Enhancing Process Control in Agriculture: Leveraging Machine Learning for Soil Fertility Assessment

Context: The primary factor in determining whether or not a crop can be produced on a certain type of soil is soil fertility.When faced with many options, farmers frequently find it difficult to decide which crop to plant.We created this project to address that particular issue.The provision of soil data is mandatory since it will significantly influence the determination of the soil's fertility.The output and accuracy of the model may suffer if the data are not supplied discretely.The nature of the dataset indicates that the result is a binary value, i.e., either "Fertile" or "Non-Fertile", along with the accuracy percentage of each algorithm.Objective: The main aim of this paper is to determine whether the soil is fertile based on soil properties like N, P, K, Ph, nutrient level, moisture levels, temp, rainfall, and topography.Material/Method: We used the dataset from Kaggle, where N, P, K, and pH values are input into the model, and the ML determines whether it is fertile or not.In this article, four machine learning classifiers are trained, and determine the best classifier based on the performance metrics.Result: The results demonstrated that the machine learning classifier significantly improves prediction accuracy.We used LR, KNN, NB, and DT classifiers to increase the accuracy, as well as to increase the efficiency of the soil fertility assessment.The DT classifier exhibited well in comparison to other classifiers.The DT classifier's accuracy was 89%, but the performance metrics precision, LR, and KNN, was 90%.