Soil nutrients are a vital part of soil fertility and other environmental factors. Soil testing is an efficient tool used to evaluate the existing nutrient levels of soil and aid to compute the appropriate quantity of soil nutrients depending upon the fertility level and crop requirements. Since the conventional soil nutrient testing models are not feasible in real time applications, an efficient soil nutrient, and potential of hydrogen (pH) prediction models are essential to improve overall crop productivity. In this aspect, this paper aims to design an intelligent soil nutrient and pH classification using weighted voting ensemble deep learning (ISNpHC-WVE) technique. The proposed ISNpHC-WVE technique aims to classify the existence of nutrients and pH levels exist in the soil. In addition, three deep learning (DL) models namely gated recurrent unit (GRU), deep belief network (DBN), and bidirectional long short term memory (BiLSTM) were used for the predictive analysis. Moreover, a weighted voting ensemble model was employed which allows a weight vector on every DL model of the ensemble depending upon the attained accuracy on every class. Furthermore, the hyperparameter optimization of the three DL models was performed using manta ray foraging optimization (MRFO) algorithm. For investigating the enhanced predictive performance of the ISNpHC-WVE technique, a comprehensive simulation analysis takes place to examine the pH and soil nutrient classification performance. The experimental results showcased the better performance of the ISNpHC-WVE technique over the recent techniques with accuracy of 0.9281 and 0.9497 on soil nutrient and soil pH classification. The proposed model can be utilized as an effective tool to improve productivity in agriculture by proper soil nutrient and pH classification.

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xIn this study, the influence of the process parameters, traverse and rotational speeds, of dissimilar friction stir welded joints of AA2024-O and AA6061-O aluminum alloys on the corrosion resistance was evaluated. Potentiodynamic tests using a 3.5% NaCl solution obtained open circuit potential curves and polarization curves showing the corrosion behavior for the different welding parameters. These data were correlated with those obtained by mechanical tests (microhardness, tensile, and fracture analysis) and microstructure analysis by optical microscopy and scanning electron microscopy. It was observed that the combined effect of the parameters influenced the variation of corrosion resistance. This was mainly evidenced by the improvement in corrosion resistance at 1200rpm − 65mm · min−1 which was related to the recrystallization of the grain size and the heat input presented. Corrosive attacks on the welded joints showed greater affectations in the presence of base material 1 (AA6061-O) with greater metallic dissolution. The attacks mentioned above were presented in different forms, such as pitting, localized, and selective, and were observed by scanning electron microscopy. Finally, in corrosive and mechanical terms, the best performing condition was 1200rpm − 65mm · min−1 compared to the low parameter 840rpm − 45mm · min−1 .