Abstract
Oxygen is a sensitive indicator of atmospheric compositional changes and is also a primary requirement for human life. With deforestation on the rise, dwindling level of oxygen concentration in the air is a concern for mankind. Our paper aims to estimate the oxygen production levels of a particular area of land captured by satellite imagery. Our algorithm aims to identify forests and agricultural land patches and analyzes the oxygen production in a very efficient manner. Further, the algorithm takes in images of leaves from these areas and processes them to identify the species, chlorophyll content, and nitrogen levels in the plant using feature selection and probabilistic neural networks (PNN). We have computed the oxygen level of the patch of land and these calculations were performed on images spanning over few years allowing us to calculate the changes in oxygen production. This not only helps to map the carbon footprint, but also acts as a curative measure for global warming.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.X., Chang, Y.F., Xiang, Q.L.: A leaf recognition algorithm for plant classification using probabilistic neural network. In: 2007 IEEE International Symposium on Signal Processing and Information Technology, pp. 11–16. IEEE (2007, December)
Abdullahi, H., Sheriff, R., Mahieddine, F.: Convolution neural network in precision agriculture for plant image recognition and classification. In: 2017 Seventh International Conference on Innovative Computing Technology (Intech), pp. 1–3. IEEE, Londrés (2017, August)
Weng, Q.: Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J. Environ. Manag. 64(3), 273–284 (2002)
Zhang, L., Zhang, B., Li, W., Li, X., Sun, L., Jiang, L., Liu, X.: Spatiotemporal changes and drivers of global land vegetation oxygen production between 2001 and 2010. Ecol. Ind. 90, 426–437 (2018)
Tracewski, Ł., Butchart, S.H., Di Marco, M., Ficetola, G.F., Rondinini, C., Symes, A., Buchanan, G.M.: Toward quantification of the impact of 21st-century deforestation on the extinction risk of terrestrial vertebrates. Conserv. Biol. 30(5), 1070–1079 (2016)
Karcher, D.E., Richardson, M.D.: Quantifying turfgrass color using digital image analysis. Crop Sci. 43(3), 943–951 (2003)
Sowmya, D.R., Shenoy, P.D., Venugopal, K.R.: Remote sensing satellite image processing techniques for image classification: a comprehensive survey. Int. J. Comput. Appl. 161(11) (2017)
Ali, M.M., Al-Ani, A., Eamus, D., Tan, D.K.: An algorithm based on the RGB colour model to estimate plant chlorophyll and nitrogen contents. In: International Proceedings of Chemical, Biological and Environmental Engineering, vol. 57 (2013)
Congalton, R.G., Green, K.: Assessing the Accuracy of Remotely Sensed Data: Principles and Practices Boca Rotan. Lewis Publishers, Florida (1999)
Yang, X.: Satellite monitoring of urban spatial growth in the Atlanta Metropolitan area. Photogramm. Eng. Remote Sens. 68(7), 725–734 (2002)
Jensen, J.R.: Digital Change Detection. Introductory Digital Image Processing: A Remote Sensing Perspective. Prentice-Hall, New Jersey (2004)
Chaki, J., Parekh, R.: Plant leaf recognition using shape based features and neural network classifiers. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 2(10) (2011)
Bama, B.S., Valli, S.M., Raju, S., Kumar, V.A.: Content based leaf image retrieval (CBLIR) using shape, color and texture features. Indian J. Comput. Sci. Eng. 2(2), 202–211 (2011)
Ab Jabal, M.F., Hamid, S., Shuib, S., Ahmad, I.: Leaf features extraction and recognition approaches to classify plant. J. Comput. Sci. 9(10), 1295 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Saha, A., Debgupta, R., Chaudhuri, B.B. (2020). A Novel and Efficient Spatiotemporal Oxygen Production Estimation Based on Land Vegetation Using PNN and Convolutional Autoencoder. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (eds) Intelligent Computing and Communication. ICICC 2019. Advances in Intelligent Systems and Computing, vol 1034. Springer, Singapore. https://doi.org/10.1007/978-981-15-1084-7_29
Download citation
DOI: https://doi.org/10.1007/978-981-15-1084-7_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1083-0
Online ISBN: 978-981-15-1084-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)