###### Abstract
In order to optimize the plant aerial parts in aeroponic growing lettuce, the appropriate value definition of irrigation water pressure, droplet size and fogging interval is needed to improve the continuous water and nutrient availability. The definition of these aeroponic lettuce cultivation conditions is a current research area of interest in the scientific community [6; 8]. The root development process is crucial for the plant growth performance. The understanding of fundamental mechanisms regulating the root system architecture (RSA) is a key factor for future crop improvement [9; 10; 11; 12]. Consequently, further research, focused on maintaining the best nutrient and water conditions by identifying the droplet size and root nebulization intervals throughout the plant growing period, is needed [7]. Over the past decade, the combination of digital imaging and machine learning techniques to address agricultural problems has been one of the most explored areas of digital agriculture [13]. Agricultural sustainability promotes the development of new technologies for lower environmental impacts, greater accessibility to farmers and, consequently, greater productivity. The use of computer vision with deep learning methods allows the early and correct identification of the evaluated stress-causing agent [14]. Hyperspectral [14; 15; 16; 17; 18; 19; 20; 21] and multispectral images [22; 23; 24; 25; 26; 27; 28] are increasingly important in rapid and non-destructive testing for diagnosing plant diseases. The main image-based techniques for plant stress detection [29] (water stress and other stress types) are thermography [30; 31; 32], visible reflectance (VIS) [33], shortwave and near infrared (SWIR/NIR) [34], far infrared (FIR) and fluorescence [35]. These techniques evaluate the plant water content [36], being one of the most common biochemical parameters limiting the photosynthesis efficiency and crop productivity [37]. Water stress is one of the most critical abiotic factors restricting crop development. Alternatively, plant temperature variation can be clearly detected by thermal images before crop wilting is evident; early water and saline stage identification is achieved by this thermal analysis. Nevertheless, thermal analysis has also been used for stress monitoring in fruit trees, such as sweet cherry trees [38]. Besides, diverse non-invasive optical spectroscopic techniques, including Raman and Elastic spectroscopy, have been used for plant physiology monitoring and assessment [39; 40]. Alternatively, optical parameters, such as spectral reflectance indexes, have been employed as a non-invasive technique in lettuce physiology assessment [41; 42; 43; 44; 45; 46] for early-stage detection of Botrytis cinerea after 12, 18, 36, 60 and 84 h [47]. The vegetative growth parameters of the root and foliar systems for aeroponic lettuce crops are reported in this paper. The obtained results are similar to the growth parameters reported by [48; 49]. In addition, the growth and thermal parameters of the plant root are also reported, the characterization of which is only possible in aeroponic crops, where the described analysis of these parameters is one of the main contributions of this work. Theseparameters are obtained from the processing of multispectral images captured by a vision system installed in an aeroponic growth chamber. The main objectives of the presented study are: (i) to develop aeroponics crops based on root growth visualization, (ii) to design and implement a multispectral vision system for image capturing of lettuce plant roots and leaves, and (iii) to develop multispectral image algorithms for measuring morphometric and thermal descriptors to characterize the lettuce plant growing process through statistical analysis of the computed parameters. According to the obtained results, it is concluded that aeroponic crops represent a favorable alternative for the growth parameter study of a root and leaf lettuce system in a non-invasive way.