干旱区生态环境知识资源中心(Arid): Soil salinity prediction based on hybrid classifier: study on Bellary and Chamarajanagar district in Karnataka

Arid

DOI	10.1007/s11042-023-16652-8
	Soil salinity prediction based on hybrid classifier: study on Bellary and Chamarajanagar district in Karnataka
	Vijayalakshmi, V.; Kumar, D. Mahesh; Kumar, S. C. Prasanna; Veeramani, S.
通讯作者	Vijayalakshmi, V
来源期刊	MULTIMEDIA TOOLS AND APPLICATIONS
ISSN	1380-7501
EISSN	1573-7721
出版年	2023
英文摘要	Soil salinization is one of the most frequent environmental concerns that contribute to the degradation of agricultural land, particularly in arid and semi-arid regions. The correct methods must be developed by farm owners and decision-makers in order to reduce soil erosion and increase crop output. For this, accurate spatial forecasting and soil salinity modeling in agricultural areas are needed. The accurate consideration of environmental elements under the scale effects, which have received less attention in prior research, is essential for digital soil mapping. The goal of this research is to create a special technique for predicting soil salinity. Preprocessing is done on the sentinel image input first. The next step is to determine the spectral channels, salinity index, and vegetation index. The development of transformation-based features also takes advantage of enhanced PCA. The suggested hybrid classifier uses Deep Belief Network (DBN) and Bidirectional Long Short Term Memory (Bi-LSTM) to predict salinity while accounting for these variables. The final forecast result is determined by the increased score level fusion. To improve the precision and accuracy of the prediction, Self Upgraded BSO (SU-BSO) calibrates the weights of the Bi-LSTM and DBN. The MSE values of the suggested technique are lower than those of other conventional methods like CNN, DBN, SVM, BI-LSTM, MLP-FFA, and MLSR metrics, achieving lower values of 0.13, 0.07, 0.03, 0.05, 0.09, and 0.094%, respectively. Finally, numerous measurements are employed to demonstrate the value of the selected approach.
英文关键词	Soil salinization Proposed PCA Deep Belief Network Optimized Bi-LSTM SU-BSO Algorithm
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001086868500001
WOS关键词	GROWTH
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS研究方向	Computer Science ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397874
推荐引用方式 GB/T 7714	Vijayalakshmi, V.,Kumar, D. Mahesh,Kumar, S. C. Prasanna,et al. Soil salinity prediction based on hybrid classifier: study on Bellary and Chamarajanagar district in Karnataka[J],2023.
APA	Vijayalakshmi, V.,Kumar, D. Mahesh,Kumar, S. C. Prasanna,&Veeramani, S..(2023).Soil salinity prediction based on hybrid classifier: study on Bellary and Chamarajanagar district in Karnataka.MULTIMEDIA TOOLS AND APPLICATIONS.
MLA	Vijayalakshmi, V.,et al."Soil salinity prediction based on hybrid classifier: study on Bellary and Chamarajanagar district in Karnataka".MULTIMEDIA TOOLS AND APPLICATIONS (2023).