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

• 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
• ISSN: 2227-7080; 2227-7080
• DOI: 10.3390/technologies12060077

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.