On the Development of Neuro-Fuzzy Expert System for Detection of Leghemoglobin (NFESDL) in Legumes

Abstract

The regular supply of affordable complete meals most especially protein from animals has been threatened. Protein sourced from animals carry too many health risks. Obesity, cancer, diabetes, etc., have been traced to the consumption of meats, most especially beef. Medical experts claim that some ailments are as a result of the chemically processed feeds given to raise animals. Therefore, an alternative to meat from plants is imperative. This led to the development of a neuro-fuzzy expert system for detection of leghemoglobin in legumes. This work utilized production rule-base technique and forward-chaining mechanisms with linguistic antecedent conditions to detect the presence of leghemoglobin in plants. To further remove clumsiness and ambiguity in the identification process, metrics/weights were obtained and attached to each morphological feature. MATLAB platform was employed for the development of the system. Class and objects were used to model the information elicited. The result is a system that detects the presence of leghemoglobin in plants. Keywords: Expert system, inference system, neuro-fuzzy, dataset, leghemoglobin