Optimizing Fertilizer Recommendations for Banana Plant Using Feature Extraction Method and Machine Learning Classification

Abstract

The Fertilizer Recommendation System for Banana Plant using Feature Extraction proposes a system for recommending the optimal fertilizer type and quantity for banana plants based on various features extracted from the soil and plant samples. The proposed system utilizes various machine learning techniques, such as feature extraction and classification, that analyze the data collected from the soil and plant samples. The study also involves data mining techniques to identify relevant features that affect the growth and health of the banana plant. The system provides an easy-to-use interface that enables farmers to input the collected data and receive customized fertilizer recommendations that are specific to their banana crop. The proposed system is expected to improve the yield and quality of banana crops while reducing the cost of fertilizers and minimizing environmental impact.