## 1 Introduction and Related Work
The authors proposed a combination of neural networks using a committee machine with twelve statistical RGB color features to evaluate input images. They applied a neural network (NN) to distinguish the wheat leaves, and then combined a committee machine and a genetic algorithm (GA) in the NN to estimate the nitrogen content. Like previous studies, the authors used a different method based on NN to extract basic colors in order to estimate nitrogen content. In addition, the authors also presented a comparison of several methods (e.g., SPAD, univariate linear regression, multivariate linear regression, ANN 30, ANN 35, and committee machines) with respect to nitrogen prediction performance on the basis of various comparative error values, such as MAPE, MAE, RMSE, MSE, and SSE [27]. Thereby, the committee machine achieved better results than other methods in terms of the evaluated indexes, with its values such as 3.15% for MAPE, 0.088 for MAE, RMSE of 0.125, 0.016 for MSE, and SSE of 0.565. As mentioned above, most studies have focused on distinguishing and predicting certain kinds of disease in plants or crops, such as the effects of the symptoms of nutrient deficiency on the growth of tomato plants/crops. Furthermore, previous approaches have not used particular deep neural network models in order to forecast and evaluate the performance of models under real conditions. Therefore, the primary goal of this paper is to apply a particularly deep neural network (i.e., Inception-ResNet v2 and Autoencoder), which recognizes and predicts deficiencies in the essential mineral nutrients Calcium, Potassium, and Nitrogen based on CNN, as well as to evaluate their performance effectiveness under real natural conditions. To collect a dataset for training and validation, we captured images of normal and nutrient-deficient tomato fruits. Accordingly, Inception-ResNet v2 and Autoencoder were utilized for training, recognition, and prediction of the nutrient status of tomato plants on the basis of the images captured in this study. We upgraded the CNN model to increase the accuracy of identification and prediction of the AI system. In contrast to previous studies, we focused on analyzing, evaluating, and predicting nutrient deficiency status in the growth stages of the tomato plant. We aim to improve the predictive performance of DL models in order to achieve high production yields and prevent the emergence of tomato pathologies caused by the lack of nutrients. In particular, we used the modified structure of Inception-ResNet v2 and Autoencoder for predicting nutrient deficiency, and achieved an accuracy rate of 87.27% for the Inception-ResNet v2 model and 79.09% for the Autoencoder. The top-3 error rate was 12.73% for Inception-ResNet v2 and 20.91% for Autoencoder. Moreover, we combined two of the previously described models by using the Ensemble Averaging method to improve the predictive precision, resulting in an accuracy of 91%. This paper is structured as follows. Section 2 presents the proposed system and dataset collection with Calcium, Potassium, and Nitrogen deficiencies. Section 3 shows the building of Inception-ResNet v2 and Autoencoder models based on CNN, and the use of Ensemble Averaging to predict and distinguish mineral nutrient deficiencies based on the captured images of tomato fruit and leaves. Section 4 demonstrates the results of forecasting. The comparison and evaluation of predictive performance for each model is described in Section 5. Section 6 summarizes the study and highlights the key development in this article.