Soil salinity prediction based on hybrid classifier: study on Bellary and Chamarajanagar district in Karnataka.

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. [ABSTRACT FROM AUTHOR]