## 4 Discussion
In our study, we used the predicted size of the plant's leaf growth as a benchmark. Most of the prediction models are able to predict that the average growth will be in the range of 8 to 10 mm. The training data set was calculated based on daily plant growth. Therefore, it includes temperature, pH, nitrogen, phosphorus, and potassium. We measured these according to the estimated growth rate. In this way, the same factors can occur but fall within different growth cycles, resulting in different growth values of the corresponding growing leaves. In the results of the prediction, we can observe more precisely that the difference in the prediction can be up to 2 mm. Previously, this discrepancy was not visible to the naked eye. Therefore, in the research process, we adjusted and conducted as many secondary experiments as possible. We can consider that in a different growth information node, when there is a gap between its variation in growth and the result of the formation, the training model will perform another layer of improvement. In the case of the study, we observed that there was still a difference. The MSE was reduced from 7.8 to 0.09 when the training results were compared with the actual data. This means that the \"deeper\" the layer is in the model, the more the model's prediction accuracy can improve. Therefore, we can use the same data set for the second repetition of the training. The results of the training are shown in Figure 8. The findings show that the blue waveform has more vibrations after training. In comparison to Figure 5, the loss of the model has a higher accuracy after training and testing the same model. The experimental results were tested on the prediction results from the initial data, and it is still possible to see that the relevant values of plant growth are not fully compatible with the intended results. After the second training, the accuracy of the model was still good. In the use of experimental training and testing of training results, it can be observed in the research that data from the data set can be validated by training on the data set itself. The experimental results shown in Figure 6 were highly efficient, which indicates  
Figure 8: The MSE is significantly improved after retraining results. Edge computing is used to predict or calculate plant growth characteristics. The main purpose is to solve the problem of agricultural experts assessing and observing plant growth trends based on past experience. After global warming, environmental and climatic factors are more complex than before. Indoor planting has been popular in recent years, but the growth effect of plants is still limited. The main reason is that the past experience judgment is based on the agricultural experience of field planting, but after changing environmental information, it exceeds the experts' past experience judgment results. Therefore, we have started to collect and monitor plant growth factors in our research and to predict trends that may affect plant growth according to different factors. In this study, the training pattern is assigned to different nodes for experiments by combining IoT and machine learning modeling with the concept of cloud fog fusion. In the future, this search template can be used to perform more verification on many different edge nodes; thus, improving the accuracy of analytical data and predictive information could be enhanced. Our research is limited to the corresponding analysis of the five proposed factors on plant leaf growth size, first verifying whether different factors can predict plant growth trends and the accuracy of growth prediction data. In the research, we took out five factors including temperature, soil pH, soil nitrogen, soil phosphorus and soil potassium and calculated them according to the growing length of the leaves. As there may be other factors such as air pollution, hours of sunlight, average air temperature and so on, these other variables may be used in future studies to add numeric information to the automatically collected and computed elements.