Influence of Membership Function and Degree on Sorghum Growth Prediction Models in Machine Learning

Abstract

Rapid advances in science and technology have significantly changed plant growth modeling. The main contribution to this transformation lies in using Machine Learning (ML) techniques. This study focuses on sorghum, an important agricultural crop with significant economic implications. Crop yield studies include temperature, humidity, climate, rainfall, and soil nutrition. This research has a novelty: the input factors for predicting sorghum plant growth, namely the treatment of applying organic fertilizer and dolomite lime to sorghum planting land. The three predicted sorghum plant growth factors, namely Height, Biomass, and Panicle weight, are the reasons for using the Multiple Adaptive Neural Fuzzy Inference System (MANFIS) model. This research investigates the impact of Membership Function and Degree on the MANFIS model. A comprehensive comparison of various membership functions, including Gaussian, Triangular, Bell, and Trapezoidal functions, along with various degrees of membership, has been carried out. The dataset used includes data related to sorghum growth obtained from field experiments. The main objective was to assess the effectiveness of membership and degree functions in accurately predicting sorghum growth parameters, consisting of height, biomass, and panicle weight. This assessment uses metrics such as MAPE (Mean Absolute Percentage Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) to evaluate the predictive performance of the MANFIS model when using four different types of membership functions and degrees. The results obtained the best level of accuracy in predicting panicle weight (ANFIS-3) with chicken manure treatment using the Trapezoidal membership function type and degree of membership function [3,3] with MAPE results of 5.77%, MAE of 0.2994, and RMSE of 0.395.