Fertilizer Recommendation Using Deep Soil Inspection

Abstract

In global agriculture, it is increasingly important to manage fertilizer application in a way that enhances crop productivity, promotes food security, and protects the environment. Farmers have access to these traditional farming techniques for their crops which generally include recommended doses of fertilizers that most of the time disregard the different farming situations. The aim of this research is to propose a data-based fertilizer recommendation system based on XG Boost, one of the most promising algorithms in terms of prediction and computation power. The study takes into account all parameters that affect the value of the fertilizer including soil nutrients content (NPK values), weather, average temperature, water supply, and type of soil. The integration of these factors into the model aims at giving recommendations that will improve the yields of the crops and at the same time minimize the adverse effects on the environment. With respect to the data collection, there searchers took soil samples for nitrogen, phosphorus and potassium, collected the relevant climatic data from the meteorological service, and reviewed the information from the local irrigation authorities on the water supply. Pre processing steps include filling in missing data, defining outliers, and scaling variables so that all variables are on comparable ranges for the integrity of the data set. In order to minimize the number of variables in the model, correlation analysis and recursive feature elimination methods are used. The sensitivity analysis reveals that even slight modifications in soil NPK levels considerably change fertilizer recommendations, which stresses the need for proper soil testing. The model works as a decision-support mechanism for the farmers by giving information from the data for the farmer’s locality that is like lyto enhance their output with less environmental degradation. Numerous challenges are faced along the way, even though the results are hopeful, such as the geographical limit and non- consideration of pest control. Future studies could collect data in other areas with different crops and make use of IoT for data collection on the certain region in real time. It has been illustrated in this study how the XG Boost algorithm can be employed to generate accurate fertilizer application advice, and that there is a need to start practicing precision agriculture which is aimed at growing food in a more efficient way and addressing the food security challenges across the globe by increasing crop production.

Keywords: Fertilizer Recommendation, Sustainable Agriculture, XGBoost algorithm, Soil Parameter, Climate data, Crop management, precision farming, nutrient optimization, data-driven decision.