Recognition of Ziziphus lotus through Aerial Imaging and Deep Transfer Learning Approach

Ahsan Bin Tufail,Yong-Kui Ma,Adnan Yousaf,Habib Hamam,Luqman Ali,Ateeq Ur Rehman,Inam Ullah,Wajdi Alhakami,Omar Cheikhrouhou,Rahim Khan,Fazlullah Khan

doi:10.1155/2021/4310321

Abstract

There is a growing demand for the detection of endangered plant species through machine learning approaches. Ziziphus lotus is an endangered deciduous plant species in the buckthorn family (Rhamnaceae) native to Southern Europe. Traditional methods such as object-based image analysis have achieved good recognition rates. However, they are slow and require high human intervention. Transfer learning-based methods have several applications for data analysis in a variety of Internet of Things systems. In this work, we have analyzed the potential of convolutional neural networks to recognize and detect the Ziziphus lotus plant in remote sensing images. We fine-tuned Inception version 3, Xception, and Inception ResNet version 2 architectures for binary classification into plant species class and bare soil and vegetation class. The achieved results are promising and effectively demonstrate the better performance of deep learning algorithms over their counterparts.

Highlights

Rates for the destruction of habitat and plant species are on the rise worldwide due to several alterations in the land cover and land use which can significantly impact the environment and society
Study Areas. e training and validation zone for training and validating the convolutional neural networks (CNNs) based model is located in Cabo de Gata-Nıjar Natural Park, 36°49′43′′ North, 2°16′22′′ West, which is located in the province of Almerıa, Spain. e vegetation is found to be scarce and patchy, dominated by Ziziphus lotus plants that are surrounded by a mix of bare soil and small scrubs [45]. ere are two test zones. e first test zone is located one and a half kilometer away from the training zone, 36°49′28′′ North, 2°17′28′′ West. e second test zone is located in Rizoelia National Forest Park in Cyprus, 34°56′09′′ North, 33°34′26′′ East [45]
We explored, analyzed, and compared the performance of deep learning architectures for the classification of Ziziphus lotus which is an endangered species in the European habitat ecosystem

Summary

Introduction

Rates for the destruction of habitat and plant species are on the rise worldwide due to several alterations in the land cover and land use which can significantly impact the environment and society. A very high-resolution spatial remote sensing provides detailed information about vegetation [23], man-made, water, green vegetation, and bare soil [24], as well as mapping wildlife habitat [25] Deep learning techniques such as convolutional neural networks (CNNs) have application in healthcare [26] and other domains [27,28,29,30] and are getting popularity for the classification of land cover using light detection and ranging (LIDAR) and Landsat imagery across different time points [31], scene classification using very high-resolution cameras for remote sensing applications [32], and hyperspectral imagery [33,34,35,36]. A description of the proposed methodology is given in Section 3 followed by the experiments, discussion, and conclusion in Sections 4, 5, and 6, respectively

Prior Art

Proposed Methodology

Experiments

Findings

Conclusion