AI-Based Agriculture Application for Crop Recommendation and Guidance System for Farmers

Agriculture plays a crucial role in safeguarding food security and economic stability, especially in countries where a significant population relies on farming. However, traditional farming practices are often marred by challenges such as suboptimal crop selection, inefficient fertilizer management, disease outbreaks, and market price uncertainties. This paper compares different algorithms like Gaussian Naive Bayes, SVM, Decision tree regression, XGBoost, random forest, logistic regression, etc., and based on the result generated the best algorithm will be used. The proposed smart agriculture system includes applications for crop recommendation, fertilizer guidance, plant disease prediction, and crop price forecasting and will incorporate these applications into a single website which later can also be developed and connected through the servers. This research provides the farmers with new and advanced information regarding their soil and field data. Through binding the power of data-driven decision-making, this project aims to enhance agricultural productivity, encourage sustainability, and empower farming communities. Continuous improvement, user feedback, and interdisciplinary collaboration are integral to the project's success.