1D convolutional neural networks-based soil fertility classification and fertilizer prescription

Abstract

Sustainable agriculture is essential to meet the demands of the global population. An adequate application of fertilizers is essential for sustainable agricultural productivity. This research aims to determine soil fertility and provide precise fertilizer to improve crop yield. Many researchers have proposed soil fertility classification using deep learning-based approaches, such as extreme learning machines (ELMs) and multilayer perceptrons (MLPs). Although both ELM and MLP have the highest performance, insufficient training data prevent them from being useful. To address this limitation, this research proposes a 1D convolution neural networks (1D-CNN)-based soil fertility classification method that is straightforward, compact, and supports scalar additions and multiplications. To classify soil fertility, the classifier employs laboratory-measured soil data that encompasses electrical conductivity, pH, organic carbon, potassium, phosphorus, sulfur, boron, copper, iron, manganese, and zinc. The proposed approach employs MinMax normalization and the synthetic minority oversampling technique (SMOTE) to improve the classifier performance. The results of soil classification are used to recommend fertilizers. An experimental study using a laboratory-measured soil dataset showed that the proposed technique outperformed ELM and MLP classifiers. The proposed approach outperformed ELM and MLP with a classification accuracy of 97.9%, while ELM and MLP achieved classification accuracies of 69.80% and 87.06%, respectively. The proposed method can help farmers manage soil fertility sustainably to increase crop production.