Precision agriculture in India

As the agricultural sector is facing numerous challenges including global warming, resource/land scarcity, and global growing population, precision farming has emerged as one of the solutions in advancing agricultural productivity, efficiency, and sustainability. Since the overall agricultural sustainability depends on environmental factors, this chapter aims to determine the role of environmental factors in optimizing crop selection and enhancing agricultural productivity through precision farming techniques in India. In this chapter, we employed a machine learning technique, in particular, the Random Forest (RF) model, to determine the relationship between the ratio of nitrogen, phosphorus, and potassium content in the soil, humidity, pH value in the soil, rainfall, and crop type. To determine the most effective number of features at each split, three values of mtry (2, 3, and 4) were evaluated during a ten-fold cross-validation. The findings of the study demonstrate that environmental factors influence crop selection. The chapter provides additional insights for sustainable agricultural policies in India and confirms the validity of using machine learning to optimize farming practices.