During the process of plant growth, such as during the flowering stages and fruit development, the plants need to be provided with the various minerals and nutrients to grow. Nutrient deficiency is the cause of serious diseases in plant growth, affecting crop yield. In this article, we employed artificial neural network models to recognize, classify, and predict the nutritional deficiencies occurring in tomato plants (Solanum lycopersicum L.). To classify and predict the different macronutrient deficiencies in the cropping process, this paper handles the captured images of the macronutrient deficiency. This deficiency during the fruiting and leafing phases of tomato plant are based on a deep convolutional neural network (CNN). A total of 571 images were captured with tomato leaves and fruits containing the crop species at the growth stage. Among all images, 80% (461 captured images) were used for the training dataset and 20% (110 captured images) were applied for the validation dataset. In this study, we provide an analysis of two different model architectures based on convolutional neural network for classifying and predicting the nutrient deficiency symptoms. For instance, Inception-ResNet v2 and Autoencoder with the captured images of tomato plant growth under the greenhouse conditions. Moreover, a major type of statistical structure, namely Ensemble Averaging, was applied with two aforementioned predictive models to increase the accuracy of predictive validation. Three mineral nutrients, i.e., Calcium/Ca²⁺, Potassium/K⁺, and Nitrogen/N, are considered for use in evaluating the nutrient status in the development of tomato plant with these models. The aim of this study is to predict the nutrient deficiency accurately in order to increase crop production and prevent the emergence of tomato pathology caused by lack of nutrients. The predictive performance of the three models in this paper are validated, with the accuracy rates of 87.273% and 79.091% for Inception-ResNet v2 and Autoencoder, respectively, and with 91% validity using Ensemble Averaging.

During the welding process, input factors (welding current, welding cycle, instantaneous heat rate, welding force, electrode tip diameter, etc.) all affect the quality of the welded button created by a resistance spot welding machine (RSW). Fixed input parameters such as welding force and welding cycle can be controlled and adjusted manually based on welding standards, but variable input parameters such as welding current and instantaneous heating rate (IHR). IHR and electrode tip size cannot be controlled manually. This study shows the relationship between welding current, heating rate and electrode tip size affecting welding quality. Classifying weld quality and adjusting appropriate welding current using an artificial intelligence model is proposed in this study. An artificial intelligence (LSTM – Long-short Term Memory) model is designed with several input parameters of the RSW machine. For RSW machine using an AC inverter, the quality of the weld depends on various influencing factors such as welding cycle, compression force, electrode cross-sectional area, welding current, etc. This paper is based on the calculation of IHR values by a data collection software program. Based on the IHR values, the weld quality management system can classify the quality of the weld at the current moment. To predict the weld quality, the correlation between IHR values, electrode cross-sectional area, and welding current is analyzed and evaluated. The performance evaluation indicates that the LSTM model in predicting the trend of IHR value with training data, namely MSE, MAE, and Coefficient of determination (R2), are 0.5, 0.25, and 0.78, respectively. For validation, the evaluation of LSMT's performance is 0.8 for MSE, 0.64 for MAE, and 0.8 for R2, respectively.