An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration

Abstract

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.

A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop

“…The high performance of the LSTM model is attributed to its special asset among traditional RNNs, characterized by its special gates that are responsible for controlling the flow, the volume, and the type of the previously attained and processed information that is going to be maintained and forwarded from the memory cell to the next hidden state. The utilization of the Aquacrop 7.0 model to simulate soil MC data due to data limitations in the investigated fields, equipped the LSTM model with a consistent and adequate volume of data in order to perform precise predictions for irrigation scheduling in maize crops, confirming its effectiveness towards the accurate and reliable simulation of moisture content (MC)[17] that is also mirrored in our findings.For both investigated fields, the LSTM model becomes more efficient in predicting MC reduction as we move through the four investigated phases, since the dataset is enriched…”

“…Aquacrop 7.0 is regarded as a reliable tool due to its proven capability of simulating successfully parameters used as input data. These types of data can include soil MC, evapotranspiration (ET), and yield in the occasion that they fail to be measured in a continuous and accurate manner during experimental field measurements [17]. In the current study, the failure of acquiring continuous and accurate records is often attributed to sensor malfunctions and data transmission errors.…”

Evaluating the Impact of Controlled Ultraviolet Light Intensities on the Growth of Kale Using IoT-Based Systems

“…The effectiveness of IoT-enabled sensors and actuators in optimizing crop growth parameters and reducing resource wastage is demonstrated in these studies [14,21]. For example, scholarly investigations into precision irrigation systems have demonstrated substantial water conservation and enhanced agricultural productivity by utilizing automated irrigation scheduling and real-time monitoring of soil moisture levels [10,22]. The utilization of IoT sensors for soil health monitoring has also allowed farmers to evaluate microbial activity [21], nutrient levels [23], pH balance [23,24], humidity [25,26], and temperature [25,26].…”

Improving Irrigation Performance by Using Adaptive Border Irrigation System

“…Scientific irrigation scheduling can solve the problems of whether or not to irrigate and when to irrigate and can determine the necessary amount of irrigation water [15]. Effective research has been achieved in this area, which has improved the efficiency of irrigation water use [16,17]. However, further research is needed on how to reduce the labor demand and overcome the influence of the natural parameter variation during irrigation to ensure satisfactory irrigation performance.…”