Abstract:
Agriculture is a key sector in Bangladesh, providing employment and sustenance to a significant portion of the population. However, many farmers struggle with selecting the most suitable crops for their land and applying the appropriate fertilizers, which leads to reduced productivity and economic losses. To address this challenge, precision agriculture offers a data-driven approach to improving decision-making in farming. Precision agriculture incorporates advanced techniques that analyze soil characteristics, soil types, and crop yield data to provide informed recommendations for farmers. By minimizing errors in crop and fertilizer selection, this method significantly enhances agricultural efficiency and output.This paper introduces a machine learning-based crop recommendation system that employs a majority voting technique, integrating algorithms such as Random Forest, Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression. These models are used to analyze site-specific parameters and predict the most suitable crop with high accuracy. Additionally, real-time testing is implemented through an IoT-based system. The fertilizer recommendation module is designed using Python logic, where the system compares user-input soil data with optimal nutrient values. The nutrient with the most substantial variation is classified as either HIGH or LOW, prompting tailored fertilizer suggestions
