Research Article
Deep Learning Technique for Desert Plant Classification and Recognition
@INPROCEEDINGS{10.1007/978-3-031-04409-0_17, author={Najla Alsaedi and Hanan Alahmadi and Liyakathunisa Syed}, title={Deep Learning Technique for Desert Plant Classification and Recognition}, proceedings={Machine Learning and Intelligent Communications. 6th EAI International Conference, MLICOM 2021, Virtual Event, November 2021, Proceedings}, proceedings_a={MLICOM}, year={2022}, month={5}, keywords={Bark texture Deep learning techniques Prewitt edge detection}, doi={10.1007/978-3-031-04409-0_17} }- Najla Alsaedi
Hanan Alahmadi
Liyakathunisa Syed
Year: 2022
Deep Learning Technique for Desert Plant Classification and Recognition
MLICOM
Springer
DOI: 10.1007/978-3-031-04409-0_17
Abstract
Recognition of desert plants has been a difficult activity for both human and computers due to similarities between these plants. In this paper, we propose an approach for recognizing desert plants by images of the bark. This approach depends on deep learning techniques for image recognition. The recognition process depends on texture of the bark. Therefore, we use Prewitt edge detection and Hough transform to detect the bark from original image. Further, we build a bark dataset for desert plants; this dataset consists of 1660 bark images for five species of desert plants. Each species in the dataset has 332 images. These species are Palm Dates, Mimosa Scabrella, Sidr, Lemon and Pomegranate. Convolutional Neural Network (CNN) is a deep learning technique that used in image classification tasks. Therefore, we test CNN on our dataset, and it gives an accuracy of 99.8%. Performance of CNN is very high, hence CNN can be adapted for recognition of desert plants.
