Justifying the prediction of major soil nutrients levels (N, P, and K) in cabbage cultivation

Abstract

In a recent paper by Sajindra et al. [1], the soil nutrient levels, specifically nitrogen, phosphorus, and potassium, in organic cabbage cultivation were predicted using a deep learning model. This model was designed with a total of four hidden layers, excluding the input and output layers, with each hidden layer meticulously crafted to contain ten nodes. The selection of the tangent sigmoid transfer function as the optimal activation function for the dataset was based on considerations such as the coefficient of correlation, mean squared error, and the accuracy of the predicted results. Throughout this study, the objective is to justify the tangent sigmoid transfer function and provide mathematical justification for the obtained results. • This paper presents the comprehensive methodology for the development of deep neural network for predict the soil nutrient levels. • Tangent Sigmoid transfer function usage is justified in predictions. • Methodology can be adapted to any similar real-world scenarios.

Keywords

• Deep Neural Network (DNN)