PDF(Pubmed)
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.
摘要：
在Sajindra等人最近的一篇论文中。[1],土壤养分水平，特别是氮，磷，钾，使用深度学习模型对有机白菜栽培进行了预测。这个模型设计了总共四个隐藏层，不包括输入和输出层，每个隐藏层精心制作，包含十个节点。正切S形传递函数作为数据集的最佳激活函数的选择是基于相关系数等考虑因素，均方误差，以及预测结果的准确性。在整个研究中，目的是证明正切sigmoid传递函数，并为获得的结果提供数学依据。•本文介绍了开发用于预测土壤养分水平的深度神经网络的综合方法。•TangentSigmoid传递函数的使用在预测中是合理的。•方法可以适应任何类似的现实世界场景。
