Ensemble Model for Predicting the Best Fruit Crop based on Soil Chemical Composition and Environmental Variables

For sustainable agriculture and food security, it is essential to choose crops that are suitable for the particular soil type and environmental circumstances. The goal of this study is to create a machine learning model that can forecast the best fruit crop based on a particular combination of environmental factors and soil chemical composition. The model outputs the most suitable fruit crops for those soil conditions and environmental variables based on input features like nitrogen (N), magnesium (Mg), phosphorus (P), potassium (K), calcium (Ca), zinc (Zn), potential of hydrogen (pH), temperature, rainfall, electrical conductivity, and levels of organic carbon (OC). Using a dataset of soil samples, environmental circumstances, and their related best fruit harvest, we compare the performance of several machine learning methods, such as Random forests (Rf), Support vector machines (SVM), Naive Bayes (NB), Logistic Regression, and K-Nearest Neighbours (KNN). To increase the accuracy of the less accurate models, feature selection strategies and hyperparameter tuning are then explored. Building an ensemble machine learning model by integrating these improved models with the Stacking classifier and Voting classifier. Based on the chemical makeup of the soil and other environmental parameters, our model can help farmers and agronomists make educated judgements about which fruit crops to produce. Farmers can choose the most suitable fruit crop by taking into account these variables, there by increasing agricultural production and maintaining food security.