Site suitability for Aromatic Rice cultivation by integrating Geo-spatial and Machine learning algorithms in Kaliyaganj C.D. block, India - Debabrata Sarkar; Sunil Saha; Manab Maitra, B.Sc. in Geography; Prolay Mondal, Ph.D. (Artificial Intelligence in Geosciences, 2021)
The purpose of this work is to assess the soil fertility for Tulaipanji rice cultivation in Kaliyaganj C.D. Block using the Analytic Hierarchy Process (AHP) and Machine learning algorithms along with the field survey data and GIS. A total of 40 soil samples from Tulaipanji rice fields (from 0 to 40 ​cm depth) have been randomly collected for the analysis of the soil health condition. For the purpose of assigning ratings to the parameters, ten experts' opinions were taken into account. The final soil fertility map indicates that 18.01% of the land is in excellent health condition to support Tulaipanji cultivation. The artificial neural networks (ANN), support vector machine (SVM), and Bagging models-based suitability analysis was also done using geo-spatial and soil data for Tulaipanji cultivation. Nevertheless, the ANN is the more appropriate model for locational analysis of Tulaipanji cultivation. The ANN-based findings show that areas of 25.8% (77.89 sq. km) are excellent for growing Tulaipanji rice, about 22.01% (66.45 sq. km) are highly suitable, 19.84% (59.90 sq. km) are moderately suitable, 21.19% (63.97 sq. km) are low suitable and 11.16% (33.69 sq. km) are not suitable for Tulaipanji rice cultivation. The receiver operating characteristic (ROC) curve depicts that the applied models have a high degree of accuracy. This endeavour will aid much in the soil fertility and site suitability assessment that will aid local government officials, academics, and the framers, to utilize the lands in a scientific way.