Crop Prediction & Fertilizer Recommendation using AODE Algorithm

Abstract

Agriculture serves as the cornerstone of India’s global economic landscape, constituting a substantial proportion of the nation’s GDP. In light of the ever-expanding global population, safeguarding food security becomes an imperative task, and this responsibility rests upon the shoulders of the country’s agricultural productivity. This innovative data-driven solution harnesses the power of the Average One Dependence (AODE) algorithm in conjunction with a wealth of agricultural data to deliver highly accurate predictions of crop yields and tailored recommendations for optimal fertilizer usage. By delving into historical data and relevant influencing factors, and employing well-established Python libraries including Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and Streamlit, the system excels in data preprocessing, feature engineering, model training, and the creation of an intuitive web-based interface. Its primary goal is to provide farmers with precise forecasts, enabling them to make well-informed decisions regarding resource allocation and crop management. Designed as a comprehensive framework that encompasses data integration, advanced feature engineering, model training, and rigorous performance evaluation, this pioneering initiative paves the way for the advancement of precision agriculture. By enhancing overall productivity, optimizing resource utilization, and promoting sustainable farming practices, it holds the potential to reshape the landscape of agricultural operations. This system empowers farmers with data-driven insights, aiding resource allocation and enhancing productivity while addressing the challenges of crop selection and optimizing fertilizer use in agriculture. The Accuracy of the machine learning including the crop prediction & fertilizer prediction model is 76.36 %

Topics & Concepts