Fast Classification of Leaf Images for Agricultural Remote Sensing Applications
Abstract
This paper introduces a method of classifying leaves using machine learning. Considerable emphasis has been put on leaf classification for use in remote sensing applications such as plant phenotyping and precision agriculture. Convolutional neural networks (CNN) have been extensively used in computer vision for image classification. However, CNN can be computationally expensive. This paper describes a method that achieves a comparable accuracy, with a lower computational burden, using a support vector machine (SVM) classifier. This method uses image processing algorithms to extract features from Hough transform and Hough Lines. These features are then integrated with those extracted from binary images, and "eigenleaves" extracted from grayscale, gradient, and different color-space images of leaves as data preprocessing for classification. The classifier is implemented on two publicly available datasets: Flavia and Swedish; and is able to achieve state-of-the-art accuracies using a SVM classifier.
Recommended Citation
V. Gajjar et al., "Fast Classification of Leaf Images for Agricultural Remote Sensing Applications," Proceedings of the International Telemetering Conference, Scimago Labs, Jan 2018.
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-000000000-2
International Standard Serial Number (ISSN)
0884-5123
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Scimago Labs, All rights reserved.
Publication Date
01 Jan 2018