## 1 Introduction  
_Dendrobium noble_ (_Orchidaceae_) is an endangered orchid species listed under Appendix II of CITES [1] and forms the largest vascular epiphytes having features of CAM plants [2]. It serves as an ornamental and medicinal food for humans [3]. Recently HPLC fingerprints showed a strong inhibiting effect on cancer cells, through increased bioactive compounds; signifying _D. noible_ as a functional herb for the market [4]. Leaf area is a necessary bio-empirical variable that helps not only in the computation of various physiological indices such as leaf area index (LAI), specific leaf area (SLA), net assimilation rate (NAR), specific leaf weight (SLW), leaf area duration (LAD) and various plant physiological mechanisms viz., photosynthesis, respiration, light interception, transpiration, etc. [5, 6, 7], but also water-related anatomical traits such as leaf density and vein density, thus, helping in water conservation and transport ultimately resulting in the ability of Dendrobium to cope in water stress situations in the environment [8]. Leaf area, shape, size, and number present per plant affects the source-sink relationship and ultimately the yield of the plant as green leaves act as a primary source of assimilates for petals in Dendrobium orchid [9, 10]. Leaf area is also responsible for nutrient spray responsiveness in Dendrobium [11]. The leaf area trait, i.e., specific leaf area (SLA), is strongly influenced by phylogeny (k value > 1) indicating strong conservation in changes in this trait over evolution in Dendrobium [12]. Leaf area further can act as an important component of complex process-based plant growth models for the development of support decision-based systems for management of cultural practices in Dendrobium. Therefore, leaf area (LA) is a fundamental component of estimation of physiological, ecological, evolutionary, and anatomical traits that affect the survival, growth, and distribution of Dendrobium. Leaf area modeling enables an easy way of predicting LA through models which aid in a non-destructive estimation, rather than manual methods. LA can be measured directly through techniques such as photographing [13], image analysis, blueprinting, use of expensive instruments including digital planimeter [14], scanning planimeter [15], etc. Major disadvantage in these direct methods lies in the removal of leaves, which poses a problem of not only for the experiments which are time-shared, but also leads to a decline in these valuable plant samples, in our case a rare and endangered orchid species. Other problems in these methods can be mentioned as requirements of time and labor, costly instruments, reduction in canopy affecting photosynthesis, growth, etc. The above bottleneck in LA estimation is successfully overcome through the various non-invasive technologies using very few measurements such as leaf length (L) and leaf width (W) or its different combination [16; 17; 18; 19], constant leaf area term KA [20] or a correction factor [21]. These indirect LA estimation methods are performed in situ [22], low in cost, reliable, provide fast results without detachment of the leaves from the plants, and also excludes biological variations in the experiment [23]. In our study, an _Orchidaceae_ family member, _D. nobile_, was assessed for LA estimation where conservation prioritization is essential and automated conservation assessments were recently performed through deep learning [24]; thus making it inevitable to use the non-destructive ML methods for development of LA prediction models. Machine learning (ML) techniques have gained a lot of importance due to their non-requirement of explicit instructions, i.e., a data driven nature, and their reliable, non-destructive, fast, accurate, high throughput, user friendly and less laborious approach of solving real life problems. Recently, ML and modeling has become very popular in the field of LA estimation, as it allows the adjustments and improvements of the model for accurate prediction of LA in crops.