Vol.33, No.1, 2022, pp.515-534, doi:10.32604/iasc.2022.023449
OPEN ACCESS
ARTICLE
Efficient Urban Green Space Destruction and Crop Stress Yield Assessment Model
  • G. Chamundeeswari1, S. Srinivasan1,*, S. Prasanna Bharathi1,2
1 Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Thandalam, Chennai, 602105, India
2 Department of Electronics and Communication Engineering, College of Engineering & Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, 600026, India
* Corresponding Author: S. Srinivasan. Email:
Received 08 September 2021; Accepted 11 October 2021; Issue published 05 January 2022
Abstract
Remote sensing (RS) is a very reliable and effective way to monitor the environment and landscape changes. In today’s world topographic maps are very important in science, research, planning and management. It is quite possible to detect the changes based on RS data which is obtained at two different times. In this paper, we propose an optimal technique that handles problems like urban green space destruction and detection of crop stress assessment. Firstly, the optimal preprocessing is performed on the given RS dataset, for image enhancement using geometric correction and image registration. Secondly, we propose the improved cat swarm optimization algorithm to optimize the greenery region with the help of vegetation index parameters like Normalized Difference Built-up Index (NDBI) & Normalized Difference Vegetation Index (NDVI). Thirdly, we use Conditional Principal Component Analysis (PCA) to reduce dimension of a response matrix & retain the dominant information to identify key vegetation indices and the classification of crops. Then, an optimal decision maker-based post classification method is introduced to differentiate area changes based on the overlay of two or more classified images. From the simulation results we observed and conclude that the performance of proposed crop classification, crop stress and yield assessments performed very effective compared to existing methods in terms of F-Measure, recall, precision & accuracy.
Keywords
Remote sensing; crop classification; crop stress assessment; green space destruction; machine learning; preprocessing
Cite This Article
G. Chamundeeswari, S. Srinivasan and S. Prasanna Bharathi, "Efficient urban green space destruction and crop stress yield assessment model," Intelligent Automation & Soft Computing, vol. 33, no.1, pp. 515–534, 2022.
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