The collection and identification of lichen samples within the reserved forest pose significant challenges, leading researchers to seek alternative methodologies. Manual identification of lichens requires microscopic and chemical analysis, which is time-consuming and requires domain expert knowledge. Deep learning models can automatically learn complex lichen features from images, thereby avoiding manual feature extraction. In this context, an in-depth study was conducted using two approaches to evaluate the efficacy of several network models for the identification of lichens in digital images. In the initial method, a total of 119 lichen images, comprising 15 images from experimental work and additional images from open sources, were employed for the traditional classification approach utilizing MATLAB. In which, 90 different features were extracted from various color models such as RGB, YCbCr, HSV, CMYK, LAB, YIQ and subjected for classification in ANN model, which resulted 76.6 % accuracy for the class of lichen family. With the limited original images, the transfer learning enables effective model training and makes the process faster and scalable. In this approach, each species of lichen images was augmented using generative augmentation tool and subjected for multiclass deep learning network models, precisely transfer learning model. In total, 10,410 images comprising 5 thallus type, 8 order and 77 distinct lichen species were used to train three different network models (VGG16, VGG19, Res.Net50). It was found that Res.Net50 model exhibited highest sensitivity (>0.99) and precision. The class lichen thallus type achieved a maximum accuracy of 86.6 %, while the order exhibited 80 %. Overall, the lichen species under the order of Laccanorals with fruticose thallus type showed highest classification accuracy in all the three models attempted in this study. The study concluded that utilizing transfer learning with a multilayer perceptron model proved to be more effective and accurate for the lichen dataset, especially when dealing with greater complexity in picture information. The obtained results were comparable to those of other recent deep learning approaches. Keywords: Lichens; Classification; Color models; Principal component analysis; Feed forward neural network; VGG16; VGG19; Res.Net50.