## 5 Comparison and Validation  
Based on the number of images used to validate forecasting ability in Table 1 and the comparison results in Table 3, it is apparent that the performance of Inception-ResNet v2 model with the modified structure using several sub-networks is higher than the Autoencoder model (without residual learning in its structure). Considering both of the model structures described in Sections 3.1 and 3.2, as well as the results in Table 3, we evaluate the effectiveness of forecasting under the same conditions. In general (see Table 3), the forecast results of Inception-ResNet v2 achieve greater accuracy than Autoencoder in our study. When looking at the comparison indexes in Table 3, it is easy to recognize that prediction and classification in the Calcium and Nitrogen groups using Inception-ResNet v2 is higher than when using Autoencoder. Particularly, in Figure 7a, and Figure 9a, Inception-ResNet v2 accurately predicts the expression of Calcium deficiency on the fruit of tomato, whereas Autoencoder does not match it correctly. In the case of Potassium prediction, the accuracy rates of the two models are almost equal. However, for forecasting the expression of Nitrogen deficiency, Autoencoder has an uncorrected prediction, as presented in Figure 9g. Meanwhile, Inception-ResNet v2 achieved an exact ratio of 0.747, which is not so much higher than the 0.650 achieved by Autoencoder, as shown in Figures 7h and 9h. Table 4 shows that validation rate of 87.27% for the Inception-ResNet v2 model, which is higher than the Autoencoder model, whereas the validation rate of Autoencdoer is only 79.09% on the basis of the same number of parameters, i.e., training and validation dataset, number of epochs, learning rate, and so on. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Average Value** & **Calcium** & **Potassium** & **Nitrogen** & **Figures** \\ \hline Inception-ResNet v2 & 7.30 & 1.12 & \(-\)8.20 & Figures 7a and 9a \\ Autoencoder & 3.53 & \(-\)2.81 & 0.81 & \\ Total\_1 & 10.83 & \(-\)1.69 & \(-\)7.39 & \\ Prediction rate & Calcium 1.0 & & & \\ Inception-ResNet v2 & \(-\)0.23 & \(-\)2.72 & 3.08 & Figures 7g and 9g \\ Autoencoder & 0.62 & \(-\)3.97 & 5.25 & \\ Total\_2 & 0.39 & \(-\)6.69 & 8.33 & \\ Prediction rate & & & Nitrogen 1.0 & \\ Inception-ResNet v2 & \(-\)1.00 & \(-\)0.47 & 1.07 & Figures 7h and 9h \\ Autoencoder & \(-\)0.12 & \(-\)1.10 & 2.79 & \\ Total\_3 & \(-\)1.12 & \(-\)1.57 & 3.86 & \\ Prediction rate & & & Nitrogen 0.989 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average value of each prediction model using the ensemble learning technique. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nutrient** & \
\begin{tabular}{c} **Inception-ResNet** \\ **v2 Top-3 Accuracy** \\ \end{tabular} & **Figures** &
\begin{tabular}{c} **Autoencoder** \\ **Top-3 Accuracy** \\ \end{tabular} & **Figures** \\ \hline Calcium (Ca\({}^{2+}\)) & 0.998 & Figure 7a & 0.000 & Figure 9a \\  & 0.977 & Figure 7b & 1.000 & Figure 9b \\  & 1.000 & Figure 7c & 1.000 & Figure 9c \\ Potassium (K\({}^{+}\)) & 0.989 & Figure 7d & 1.000 & Figure 9d \\  & 0.999 & Figure 7e & 1.000 & Figure 9e \\  & 1.000 & Figure 7f & 1.000 & Figure 9f \\ Nitrogen (N) & 0.962 & Figure 7g & 0.000 & Figure 9g \\  & 0.747 & Figure 7h & 0.650 & Figure 9h \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the accuracy rate of the two prediction models for forecasting nutrient deficiencies in the validation dataset. Figure 10a,b shows the relationship between the cost function and the training steps for the two predictive models using only the training dataset. As can be seen from these figures, it is obvious that the Inception-ResNet v2 model creates an algorithm with a higher accuracy than Autoencoder, due to the cost function of the former approaching zero more quickly.