Paper Title
Predicting Soil Fertility and Managing Crops in Agriculture with Machine Learning Techniques

Abstract
In order to achieve these goals, this study aims to review the use of ML techniques in crop management and predict soil fertility properties in agriculture. The machine learning approaches can assist predicted the levels of phosphate, organic matter contents, and nutrient levels. These sources of data include soil composition, historical yields and climate trends. These projections will allow farmers to make better decisions on when to pick their crops, how much irrigation to dispense, and how much fertilizer to put on their fields, thereby increasing output and sustainability. Machine Learning-driven corresponding insights behind precision agriculture, which increases yields while leading to resource management and preserving the environment. Machine learning (ML) has great power to change agricultural operations, though obstacles remain such as interpretability of models or data quality. By describing recent achievements, current problems, and future opportunities, this study elucidates the research contribution that data-driven approaches will play in meeting modern agricultural needs. Keywords - Agriculture, AdaBoost, Extra Tree Classifier, Soil Fertility Prediction Nutrients in the Soil, Resilient Farming, Predictive Modeling.