Modeling for the Prediction of Soil Moisture in Litchi Orchard with Deep Long Short-Term Memory

Peng Gao,Hongbin Qiu,Jianqiang Lu,Weixing Wang,Yubin Lan,Wadi Chen,Xiongzhe Han

doi:10.3390/agriculture12010025

Abstract

Soil moisture is an important factor determining yield. With the increasing demand for agricultural irrigation water resources, evaluating soil moisture in advance to create a reasonable irrigation schedule would help improve water resource utilization. This paper established a continuous system for collecting meteorological information and soil moisture data from a litchi orchard. With the acquired data, a time series model called Deep Long Short-Term Memory (Deep-LSTM) is proposed in this paper. The Deep-LSTM model has five layers with the fused time series data to predict the soil moisture of a litchi orchard in four different growth seasons. To optimize the data quality of the soil moisture sensor, the Symlet wavelet denoising algorithm was applied in the data preprocessing section. The threshold of the wavelets was determined based on the unbiased risk estimation method to obtain better sensor data that would help with the model learning. The results showed that the root mean square error (RMSE) values of the Deep-LSTM model were 0.36, 0.52, 0.32, and 0.48%, and the mean absolute percentage error (MAPE) values were 2.12, 2.35, 1.35, and 3.13%, respectively, in flowering, fruiting, autumn shoots, and flower bud differentiation stages. The determination coefficients (R2) were 0.94, 0.95, 0.93, and 0.94, respectively, in the four different stages. The results indicate that the proposed model was effective at predicting time series soil moisture data from a litchi orchard. This research was meaningful with regards to acquiring the soil moisture characteristics in advance and thereby providing a valuable reference for the litchi orchard’s irrigation schedule.

Highlights

Introduction published maps and institutional affilLitchi is a traditional fruit in China that has been cultivated for over 3500 years [1].China’s litchi yield has reached about one-third of the world’s total yield [2]
It can be concluded that the wavelet denoising method had a significant effect on improving data quality with the decrease in the dispersion of soil moisture
It can be inferred from the reduction in the standard deviation (Std) that when the litchi was in the growing season from February to April, the soil moisture sensor detected the most significant noise in collecting data, which might have been caused by environmental interference [45]

Summary

Introduction

Introduction published maps and institutional affilLitchi is a traditional fruit in China that has been cultivated for over 3500 years [1].China’s litchi yield has reached about one-third of the world’s total yield [2]. Litchi is a traditional fruit in China that has been cultivated for over 3500 years [1]. China’s litchi yield has reached about one-third of the world’s total yield [2]. It is an economic crop that is planted in many other countries, such as Australia, Thailand, and. It requires suitable temperature and soil moisture conditions, as the temperature has a significant influence on the flowering, fruit, and other indicators. A sufficient water supply is the basis for the growth of litchi, thereby ensuring good cultivation [4]. The growth and yield are negatively affected by unreasonable conditions

Results

Discussion

Conclusion