Image-based high-throughput plant phenotyping utilises innovative imaging infrastructures to conduct automated and non-invasive time-series measurements of morphological, physiological, and biochemical traits of plant species. The primary aim is to determine the structure, performance, and tolerance to limitations of an individual plant or group of plants in a laboratory, glasshouse, or field environment. Recognition of plant ecotypes plays an important role while evaluating, selecting, and producing cultivars. However, it is challenging, time-consuming and nearly impossible to correctly identify multiple ecotypes purely based on visual inspection or through manual measurements of phenotypic traits. This research aims to classify various ecotypes of the experimental plant known as Arabidopsis thaliana grown in a controlled growth chamber. 40 ecotypes are scanned from the top-view daily taking four images using a high-resolution RGB camera. Upon data collection, all images are organised and stored in the database for subsequent use. Afterwards, several deep learning models are optimised, trained, and evaluated to conduct the classification of various ecotypes considering a single or sequence of images approach. For the sequence of images, models are trained and evaluated in order to achieve desirable performance for specific days after sowing (DAS). Owing to the high variability among replicates within each ecotype, certain samples have to be excluded by extracting general leaf phenotypic traits and computing morphological similarity. Finally, applied deep learning models demonstrated a high level of accuracy and precision during the ecotype classification task, which provides an opportunity to utilise trained models in different indoor environments such as glasshouse.