AI Driven Crop Guardian For Farmers

Abstract—Agriculture still plays a very large role in economy and food security issues, yet farmers also struggle with planting the right crops, depleting soil of nutrients, and weather which changes without notice. In the traditional approach of experience based farming which we see today, we see that it plays a large role in farm decisions which in turn leads to sometimes poor performance and profit. In this paper we present a smart crop recommendation and soil health prediction system that uses soil nutrient analysis and machine learning to better the farmer's lot. We put into play here a system which looks at key soil elements of Nitrogen (N), Phosphorus (P) and Potassium (K) as well as also looking at environment and crop production variables. Soil quality is assessed and optimal crops and fertilizers are recommended using a supervised learning model involving Linear Regression and Random Forest classification. The system's data-driven recommendations enhance crop productivity, minimize manual labor, and support sustainable farming. The aim of the solution is to assist farmers in prudent decision-making, relieve the anxiety associated with uncertain crop performance, improve their yield, and reduce their expenses. AI Driven Crop Guardian, the proposed system, machine learning algorithms such as Linear Regression, XGBoost, Resnet and Random Forest are employed to inspect the nutrient level in soil and to decide how healthy the crops can be grown there. The system informs you of cheapest, most productive crop for your soil type, and if necessary provides fertilizer advice to improve fertility of said soil. The objective of the data-based crop advisory model would be to assist farmers get accurate, scientific information, do away with guess work and ensure higher crops output and more adoption of environment-friendly farming techniques.