## 1 Introduction  
Agriculture plays an important role in environmental degradation due to its extensive use of fertilizers and pesticides, high water and energy consumption, homogenization of the agricultural mosaic, and the loss of biodiversity [1, 2, 3]. The new concept of \"smart farms\" refers to management techniques that use advanced technologies to improve food quantity, quality, and safety while minimizing environmental impacts [4, 5]. The purpose of smart farming includes increasing farming sustainability and productivity, improving the knowledge and skills of the agricultural workforce, optimizing costs, reducing waste, creating smart agriculture information and Decision Support Systems (DSSs), reducing greenhouse gas emissions, and developing resilience and adaptation to climate change [4, 5, 6, 7, 8]. Internet of Things (IoT) sensors and Machine Learning algorithms (ML) are technology sectors related to smart farming [8]. The IoT is a worldwide network of physical items that are outfitted with sensors and actuators that link to the Internet in real-time in order to be detected, sensed, and controlled remotely [9]. The sensors are used to monitor soil water content, climate parameters, animal behavior, cultural practices, etc. ML is a subfield of artificial intelligence that analyses data, learns from data, recognizes patterns, and makes predictions with little human intervention. It is used to improve the intelligence of the sensors and analyze the large datasets retrieved from the sensors. Ultimately, it can support the design of DSSs [4; 5; 10]. A subclass of ML known as Deep Learning (DL) attempts to discover usable representations of the unknown structure in the input distribution, often at many levels, using the learned lower-level features as the input to the higher-level features [11; 12]. Compared to traditional ML models, DL algorithms have more hidden layers and can automatically extract features from the input data. Specific applications of DL in agriculture management include disease detection [13; 14; 15], fruit detection and yield prediction [16; 17; 18], weed detection [19; 20; 21], soil management [22; 23], and water management [24; 25]. In recent years, several studies have attempted to create a DSS for irrigation management using ML and DL algorithms. These algorithms provided estimates for irrigation-related variables such as soil moisture content and evapotranspiration. In a study conducted by [PERSON] et al. [26], ML algorithms including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Boosted Regression Trees (BRTs), Random Forest Regression (RFR), multiple regression trees (CART), and Artificial Neural Networks (ANNs) were used to predict soil moisture in the Red River Valley of the North in North Dakota and Minnesota, where corn, soybeans, wheat, sugar beets, barley, canola, and potatoes are grown. The best results were obtained with the RFR and BRT algorithms. To predict soil moisture measured by Soil Moisture Active Passive (SMAP) V3 satellite observations using the LSTM method, [PERSON] et al. [17] used atmospheric influences, static physiographic attributes such as the sand, silt, and clay fractions, bulk density, and soil capacity and simulated soil moisture from the Noah hydrologic model [17]. Using model simulations, the approach largely removes biases and improves predicted soil moisture by achieving low test root-mean-squared errors (0.035) and high correlation coefficients of more than 0.85. [PERSON] and [PERSON] [29] predicted soil moisture for a period of 12 to 13 weeks in advance using machine learning approaches such as linear regression, support vector machine regression, Principal Component Analysis (PCA), and naive Bayes. These methods were applied to four separate datasets from 13 different districts in West Bengal and to four different crops (potato, mustard, rice, and cauliflower). The best model was Support Vector Machine (SVM), followed by linear regression models, which outperformed the PCA and naive Bayes models. In [PERSON] et al.