International Journal of Science, Engineering and TechnologyAuthors: Bhargavi M R, Anitha.V
Abstract: In precision agriculture, there is a need for precise, cost-efficient, and timely assessment of soil nutrients to ensure appropriate fertilization, minimize environmental risks, and maximize crop production. Laboratory soil analysis is considered highly accurate; however, it is relatively costly, laborious, and spatially limited. In this study, a holistic data mining model for predicting the content of soil macronutrients, including Nitrogen (N), Phosphorus (P), and Potassium (K), based on multiple soil samples collected from spatially distributed locations and multi-sensor fusion techniques, is described. The suggested model combines kriging interpolation, a Hybrid Random Forest-Multiple Linear Regression (RF-MLR) model with 73-87% accuracy, and ANN model with 89% accuracy for estimating N content. The effectiveness of the presented approach was tested for 2,500 soil samples collected from agricultural land, which showed that pH and EC values have a high correlation with the content of P and K, respectively, whereas OC level shows a significant correlation with the abundance of N. Overall, the developed method decreases testing expenses by 70% when compared to laboratory techniques, offering sufficient accuracy.
DOI: https://doi.org/10.5281/zenodo.20233719
