## 4 Discussion  
Our experiments aim to learn and predict the growth data of two identical plants in different locations. The main reason for this is that the growth data are expected to be learned by planting outdoors. Once the model is established, it will be transferred via MQTT to another indoor planting location. Since the outdoor growth environment needs to be simulated indoors and the experimental parameters are set manually, in the future, more IoT devices can be combined to set the environmental conditions automatically and in the outdoor environment. Therefore, the outdoor growth model of this study is very important for the training of indoor plant growth trends. For this reason, in this study, we first modeled the results of outdoor training, and when the trained model was tested with the prediction results of plant growth in the same environment, the trained model and the prediction results agreed with the prediction results of plant growth in the same environment. However, if we change the test terminal and feed the model with indoor plant growth data to predict the next growth trend, we find that its accuracy in predicting growth decreases. \begin{table}
\begin{tabular}{l l l} \hline \hline Model: “sequential” & \\ \hline Layer (type) & Output Shape & Param \# \\ \hline lstm (LSTM) & (None, 32) & 4992 \\ dropout (Dropout) & (None, 32) & 0 \\ dense (Dense) & (None, 1) & 33 \\ \hline Total params: 5025 & \\ Trainable params: 5025 & \\ Non-trainable params: 0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Information was consolidated following the formation of the first layer. \begin{table}
\begin{tabular}{l l} \hline \hline Epoch 26/400 & \\ \hline
8/13 [===================>........,]-ETA: 0s-loss: 0.0036 \\ Epoch 26: val\_loss did not improve from 0.00801 \\
13/13 [===================>........]-0s 11 ms/step-loss: 0.0058-val\_loss: 0.0090 \\ Epoch 27: val\_loss did not improve from 0.00801 \\
13/13 [===================>........]-0s 11 ms/step-loss: 0.0058-val\_loss: 0.0092 \\ Epoch 28/400 & \\
8/13 [===================>........]-ETA: 0s-loss: 0.0034 \\ Epoch 28: val\_loss did not improve from 0.00801 \\
13/13 [===================>........]-0s 10 ms/step-loss: 0.0061-val\_loss: 0.0092 \\ Epoch 29/400 & \\
9/13 [===================>........]-ETA: 0s-loss: 0.0038 \\ Epoch 29: val\_loss did not improve from 0.00801 \\
13/13 [===================>........]-0s 9 ms/step-loss: 0.0045-val\_loss: 0.0090 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The model is trained to access only the most specific outcomes. \begin{table}
\begin{tabular}{l l l} \hline \hline Model: “sequential\_1” & \\ \hline Layer (type) & Output Shape & Param \# \\ \hline lstm (LSTM) & (None, 32) & 4992 \\ dropout (Dropout) & (None, 32) & 0 \\ dense (Dense) & (None, 1) & 33 \\ \hline \hline Total params: 5025 & \\ Trainable params: 5025 & \\ Non-trainable params: 0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Information following two training jobs. Retraining and learning on the same data set were performed in the experiments. The average difference was too large, even though the formation and prediction results of the same parent data were very accurate. While this may be due to the growth characteristics of the plant, the environment data may not have a direct correlation with the growth trend results. However, if the same data set is used, it is always possible to have an efficient prediction that the result is consistent with the original result. In the study, the predicted calculation results and actual data are shown in Figure 8, where the data set training result model was applied to the second non-homologous training test object. From comparing the linear difference, we can clearly see that there is a difference. Especially for the test data from 5 to 10, there is a significant difference in the linear results. In Figure 7, the growth data of 25 experimental plants were randomly selected; the blue line represents the deep learning prediction, while the orange line represents the experimental data.