# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
## 1 Introduction
This technology employs sensors to determine the actual moisture content of the soil. It adjusts the time of water irrigation based on this information. However, one of soil moisture sensors current limitations is their inability to report on or represent the entire farm. Farmers must install a large number of soil moisture sensors in each area of the farm to monitor soil moisture, which raises their costs. As a result, soil moisture value forecasting is a low-cost but promising software-based alternative that requires fewer sensors and can produce accurate predictions when given the right set of input data. There are significant advantages to combining technological advances and farmer experience, such as improved crop quality and yield, as well as water savings through effective irrigation mechanisms. Our ultimate goal is to develop an automated water irrigation management system that uses a variety of technologies and tools to aid farmers' decision-making and automate the water management process. The Internet of Things (IoT) makes use of various types of sensors and wireless communication technologies to provide an efficient and effective information collection and management infrastructure. Furthermore, with the massive amounts of data that are frequently generated by such IoT devices, there must be an efficient way to analyze the collected data and use it for decision support via machine learning (ML) methods. ML methods are widely used in agriculture, for example, to predict or identify soil. In this paper, we primarily focus on methods for forecasting soil moisture. Several machine learning methods, including Artificial Neural Networks (ANN), Random Forests(RF), Support Vector Machines (SVM), and elastic net regression, were used to predict soil moisture using satellite imagery (EN). A method proposed in [13] used Landsat 8 satellite imagery as well as some geospatial data of land-use types on previously untested conditions in an Iranian semi-arid region. The authors use satellite optical and thermal sensors to calculate soil reflectance and estimate soil moisture. One study [14] proposed a soil moisture prediction model based on deep learning regression networks. Further, Ref. [15] describes a novel soil moisture prediction method in vineyards based on digital images and a multilayer perceptron (MLP) and support vector regression (SVR) implementation. Both methods presented by the authors were successful in soil moisture forecasting, with high correlation values between the predicted and measured soil moisture value when tested on unseen data. A soil moisture prediction method using a Convolutional Neural Network (CNN) is presented in [16]. The authors of [14] proposed a soil moisture prediction model based on a deep learning regression network (DNNR) using meteorological and soil moisture data. In [17], a relevance vector machine (RVM) model for content estimation was presented. Predicting soil moisture content is described using a variety of machine learning models, including Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Multiple Linear Regressions (MLR). The authors of [18] conclude that the ANFIS and SVM models are more suitable for predicting soil water con-tent under water stress conditions. A new soil moisture prediction method in vineyards using digital images with a support vector regression (SVR) and multilayer perceptron (MLP) implementation was presented in [16]. Both methods were successful in forecasting soil moisture and produced high correlation values between measured and predicted moisture when tested on unknown data. A new ResBiLSTM model to predict soil water content was proposed by [19]. The authors of [20; 21; 22] all investigated soil moisture estimation using satellite-based data soil moisture content prediction in fields using a CNN-based method, which was presented in [23].