###### Abstract  
Agricultural greenhouses must accurately predict environmental factors to ensure optimal crop growth and energy management efficiency. However, the existing predictors have limitations when dealing with dynamic, non-linear, and massive temporal data. This study proposes four supervised learning techniques focused on linear regression (LR) and Support Vector Regression (SVR) to predict the internal temperature of a greenhouse. A meteorological station is installed in the greenhouse to collect internal data (temperature, humidity, and dew point) and external data (temperature, humidity, and solar radiation). The data comprises a one year, and is divided into seasons for better analysis and modeling of the internal temperature. The study involves sixteen experiments corresponding to the four models and the four seasons and evaluating the models' performance using R\({}^{2}\), RMSE, MAE, and MAPE metrics, considering an acceptability interval of \(\pm 2\)\({}^{\circ}\)C. The results show that LR models had difficulty maintaining the acceptability interval, while the SVR models adapted to temperature outliers, presenting the highest forecast accuracy among the proposed algorithms. s 2  
A country's social and economic prosperity is strongly tied to its sustainable agricultural base. Sustainable practices aim to improve agricultural productivity while reducing harmful environmental impacts through innovative technologies in Sustainable Agriculture Supply Chains (ASCs) [3; 4; 5]. The ASCs technologies are a hopeful answer to the challenges faced by agriculture. Technology, specifically data-driven methods, is a promising solution to global agriculture problems. By collecting and analyzing farm data, we can make informed decisions regarding agricultural practices. This approach has been proven to increase crop yield, decrease costs, and promote sustainability [6]. The smart farm, also called digital agriculture, is essential to the new agricultural revolution towards green practices, with science and technology at the center of its operation. Smart farming technologies are crucial in supplying organic agriculture products, which are now in higher demand. These technologies could assist in controlling and reducing the use of chemicals, antibiotics, and synthetic chemical fertilizers, which is good for the health of consumers and farmers [7; 8]. The current smart farm is based on the greenhouse environment [9]. Greenhouses are systems that protect crops from factors that can cause them damage. They consist of a closed structure with a cover of translucent material. These aim to maintain an independent climate inside, improving the growth conditions for increasing the quality and quantity of products. These systems can produce in a particular place without restricting agroclimatic conditions [10]. Greenhouse systems must be designed according to the environmental conditions of the place where they will be installed. Control of the microclimate is necessary for the optimal development of the plant since it represents 90% of the crop production yield, where the equipment, shape, and elements of the greenhouse will depend on how different the outdoor climate is from the plant's requirements [11]. The effectiveness of a control system in a greenhouse is related to the description of the variables that affect the behavior of the climate. It can help design and practically use the agricultural process at all stages. It comprises emerging digital technologies such as remote sensing, wireless sensor networks, Cloud Computing (CC), Internet of Things (IoT), image processing, and Artificial intelligence (AI) [12; 13]. Sensors and actuators are used to regulate farming processes, while wireless sensor networks are being used to monitor the farm. Farmers can use wireless cameras and sensors to remotely collect data through videos and pictures. With the help of IoT and CC technology, farmers can also monitor the condition of their agricultural land using their smartphones from anywhere in the world.