Machine Learning Models to Develop Land Suitability Map for Coffee Cultivation by Integrating CHIRPS and SRTM DEM

Abstract

Kodagu region is a major coffee exporter, with production concentrated in three taluks, including the Somwarpet Taluk. Coffee yields have decreased due to unfavorable factors such as climate change, disease and insect outbreaks, landslides and inadequate land-use planning in turn affecting the family income. Thus, the goal of this research is to identify suitable land for cultivation of coffee based on Food and Agriculture Organization (FAO) land suitability assessment methodology for Somwarpet Taluk of Kodagu District. For this purpose, six soil chemical properties (potential of hydrogen, electrical conductivity, organic carbon, sulphur, iron, potassium and nitrogen), two topographic data (elevation and slope) and one climatic condition (rainfall) was considered to map land suitability for coffee crops. After determining land suitability classes for coffee cultivation, the study area was then mapped using machine learning (ML) methods such as random forest (RF), Naive Bayes (NB), K-Nearest Neigbhor (KNN), Extreme Gradient Boosting Tree (XgBoost) and Decision Tree (DT). The prediction of land suitability classes by ML model showed a significant variation. For example, in case of RF model, results showed the 94% of higher accuracy when compared to the XgBoost (93.5), DT (92%), NB (75%) and KNN (50%) models. The area of S1 (highly suitable) classified through RF, XgBoost, DT, NB and KNN was 8.66%, 8.75%, 8.57%, 19.17% and 28% respectively. Similarly, the S2 (moderately suitable) class area via RF, XgBoost, DT, NB and KNN was 84.17%, 82.18%, 81.33 %, 69.61% and 44%, respectively. Conversely, the area of S3 (marginally suitable) classified through RF, XgBoost, DT, NB and KNN was 6.64%, 7.64%, 8.5%, 10.52% and 27.8%. Correspondingly, the N (unsuitable) class area via RF outperformed the land suitability class for XgBoost, DT, NB and KNN by 0.53%, 1.43%, 1.6%, 0.7% and 0.2%. The sulphur and pH were the major limiting factor affecting the land suitability to map coffee cultivation. Thus, the methodologies developed in this study area can be very useful tool to ensure food security and carry out an effective assessment of land suitability in coffee crop growth and production for Somwarpet Taluk of Kodagu District, Karnataka State.