A Novel Approach to Identify Leaf Vein Morphology Using Laplacian Filter and Deep Learning for Plant Identification

Abstract

Identification of different plants, weeds, or any related type of vegetation is an important aspect of agricultural robotics and technologies. With the help of image processing and computer vision, multiple attempts have been made to achieve these results. These approaches made use of the shape and color of the leaf to identify a particular plant. But it can be observed that this approach has some limitations resulting in false positive and true negative errors. To overcome these limitations, the authors propose a novel approach of using Laplacian filter to extract veins morphology of leaves of a plant. This veins pattern is unique to every plant. With this Laplacian filter and data augmentation techniques, a unique dataset is developed on which a deep learning model can be trained. Based on this approach, the proposed system applies a deep learning algorithm called YOLO for plant identification. After preprocessing and YOLO training, the model is able to distinguish between plant and a weed successfully and create a bounding box for the detected type of plant.