## 1 Introduction  
Agriculture has become increasingly important in recent years due to its various challenges in producing food from environmental, social, and economic standpoints. The United Nations has projected that the global population will grow to 9.7 billion by 2050 [1], making it essential to produce enough food. However, the available agricultural land is limited, requiring significant efficiency gains to maximize production with scarce resources. Furthermore, special attention should be paid to climate change, which will require reducing greenhouse gas emissions as its atmospheric concentration is projected to double by 2030 and cause significant global temperature increases [2; 3]. Climate-Smart Agriculture (CSA) is a system that utilizes the latest technological advancements to tackle the challenges faced in agriculture. The Food and Agriculture Organization of the United Nations (FAO) defines CSA as a tool that enhances national food security and development goals. It involves various new technologies that work together to achieve sustainable productivity, resilience, and reduction in/removal of greenhouse gas emissions [4; 5]. The CSA concept encompasses the Smart Farming (SF) initiative, which promotes eco-friendly agricultural practices that rely on science and technology. SF is comparable to Industry 4.0's smart factories, utilizing Information and Communication Technologies (ICT) like the Internet of Things (IoT), Global Positioning System (GPS), Cloud Computing (CC), Fog Computing (FC), and Big Data (BD) analysis to monitor and manage farms and farming activities [6; 7; 8]. These technologies are driving the transformation of the agricultural sector towards a smarter and more sustainable one, contributing to the significant problems facing agriculture [9]. Smart Farming includes the greenhouse concept essential for profitable and sustainable agricultural practices. Greenhouses control important factors affecting crop growth, such as solar radiation, temperature, humidity, light intensity, and carbon dioxide levels, helping to increase yields throughout the year and protect crops from harsh weather conditions and pests [10; 11]. However, the unstable conditions within a greenhouse can negatively impact plant growth and ultimately reduce crop production. This issue can be mitigated using Artificial Intelligence (AI) tools that regulate the greenhouse variables [12; 13; 14]. Machine Learning (ML) is the subtopic of AI that allows a system to learn from previous experiences and adjust accordingly. By analyzing large amounts of data, ML can make accurate predictions.This technology helps reduce pre-harvest crop loss, often caused by inadequate irrigation and climate conditions. By utilizing data collected from sensors and an automated watering system, farmers can optimize their crop yields and minimize losses [15; 16]. Various agricultural tasks can benefit significantly from ML algorithms, which have produced state-of-the-art results. The most prominent models include Linear Regressions (LRs), Support Vector Machines (SVMs), K-Nearest Neighbor (k-NN), Neural Networks (NNs), Random Forest (RF) classifiers, and Decision Trees (DTs) [17]. Additionally, emerging forecasting methods such as Recurrent Neural Networks (RNNs) and Extreme Gradient Boosting (XGBoost) help analyze time series data. The Long Short-Term Memory (LSTM)-RNN model and XGBoost are particularly effective at avoiding the issue of vanishing and exploding gradients during training [18; 19]. Predicting microclimate conditions in greenhouses through climate forecasting has become crucially important. This is possible due to the advanced sensors and systems that enable exact measurements and evaluations of the microclimate within seconds.