A Novel Method of Hyperbola Recognition in Ground Penetrating Radar (GPR) B-Scan Image for Tree Roots Detection

Ground penetrating radar (GPR), as a newly nondestructive testing technology (NDT), has been adopted to explore the spatial position and the structure of the tree roots. Due to the complexity of soil distribution and the randomness of the root position in the natural environment, it is difficult to locate the root in the GPR B-Scan image. In this study, a novel method for root detection in the B-scan image by considering both multidirectional features and symmetry of hyperbola was proposed. Firstly, a mixed dataset B-Scan images were employed to train Faster RCNN (Regions with CNN features) to obtain the potential hyperbola region. Then, the peak area and its connected region were filtered from the four directional gradient graphs in the proposed region. The symmetry test was applied to segment the intersecting hyperbolas. Finally, two rounds of coordinate transformation and line detection based on Hough transform were employed for the hyperbola recognition and root radius and position estimation. To validate the effectiveness of this approach for tree root detection, a mixed dataset was made, including synthetic data from gprMax as well as field data collected from 35 ancient tree roots and fresh grapevine controlled experiments. From the results of hyperbola recognition as well as the estimation for the radius and position of the root, our method shows a significant effect in root detection.

ANALISIS STATIS MENGGUNAKAN MOBILE SECURITY FRAMEWORK UNTUK PENGUJIAN KEAMANAN APLIKASI MOBILE E-COMMERCE BERBASIS ANDROID

Pengguna internet di Indonesia setiap tahunnya mengalami peningkatan yang terus naik. Peningkatan yang pesat ini diiukuti juga dengan penggunaan internat menggunakan perangkat mobile. Hal ini memberikan dampak positif ke beberapa sektor bisnis seperti jual beli online dan juga memicu munculnya beragam aplikasi mobile khususnya pada platform android. Oleh karena itu perlu dilakukan analisis keamanan terhadap aplikasi dengan melakukan pengujian/pengukuran terhadap tingkat keamanan aplikasi. Tujuan dari penelitian ini adalah untuk meningkatkan pemahaman kepada pengguna aplikasi mobile e-commerce terhadap celah-celah keamanan aplikasi mobile e-commerce dan memberikan metode dalam melakukan analisis statis menggunakan Mobile Security Framework (MobSF) untuk melakukan pengujian keamanan terhadap aplikasi mobile e-commerce khususnya yang berbasis android. Analisis statis dilakukan dengan melakukan anailis terhadap kelemahan kriptografi (weak crypto), SSL bypass, penggunaan dangerous permission, hardcode secret, root detection dan domain malware check. Metode yang digunakan dalam melakukan anailis adalah Mobile Security Framework (MobSF). Sistem ini mempunyai tiga fase, yaitu kebutuhan perencanaan, proses desain RAD dan fase implementasi. Hasil analisis keamanan keamanan yang dilakukan pada aplikasi mobile e-commerce yaitu SP, TP, LZ, BL dan SR yang merupakan lima besar mobile e-commerce berbasis android paling populer di Indonesia menunjukkan bahwa beberapa celah keamanan masih terdapat dari di kelima aplikasi hasil tersebut yang perlu diketahui baik oleh pengguna maupun pengembang aplikasi.

Root detection by resistivity imaging and physiological activity with the dead-end trench on three clones of cocoa (Theobroma cacao)

Maghfiroh CN, Putra ETS, Dewi HSES. 2020. Root detection by resistivity imaging and physiological activity with the dead-end trench on three clones of cocoa (Theobroma cacao). Biodiversitas 21: 2794-2803. Indonesia is one of the largest cocoa production countries in Southeast Asia, but has low average productivity (982 kg/ha) based on Indonesian Plantation Statistics (2017). Productivity increase effort by giving dead-end trenches that applied to collect organic material, accommodate surface runoff sediments, prevent erosion, and maintain nutrient availability. Dead-end trench had an impact on root cutting and improve rooting. Clone is very influential on cocoa (Theobroma cacao L.) yield. The superior clones recommended are clones RCC-70, RCC-71, and KKM-22, which have high productivity and are resistant to pests and diseases. The objectives of this research were (i) to detect the presence of roots by resistivity imaging (ii) to study the effects of dead-end trench application on physiological activities of cocoa leaves and yields of three clones (RCC-70, RCC-71, and KKM-22); (iii) to determine which cocoa clone (s) performed a significant yield increase with the application of dead-end trench. Research was conducted in August 2018-April 2019 at PT. Pagilaran cocoa plantation in North Segayung Production Unit, subdistrict Tulis, Batang, Central Java. The experiment was arranged in a randomized complete design with two factors and three blocks as replications. The first factor was dead-end trench application (with or without dead-end trench application) and the second factor was cocoa clones (RCC-70, RCC-71, and KKM-22). This study showed that dead-end trench applications affected plant roots based on the interpretation results of geoelectric-resistivity measurements. Dead-end trench application significantly affected chlorophyll a, stomatal conductance, transpiration, and H2O leaf content. Dead-end trench application has no significant effect on the content of chlorophyll b, total chlorophyll content, nitrate reductase activity, stomatal density, CO2 leaf content, and photosynthesis rate.

Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review

Attention to the natural environment is equivalent to observing the space in which we live. Plant roots, which are important organs of plants, require our close attention. The method of detecting root system without damaging plants has gradually become mainstream. At the same time, machine learning has been achieving good results in recent years; it has helped develop many tools to help us detect the underground environment of plants. Therefore, this article will introduce some existing content related to root detection technology and machine detection algorithms for root detection, proving that machine learning root detection technology has good recognition capabilities.

Advanced GPR Signal Processing Techniques for Root Detection in Urban Environments

<p>Street trees are widely recognised to be an essential asset for the urban environment, as they bring several environmental, social and economic benefits [1]. However, the conflicting coexistence of tree root systems with the built environment, and especially with road infrastructures, is often cause of extensive damage, such as the uplifting and cracking of sidewalks and curbs, which could seriously compromise the safety of pedestrians, cyclists and drivers.</p><p>In this context, Ground Penetrating Radar (GPR) has long been proven to be an effective non-destructive testing (NDT) method for the evaluation and monitoring of road pavements. The effectiveness of this tool lies not only in its ease of use and cost-effectiveness, but also in the proven reliability of the results provided. Besides, recent studies have explored the capability of GPR in detecting and mapping tree roots [2]. Algorithms for the reconstruction of the tree root systems have been developed, and the spatial variations of root mass density have been also investigated [3].</p><p>The aim of this study is, therefore, to investigate the GPR potential in mapping the architecture of root systems in street trees. In particular, this research aims to improve upon the existing methods for detection of roots, focusing on the identification of the road pavement layers. In this way, different advanced signal processing techniques can be applied at specific sections, in order to remove reflections from the pavement layers without affecting root detection. This allows, therefore, to reduce false alarms when investigating trees with root systems developing underneath road pavements.</p><p>In this regard, data from trees of different species have been acquired and processed, using different antenna systems and survey methodologies, in an effort to investigate the impact of these parameters on the GPR overall performance.</p><p> </p><p><strong>Acknowledgements</strong></p><p>The authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust. This paper is dedicated to the memory of our colleague and friend Jonathan West, one of the original supporters of this research project.</p><p> </p><p><strong>References</strong></p><p>[1] J. Mullaney, T. Lucke, S. J. Trueman, 2015. “A review of benefits and challenges in growing street trees in paved urban environments,” Landscape and Urban Planning, 134, 157-166.</p><p>[2] A. M. Alani, L. Lantini, 2019. “Recent advances in tree root mapping and assessment using non-destructive testing methods: a focus on ground penetrating radar,” Surveys in Geophysics, 1-42.</p><p>[3] L. Lantini, F. Tosti, Giannakis, I., Egyir, D., A. Benedetto, A. M. Alani, 2019. “A Novel Processing Framework for Tree Root Mapping and Density Estimation using Ground Penetrating Radar,” In 10th International Workshop on Advanced Ground Penetrating Radar, EAGE.</p>

Tree root detection from ground surface vibration measurements

Rapid development of urban infrastructure in past decades together with a relatively recent growth of awareness of its impact on the natural environment result in an increased interest in non-destructive ground interrogation methods. Tree root damage is a very well known issue in civil engineering and can emerge as road surface fracture, building foundations disintegration or pipe penetration, among others. In this paper we investigate the feasibility of using a vibroacoustic method for tree root mapping. The core of the idea is that the mechanical waves induced by an excitation mechanism acting on the tree trunk propagate to the roots and then radiate into the surrounding soil. Owing to that, the response measured at the ground surface contains the contribution of waves radiating from roots and can be used for mapping their extent. In this paper, we report a set of field experiments on a ‘purpose-built’ root-trunk model buried underground. These preliminary results both demonstrate the technique and shed light on related challenges and limitations.