Deep Learning Approaches for Water Stress Forecasting in Arboriculture Using Time Series of Remote Sensing Images: Comparative Study between ConvLSTM and CNN-LSTM Models

Deep Learning Approaches for Water Stress Forecasting in Arboriculture Using Time Series of Remote Sensing Images: Comparative Study between ConvLSTM and CNN-LSTM Models

Irrigation is crucial for crop cultivation and productivity. However, traditional methods often waste water and energy due to neglecting soil and crop variations, leading to inefficient water distribution and potential crop water stress. The crop water stress index (CWSI) has become a widely accepte...

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Bibliographic Details
Published in:Technologies (Basel) Vol. 12; no. 6; p. 77
Main Authors: Bounoua, Ismail, Saidi, Youssef, Yaagoubi, Reda, Bouziani, Mourad
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-06-2024
Subjects:
Agriculture
Arboriculture
Artificial intelligence
Case studies
Comparative analysis
Comparative studies
CWSI
Deep learning
El Nino
Environmental aspects
Forecasting
Growth models
Irrigation
Landsat satellites
Machine learning
Measurement
Morocco
Neural networks
Precipitation
Remote sensing
Satellite imagery
Satellite imaging
Sequences
Time series
Water
Water distribution
Water engineering
Water management
Water shortages
water stress
Water use
Morocco
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Summary:Irrigation is crucial for crop cultivation and productivity. However, traditional methods often waste water and energy due to neglecting soil and crop variations, leading to inefficient water distribution and potential crop water stress. The crop water stress index (CWSI) has become a widely accepted index for assessing plant water status. However, it is necessary to forecast the plant water stress to estimate the quantity of water to irrigate. Deep learning (DL) models for water stress forecasting have gained prominence in irrigation management to address these needs. In this paper, we present a comparative study between two deep learning models, ConvLSTM and CNN-LSTM, for water stress forecasting using remote sensing data. While these DL architectures have been previously proposed and studied in various applications, our novelty lies in studying their effectiveness in the field of water stress forecasting using time series of remote sensing images. The proposed methodology involves meticulous preparation of time series data, where we calculate the crop water stress index (CWSI) using Landsat 8 satellite imagery through Google Earth Engine. Subsequently, we implemented and fine-tuned the hyperparameters of the ConvLSTM and CNN-LSTM models. The same processes of model compilation, optimization of hyperparameters, and model training were applied for the two architectures. A citrus farm in Morocco was chosen as a case study. The analysis of the results reveals that the CNN-LSTM model excels over the ConvLSTM model for long sequences (nine images) with an RMSE of 0.119 and 0.123, respectively, while ConvLSTM provides better results for short sequences (three images) than CNN-LSTM with an RMSE of 0.153 and 0.187, respectively.
ISSN:2227-7080
2227-7080
DOI:10.3390/technologies12060077