their impact on cabbage yield and emphasizing the factors that had the greatest effect on crop production, supported by relevant data from the results section.

We have added the information about the data recording and analyses.  

“Plant growth data was collected from 15 to 42 days old, including daily measurements of height, stem diameter, leaf number, width, and length, along with chlorophyll content. Additionally, at the 42-day mark, a more comprehensive analysis was conducted, measuring leaf area, root length, fresh and dry weights for both shoots and roots, and total weight, as well as nitrate accumulation.”

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript was improved according all my comments. The manuscript still demands small formatting improvements, e.g. the title should be written using capital letters. Please follow the guidelines for authors.