Integrating Soil Sensing and Rainfall Modelling for Precision Crop Recommendation Systems

This research article offers a sophisticated machine learning-based method for altitude, temperature, pressure, sunshine, rainfall, nutrients, and soil moisture data-driven soil state, and crop type analysis. The dataset is rigorously preprocessed, with seaborn used to visualize and handle null values. The process behind the article explains the four soil states of soil according to the encoding of categorical output classes: dry, very dry, wet, and very wet. Several crop types are to be predicted depending on the parameters taken. Random Forest Classifier, Decision tree, KNN, and Naive Bayes models were trained with accuracies of respectively, following data normalization and splitting into training and testing sets. This work demonstrates how machine learning can effectively classify soil conditions, which opens up new possibilities for agricultural and environmental management applications.