Intelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques

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