A hybrid vegetation detection framework: Integrating vegetation indices and convolutional neural network

Sumayyah Dzulkifly

A hybrid vegetation detection framework: Integrating vegetation indices and convolutional neural network

AVAILABLE Article 1615 Views

Back to search

Scan QR Code to share or view item link:

Digital Assets & Download Files

General Information Cached

Title

A hybrid vegetation detection framework: Integrating vegetation indices and convolutional neural network

Type

Article

Main Author

Sumayyah Dzulkifly

Subject

T Technology (General)

ISSN

2073-8994

Language Code

eng

Bibliographic Details

Place of Production

Tanjong Malim

Publisher

Fakulti Seni, Komputeran dan Industri Kreatif

Year of publication

2021

Degree Type

Others

Corporate Name

Universiti Pendidikan Sultan Idris

Abstract

Vegetation inspection and monitoring is a time-consuming task. In the era of industrial revolution 4.0 (IR 4.0), unmanned aerial vehicles (UAV), commercially known as drones, are in demand, being adopted for vegetation inspection and monitoring activities. However, most off-the-shelf drones are least favoured by vegetation maintenance departments for on-site inspection due to limited spectral bands camera restricting advanced vegetation analysis. Most of these drones are normally equipped with a normal red, green, and blue (RGB) camera. Additional spectral bands are found to produce more accurate analysis during vegetation inspection, but at the cost of advanced camera functionalities, such as multispectral camera. Vegetation indices (VI) is a technique to maximize detection sensitivity related to vegetation characteristics while minimizing other factors which are not categorised otherwise. The emergence of machine learning has slowly influenced the existing vegetation analysis technique in order to improve detection accuracy. This study focuses on exploring VI techniques in identifying vegetation objects. The selected VIs investigated are Visible Atmospheric Resistant Index (VARI), Green Leaf Index (GLI), and Vegetation Index Green (VIgreen). The chosen machine learning technique is You Only Look Once (YOLO), which is a clever convolutional neural network (CNN) offering object detection in real time. The CNN model has a symmetrical structure along the direction of the tensor flow. Several series of data collection have been conducted at identified locations to obtain aerial images. The proposed hybrid methods were tested on captured aerial images to observe vegetation detection performance. Segmentation in image analysis is a process to divide the targeted pixels for further detection testing. Based on our findings, more than 70% of the vegetation objects in the images were accurately detected, which reduces the misdetection issue faced by previous VI techniques. On the other hand, hybrid segmentation methods perform best with the combination of VARI and YOLO at 84% detection accuracy.

References

Ab Rahman, A. A., Wan Mohd Jaafar, W. S., Abdul Maulud, K. N., Noor, N. M., Mohan, M., Cardil, A., . . . Naba, N. I. (2019). Applications of drones in emerging economies: A case study of malaysia. Paper presented at the International Conference on Space Science and Communication, IconSpace, , 2019-July 35-40. doi:10.1109/IconSpace.2019.8905962 Retrieved from www.scopus.com

Ancin-Murguzur, F. J., Munoz, L., Monz, C., & Hausner, V. H. (2020). Drones as a tool to monitor human impacts and vegetation changes in parks and protected areas. Remote Sensing in Ecology and Conservation, 6(1), 105-113. doi:10.1002/rse2.127

Barbosa, B. D. S., Ferraz, G. A. S., Gonçalves, L. M., Marin, D. B., Maciel, D. T., Ferraz, P. F. P., & Rossi, G. (2019). RGB vegetation indices applied to grass monitoring: A qualitative analysis. Agronomy Research, 17(2), 349-357. doi:10.15159/AR.19.119

Bassine, F. Z., Errami, A., & Khaldoun, M. (2019). Vegetation recognition based on UAV image color index. Paper presented at the Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019, doi:10.1109/EEEIC.2019.8783830 Retrieved from www.scopus.com

Bayr, U., & Puschmann, O. (2019). Automatic detection of woody vegetation in repeat landscape photographs using a convolutional neural network. Ecological Informatics, 50, 220-233. doi:10.1016/j.ecoinf.2019.01.012

Berni, J. A. J., Zarco-Tejada, P. J., Suárez, L., González-Dugo, V., & Fereres, E. (2009). Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. Int.Arch.Photogramm.Remote Sens.Spatial Inform.Sci, 38(6) Retrieved from www.scopus.com

Csillik, O., Cherbini, J., Johnson, R., Lyons, A., & Kelly, M. (2018). Identification of citrus trees from unmanned aerial vehicle imagery using convolutional neural networks. Drones, 2(4), 1-16. doi:10.3390/drones2040039

Di Gennaro, S. F., Toscano, P., Cinat, P., Berton, A., & Matese, A. (2019). A low-cost and unsupervised image recognition methodology for yield estimation in a vineyard. Frontiers in Plant Science, 10 doi:10.3389/fpls.2019.00559

Di Leo, E. (2019). Individual tree crown detection in UAV remote sensed rainforest RGB images through mathematical morphology. Remote Sens, 11, 1309. Retrieved from www.scopus.com

Dustin, M. C. (2015). Monitoring Parks with Inexpensive UAVs: Cost Benefits Analysis for Monitoring and Maintaining Parks Facilities, Retrieved from www.scopus.com

Feng, Q., Liu, J., & Gong, J. (2015). UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sensing, 7(1), 1074-1094. doi:10.3390/rs70101074

Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76-87. doi:10.1016/S0034-4257(01)00289-9

Gopinath, G. (2015). Free data and open source concept for near real time monitoring of vegetation health of northern kerala, india. Aquatic Procedia, 4, 1461-1468. Retrieved from www.scopus.com

Harbaš, I., Prentašić, P., & Subašić, M. (2018). Detection of roadside vegetation using fully convolutional networks. Image and Vision Computing, 74, 1-9. doi:10.1016/j.imavis.2018.03.008

Haroun, F., Deros, S., & Din, N. M. (2020). A review of vegetation encroachment detection in power transmission lines using optical sensing satellite imagery. Int.J.Adv.Trends Comput.Sci.Eng, 9, 618-624. Retrieved from www.scopus.com

Kalapala, M. (2014). Estimation of tree count from satellite imagery through mathematical morphology. International Journal of Advanced Research in Computer Science and Software Engineering, 4(1), 490-495. Retrieved from www.scopus.com

Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. doi:10.1016/j.compag.2018.02.016

Kaneko, K., & Nohara, S. (2014). Review of effective vegetation mapping using the UAV (unmanned aerial vehicle) method. Journal of Geographic Information System, 6(6), 733-742. Retrieved from www.scopus.com

Kestur, R., Angural, A., Bashir, B., Omkar, S. N., Anand, G., & Meenavathi, M. B. (2018). Tree crown detection, delineation and counting in UAV remote sensed images: A neural network based Spectral–Spatial method. Journal of the Indian Society of Remote Sensing, 46(6), 991-1004. doi:10.1007/s12524-018-0756-4

Larrinaga, A. R., & Brotons, L. (2019). Greenness indices from a low-cost UAV imagery as tools for monitoring post-fire forest recovery. Drones, 3(1), 1-16. doi:10.3390/drones3010006

Li, L. (2015). The UAV intelligent inspection of transmission lines. International Conference on Advances in Mechanical Engineering and Industrial Informatics, , 1542-1545. Retrieved from www.scopus.com

Li, W., Fu, H., & Yu, L. (2017). Deep convolutional neural network based large-scale oil palm tree detection for high-resolution remote sensing images. Paper presented at the International Geoscience and Remote Sensing Symposium (IGARSS), , 2017-July 846-849. doi:10.1109/IGARSS.2017.8127085 Retrieved from www.scopus.com

Li, Z., Walker, R., Hayward, R., & Mejias, L. (2010). Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques. Paper presented at the 2010 1st International Conference on Applied Robotics for the Power Industry, CARPI 2010, doi:10.1109/CARPI.2010.5624431 Retrieved from www.scopus.com

Louhaichi, M., Borman, M. M., & Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65-70. doi:10.1080/10106040108542184

McKinnon, T., & Hoff, P. (2017). Comparing RGB-based vegetation indices with NDVI for drone based agricultural sensing. Comparing RGB-Based Vegetation Indices with NDVI for Drone Based Agricultural Sensing, (17), 1-8. Retrieved from www.scopus.com

Modzelewska, A., Stereńczak, K., Mierczyk, M., Maciuk, S., Bałazy, R., & Zawiła-Niedźwiecki, T. (2017). Sensitivity of vegetation indices in relation to parameters of norway spruce stands. Folia Forestalia Polonica, Series A, 59(2), 85-98. doi:10.1515/ffp-2017-0009

Mohd Noor, N., Abdullah, A., & Hashim, M. (2018). Remote sensing UAV/drones and its applications for urban areas: A review. Paper presented at the IOP Conference Series: Earth and Environmental Science, , 169(1) doi:10.1088/1755-1315/169/1/012003 Retrieved from www.scopus.com

Mokarram, M., Boloorani, A. D., & Hojati, M. (2016). Relationship between land cover and vegetation indices. case study: Eghlid plain, fars province, iran. European Journal of Geography, 7(2), 48-60. Retrieved from www.scopus.com

Mokarram, M., Hojjati, M., Roshan, G., & Negahban, S. (2015). Modeling the behavior of vegetation indices in the salt dome of korsia in north-east of darab, fars, iran. Modeling Earth Systems and Environment, 1(3) doi:10.1007/s40808-015-0029-y

Motohka, T., Nasahara, K. N., Oguma, H., & Tsuchida, S. (2010). Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing, 2(10), 2369-2387. doi:10.3390/rs2102369

Neupane, B., Horanont, T., & Hung, N. D. (2019). Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV). PLoS ONE, 14(10) doi:10.1371/journal.pone.0223906

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Paper presented at the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, , 2016-December 779-788. doi:10.1109/CVPR.2016.91 Retrieved from www.scopus.com

Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. Yolov3: An Incremental Improvement, Retrieved from www.scopus.com

Sanseechan, P., Saengprachathanarug, K., Posom, J., Wongpichet, S., Chea, C., & Wongphati, M. (2019). Use of vegetation indices in monitoring sugarcane white leaf disease symptoms in sugarcane field using multispectral UAV aerial imagery. Paper presented at the IOP Conference Series: Earth and Environmental Science, , 301(1) doi:10.1088/1755-1315/301/1/012025 Retrieved from www.scopus.com

Schneider, P., Roberts, D. A., & Kyriakidis, P. C. (2008). A VARI-based relative greenness from MODIS data for computing the fire potential index. Remote Sensing of Environment, 112(3), 1151-1167. doi:10.1016/j.rse.2007.07.010

Suarez, P. L., Sappa, A. D., Vintimilla, B. X., & Hammoud, R. I. (2019). Image vegetation index through a cycle generative adversarial network. Paper presented at the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, , 2019-June 1014-1021. doi:10.1109/CVPRW.2019.00133 Retrieved from www.scopus.com

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. doi:10.1016/0034-4257(79)90013-0

Watanabe, Y., & Kawahara, Y. (2016). UAV photogrammetry for monitoring changes in river topography and vegetation. Paper presented at the Procedia Engineering, , 154 317-325. doi:10.1016/j.proeng.2016.07.482 Retrieved from www.scopus.com

Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A., & White, E. (2019). Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks. Remote Sensing, 11(11) doi:10.3390/rs11111309

Xiao, C., Qin, R., & Huang, X. (2020). Treetop detection using convolutional neural networks trained through automatically generated pseudo labels. International Journal of Remote Sensing, 41(8), 3010-3030. doi:10.1080/01431161.2019.1698075

Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017 doi:10.1155/2017/1353691

Yang, M. -., Tseng, H. -., Hsu, Y. -., & Tsai, H. P. (2020). Semantic segmentation using deep learning with vegetation indices for rice lodging identification in multi-date UAV visible images. Remote Sensing, 12(4) doi:10.3390/rs12040633

Yuliantika, G., Suprayogi, A., & Sukmono, A. (2016). Analisis pengunaan saluran visible untuk estimasi kandungan klorofil daun pade dengan citra hymap. (studi kasus: Kabupaten karawang, jawa barat). Jurnal Geodesi Undip, 5(2), 200-207. Retrieved from www.scopus.com

Zhang, X., Zhang, F., Qi, Y., Deng, L., Wang, X., & Yang, S. (2019). New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV). International Journal of Applied Earth Observation and Geoinformation, 78, 215-226. doi:10.1016/j.jag.2019.01.001