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All Journal Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)
Witaningsih Witaningsih
Universitas Lampung
Agriculture, Biological Sciences & Forestry Engineering

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Artificial Neural Network Backpropagation Method for Predicting Soil Nutrient Content: Artificial Neural Network Backpropagation Method for Predicting Soil Nutrient Content Witaningsih Witaningsih; Sri Ratna Sulistiyanti; Mareli Telaumbanua; F X Arinto Setyawan; Helmy Fitriawan; Rita Anggraini
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 6 (2025): December 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v14i6.2424-2438

Abstract

Monitoring soil nutrient levels such as nitrogen (N), phosphorus (P), and potassium (K) is essential to support fertilizer efficiency and sustainable agricultural land management. However, commonly used laboratory-based analytical methods are time-consuming and costly. Therefore, alternative approaches that are more practical and efficient are needed. This study aimed to develop an Artificial Neural Network (ANN)-based system for predicting soil nutrient levels using soil physical parameters, namely pH, temperature, moisture content, and electrical resistance, as input variables. Data were collected from red-yellow podzolic soil subjected to different fertilization treatments. After normalization, the data were trained using an ANN model with four input nodes, two hidden layers (each consisting of five nodes), and one output node, employing the backpropagation algorithm and evaluating 27 combinations of activation functions. The training results showed coefficients of determination (R²) of 0.9642 for nitrogen, 1.0000 for phosphorus, and 0.9996 for potassium, with RMSE values of 0.0107, 10.5386, and 0.016457 and RRMSE values of 8.5048%, 0.79786%, and 1.581111%, respectively. During validation, R² values of 0.7218 (nitrogen), 0.6479 (phosphorus), and 0.6137 (potassium) were obtained. Nitrogen prediction exhibited good accuracy (RMSE 0.0222; RRMSE 15.54%), potassium prediction showed moderate accuracy (RMSE 0.2963; RRMSE 28.46%), while phosphorus prediction resulted in relatively high errors (RMSE 1066.77; RRMSE 80.98%), indicating the need for further model development.
Design and Implementation of an Artificial Neural Network Model for Soil Nitrogen Prediction Rita Anggraini; Sri Ratna Sulistiyanti; Helmy Fitriawan; FX Arinto Setyawan; Mareli Telaumbanua; Witaningsih Witaningsih
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 2 (2026): April 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i2.732-742

Abstract

The availability of nitrogen in soil is a crucial factor determining crop productivity. However, the measurement of total nitrogen (N-total) content requires considerable time and cost. Therefore, a fast, accurate, and easy prediction method is needed to support the agricultural development. This study aims to develop an Artificial Neural Network (ANN) model based on the backpropagation algorithm to identify soil N-total content using soil pH, moisture content, and soil resistance as input parameters. The model was trained using the trainbr training function with variations of logsig and tansig activation functions and hidden layer structures of 5–5, 8–8, and 12–12 to obtain the best configuration. The training results indicate that the tansig–tansig combination with 8–8 hidden layer structure achieved the highest performance, with a R2 training of 0.953 and a R2 testing of 0.911. The model was implemented in the form of a Graphical User Interface (GUI) application to facilitate field-level prediction. Validation using 40 testing data samples showed a classification accuracy of 70% and an R² value of 0.932 for nitrogen prediction. The model correctly classified 28 data samples out of the total 40 tested data. These results indicate that the proposed model is capable of predicting soil nitrogen content accurately and reliably.
Co-Authors F X Arinto Setyawan Helmy Fitriawan Mareli Telaumbanua Rita Anggraini Setyawan, FX Arinto Sri Ratna Sulistiyanti