Title: Hybridisation of nonlinear autoregressive model with deep long short-term memory network for crop damage detection using time series data

Authors: Saravanan Radhakrishnan; V. Vijayarajan

Addresses: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Rd., Katpadi, Vellore, Tamil Nadu 632014, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Rd., Katpadi, Vellore, Tamil Nadu 632014, India

Abstract: Climate change and population growth have intensified crop damage in recent years. This paper introduced nonlinear autoregressive model with exogenous inputs fused with deep long-short term memory (NARX-DLSTM) for detecting crop damage at an early stage. The input time series data undergoes preprocessing, followed by the extraction of technical indicators like relative strength index (RSI), double exponential moving average (DEMA), weighted moving average (WMA), simple moving average (SMA), Welles Wilder's smoothing average (WWS), moving average convergence divergence (MACD), linear regression forecast (LRF) and lowest low value (LL). Then, feature selection is performed using weighted Euclidean distance (WED), and data augmentation is applied through synthetic minority over-sampling technique (SMOTE). Finally, NARX-DLSTM is performed which is the combination of DLSTM and NARX recurrent neural networks, achieves mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and relative absolute error (RAE) of 0.200, 0.447, 0.165, and 0.114.

Keywords: weighted Euclidean distance; WED; crop damage detection; deep long-short term memory; DLSTM; synthetic minority over-sampling technique; SMOTE; recurrent neural networks; RNNs.

DOI: 10.1504/IJAMECHS.2026.150489

International Journal of Advanced Mechatronic Systems, 2026 Vol.13 No.1, pp.60 - 75

Received: 20 Sep 2024
Accepted: 19 Jun 2025
Published online: 15 Dec 2025 *

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