Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method

Abstract

In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions.

Surrogate modelling of a detailed farm‐level model using deep learning

“…It learns the sequence and the reversed sequence of the inputs. Alibabaei et al (2021) use a BiLSTM to model evapotranspiration and soil water content in irrigation scheduling. RNNs are also helpful for non‐sequential data.…”

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

“…Alibabaei et al [102] used Bidirectional LSTM (BLSTM), CNN-LSTM, and a simple LSTM model to model daily reference evapotranspiration and soil-water content. Meteorological weather data for three sites in Portugal were collected from the stations Póvoa de Atalaia, Estação Borralheira, and Direção Regional de Agricultura e Pescas do Centro, Portugal.…”

“…Moreover, the performance of these models depends on the choice of hyperparameters, loss functions, and optimization algorithm [60,67]. Algorithms such as Bayesian optimization [110] can help to find the right hyperparameters [102]. Google researchers used neural architecture search (NAS) algorithm [111] to find state of the art MobilenetV3 [112].…”

Comparison of On-Policy Deep Reinforcement Learning A2C with Off-Policy DQN in Irrigation Optimization: A Case Study at a Site in Portugal

“…Table 2 shows the action and state sets. Two BLSTM models were implemented in [29,43], to predict tomato yield using climate big data, irrigation amount, and soil profile water content, and to estimate SWTD and ET0 from climate data, respectively. Before training a neural network, hyperparameters should be established.…”

“…In this work, the trained BLSTM models of [29,43] were used as features in the agent's environment. The BLSM model for tomato yield achieved an R 2 -score of 0.97 and a Root Mean Square Error (RMSE) of 366 (kg/ha) on the test data set, and the BLSTM model for predicting SWTD achieved an RMSE of 6.841 mm and an R 2 -score of 0.98 on the test data set for tomato yield.…”