Life on Earth depends heavily on plants. Various methods have been developed to extract a plant leaf’s attributes. In order to extract the characteristics, several classification techniques are used. Because there are so many different kinds of plants in the world, it may be difficult to remember their names; a method for identifying plant types was developed. The conventional way to identify a plant is by its leaves. To extract the leaf-based characteristics from the leaf picture, image processing techniques are applied. Eventually, leaf identification was accomplished using machine learning techniques. This article contrasts the methods used by other authors that have worked on various occluded leaf species identification methodologies with this recommended method for identifying leaf species.The unique method for expressing forms described in this article is termed „Tracing Leaf Outline” (TLO). The TLO approach has a low processing complexity and is also reasonably easy to apply. It is demonstrated that this technique can identify the leaf species although there is a sizable proportion of leaf occlusion (let’s say, about 50% occlusion). Standard datasets for Flavia leaf species have been used to test this method. The efficiency of the proposed technique is evaluated using accuracy, precision, recall, and F1 score. These measures have produced a result of 99.6%, which is greater than all prior findings, and have significantly exceeded the traditional methodologies for identifying plant leaf species.