## 1 Introduction  
Plant health is crucial for agricultural success as it can be affected by biotic and abiotic stress. Real-time monitoring of plant physiological status helps identify signs of stress conditions and disease, enabling early intervention. Advanced techniques like multispectral and hyperspectral imaging provide spectral and spatial information, making them useful for food and agricultural applications. The ability of multispectral cameras to collect data on a limited number of spectral bands, as opposed to hyperspectral imaging, allows for a more rapid and cost-effective assessment of plant physiology, including size, shape, and color, but also presents the potential for disease detection [1]. The most well-known applications are the use of satellites and drones as they are designed to survey large areas due to their small size and cost accessibility [2]. The main disadvantages of the remote sensing (RS) approach are the lower sensitivity and spatial resolution of the images, which are only useful for providing the evolution of the average state of a field [3]; the acquisition of more detailed data on the state of individual plants requires close-up images, such as those mounted on rovers [4]. More and more proximal sensing platforms have been applied to identify differences in plant physiology due to growth and responses to stresses. Over the last few years, several researchers have proposed phenotyping platforms to estimate several morphological indices for plants grown in greenhouses, such as weight, height, and leaf area [5; 6], supporting cultivation and weed management decisions [7]. Commercially available systems (e.g., the PlantEyeF500 (PE) 3D scanner) have been commonly employed to support weed management [7] and to monitor physiological crop stress [8]. On the other hand, other research lines have developed prototype designs: hyperspectral imaging (HSI) was implemented to monitor the detrimental effects of salinity on the physiological and biochemical processes of okra [9]. Similarly, [10] developed a low-cost hyperspectral camera, Senop HSC-2, within an HTP platform to evaluate the drought stress resistance and physiological responses of four tomato genotypes. Recently, [11] developed a mobile platform equipped with a commercial multispectral camera (RedEdge-MX) at close range to characterize the water stress response in poplar seedlings (_Populus_ L.). Meanwhile, hyperspectral proximal sensing was trained to detect short-term water drought effects in broccoli [12]. A miniaturized multicamera imaging sensor to simplify the analysis of physiological indices in lettuce was proposed by [13]. A rotating wheel of interchangeable band-pass filters in the optical visible and near-infrared (VIS-NIR) and thermal, long-wave infrared (LWIR) ranges was proposed as a low-cost solution for multispectral camera-based scouting systems [14]. In contrast to traditional point spectroscopy, imaging spectroscopy allows it to be easier to automatically obtain spectral data from specific crop regions. To identify and detect drought stress [8; 12; 15], weed competition [7], the impact of environmental changes [16], and nutrient deficits [17] and to distinguish between various types of stress [18], multispectral and hyperspectral imaging has proven to be effective. Specifically, compared with conventional visual observations, changes in the concentration of pigments such as anthocyanins [19] and chlorophyll [20] can be identified with significantly higher sensitivity. Spectral indices are now frequently used to track the health of plants since they offer an indirect measure of photosynthetic capability [21]. By analyzing these indices, possible issues throughout different temporal and spatial domains can be promptly diagnosed, and resource management, such as water and fertilizers, is enhanced [22].