# An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth

arXiv: https://arxiv.org/abs/2012.04041 · HTML: https://mlsanity.com/paper/2012.04041/

## Key facts

- Cited by 7 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Contrastive Domain Adaptation](https://mlsanity.com/paper/2103.15566/) (2021)
- [Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing](https://mlsanity.com/paper/2304.09876/) (2023)
- [Fully Homomorphically Encrypted Deep Learning as a Service](https://mlsanity.com/paper/2107.12997/) (2021)
- [AI-enabled Efficient and Safe Food Supply Chain](https://mlsanity.com/paper/2105.00333/) (2021)
- [MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction](https://mlsanity.com/paper/2306.10164/) (2023)
- [Interpretable System Identification and Long-term Prediction on Time-Series Data](https://mlsanity.com/paper/2303.01193/) (2023)
- [Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review](https://mlsanity.com/paper/2412.10538/) (2024)

## Abstract (arXiv)

Multi-step prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since multiple step forecasting generally fails due to accumulation of prediction errors. This paper presents a novel approach for predicting plant growth in agriculture, focusing on prediction of plant Stem Diameter Variations (SDV). The proposed approach consists of three main steps. At first, wavelet decomposition is applied to the original data, as to facilitate model fitting and reduce noise in them. Then an encoder-decoder framework is developed using Long Short Term Memory (LSTM) and used for appropriate feature extraction from the data. Finally, a recurrent neural network including LSTM and an attention mechanism is proposed for modelling long-term dependencies in the time series data. Experimental results are presented which illustrate the good performance of the proposed approach and that it significantly outperforms the existing models, in terms of error criteria such as RMSE, MAE and MAPE.