Segmentation of leaves is an initial step in image-based plant phenotyping, which has received more attention recently. Leaf segmentation shows features of the leaf and growth stages at the leaf level. Due to the significant overlapping between leaves and fluctuating environmental circumstances, such as intensity change and blur due to wind, segmentation of plant tissues like leaves is a most important issue in plant phenotyping. The leaf segmentation work has more complicated problems, such as leaf surface, genotypes, length, structure, and thickness variability. This study proposes an ultra-modern deep gaining knowledge of architectures: UUNet++, a convolutional neural network for advance segmentation. A vital assessment is done using Computer Vision for Plant Phenotyping dataset (CVPPP). The plant phenotyping is performed on tobacco and Arabidopsis leaf of RGB images from plant village dataset. The proposed model needs less space as compared to the main UNet architecture. In contrast to typical deep learning image sets, this dataset contains a limited number of samples. Despite that, we achieve exceptional results in leaf segmentation, specifically in distinguishing the interior of leaves from the binary segmentation of the entire leaf. Furthermore, studies are necessary to accurately quantify the number of leaves. Without reducing performance measures, the disease classification inferences time is extremely efficient and accurate.