Development of a Support System Crop Yield Growth using Fuzzy Logic Machine Learning for Chili Plant

Abstract

The increasing global population necessitates improved crop yields for food supply and preventing starvation. Agriculture, especially in developing countries like Malaysia, faces challenges like yield forecasting, soil health, and natural disasters. The research aims to create a Fuzzy Logic System to predict chili plant growth based on input parameters; soil moisture and temperature. The system uses Fuzzy Inference System (FIS) and MATLAB software to analyze soil and obtain precise crop growth values. The fuzzy modelling takes into account the triangular membership function. Data from MARDI is used to investigate crop growth under various conditions. The findings are validated by the MARDI organization. The performance of fuzzy logic for Crop Yield Prediction for Soil Analysis was evaluated. The model showed some accuracy but required constant optimization. The system offers farmers a flexible, adaptable approach to crop growth prediction, considering environmental factors like soil moisture and temperature, enhancing agricultural practices and production. This work contributes to precision agriculture and smart farming, providing innovative tools for better decision-making and resource management.