An adaptive neuro-fuzzy inference system to monitor and manage the soil quality to improve sustainable farming in agriculture

Abstract

Neural network is one among the foremost powerful predictive analytics techniques. This paper considers a wide range of neural network topologies, followed by an examination of how to optimize and analyze neural networks using agriculture data. A back-propagation model was proposed here to create a predictive inference model, with five hidden layers and activation function as sigmoid. Four Kaggle datasets are used here to assess the ability of the proposed neural network approach regarding prediction, and three datasets are gathered from Kerala Agriculture University in real time. Finally, as an optimization strategy, adaptive momentum estimation was utilized, and the applied findings demonstrate that Adam optimizer achieves good prediction accuracy, and F1 score with the help of neuro-fuzzy system and therefore better convergence rate. The proposed method enables more precise control of the direction and step size for updating weight vectors, leading to significantly improved generalization performance. Experimental results reveal that the proposed method shows an accuracy of 97% in average. The suggested approach outperforms other benchmark algorithms such as C4.5, ID3, CART, Naïve Bayes, fuzzy, MLP and hence it can be concluded that the proposed hybridization of back-propagation neural networks with adaptive optimization method clearly verifies the efficiency of Adam optimizer with back propagation for text data.