Abstract
The determination of plant species from field observation requires substantial botanical expertise, which puts it beyond the reach of most nature enthusiasts. Traditional plant species identification is almost impossible for the general public and challenging even for professionals who deal with botanical problems daily such as conservationists, farmers, foresters, and landscape architects. Even for botanists themselves, species identification is often a difficult task. This paper proposes a model deep learning with a new architecture Convolutional Neural Network (CNN) for leaves classifier based on leaf pre-processing extract vein shape data replaced for the red channel of colors. This replacement improves the accuracy of the model significantly. This model experimented on collector leaves data set Flavia leaf data set and the Swedish leaf data set. The classification results indicate that the proposed CNN model is effective for leaf recognition with the best accuracy greater than 98.22%.
Highlights
Image-based methods are considered a promising approach for species identication
Extract leaf vein shape To perform the extraction of leaf vein shape, the image segmentation process involves converting the image to grayscale, and using adaptive thresholding techniques to segment the image and extract the vein leaf image
Swedish leaf data set: The Swedish leaf data set has been captured as part of a joined leaf classication project between the Linkoping University and the Swedish Museum of Natural History.[20]
Summary
Image-based methods are considered a promising approach for species identication. A user can take a picture of a plant in theeld with the built-in camera of a mobile device and analyze it with an installed recognition application to identify the species or at least to receive a list of possible species if a single match is impossible. Image acquisition: The purpose of this step is to obtain the image of a whole plant or its organs so that analysis towards classication can be performed. The aim of image preprocessing is enhancing image data so that the 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 modied image as output, suitable for the 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. Classication: In the classication step, all extracted features are concatenated into a feature vector, which is being classied
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