Description

Plant Species Identification Using OpenCV and Python

Species knowledge is essential for protecting biodiversity. The identification of plants by conventional keys is complex, time consuming, and due to the use of specific botanical terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Today, there is an increasing interest in automating the process of species identification. 

An image classification process can generally be divided into the following steps 

• Image acquisition—The purpose of this step is to obtain the image of a whole plant or its organs so that analysis towards classification can be performed.

• Preprocessing—The aim of image preprocessing is enhancing image data so that undesired distortions are suppressed and image features that are relevant for further processing are emphasized. The preprocessing sub-process receives an image as input and generates a modified image as output, suitable for the next step, the feature extraction. Preprocessing typically includes operations like image denoising, image content enhancement, and segmentation. These can be applied in parallel or individually, and they may be performed several times until the quality of the image is satisfactory.

• Feature extraction and description—Feature extraction refers to taking measurements, geometric or otherwise, of possibly segmented, meaningful regions in the image. Features are described by a set of numbers that characterize some property of the plant or the plant’s organs captured in the images.

• Classification—In the classification step, all extracted features are concatenated into a feature vector, which is then being classified.