JOURNAL OF SOFTWARE ENGINEERING & INTELLIGENT SYSTEMS ISSN 2518-8739 31 December 2019, Volume 4, Issue 3, JSEIS, CAOMEI Copyright © 2016-2019 www.jseis.org Corresponding Author: Jonah Joshua 132 Email Address: moosayimahdis@gmail.com DIAGNOSIS MODEL OF SOY BEANS DISEASES USING NEURO-FUZZY SYSTEM IBRAHIM RAHMON 1 , OLUTAYO AJAYI 2 , MONDAY EZE 3 , JONAH JOSHUA 4 1,3,4 Babcock University, Nigeria 2 Federal University of Agriculture Abeokuta, Nigeria Email: rahmon.ibrahim@yahoo.com 1 , olutayoajayi@gmail.com 2 , ezem@babcock.edu.ng 3 , joshuaj@babcock.edu.ng 4 ABSTRACT Soybean is an important legume crop, extensively cultivated for food on which low-income population highly depend on its proteineous nutrient on daily basis for food. and oil. Soy beans consumption have been the major cheap protein-rich grain useful for treatment of malnutrition among children, for fighting against diabetes, high blood pressure, etc. Despite the nutritional and economic value of soy bean crop, a variety of pest attack such as fungi, nematode, bacteria and viruses are speedily becoming a constraint to quality and bountiful harvest. The effort of farmers to specifically identify the specific pest responsible for damaging of plants’ segment such as roots, stem, pod and leaves still remain vague and imprecise to many farmers. In this work, a neuro-fuzzy system was built with MATLAB version 8 with100 rules on six input parameter as linguistic variable or symptoms into the system to determine the disease type either as fungi or bacteria or virus and to also determine intensity rate, that is, level of damage, as the output in form of a crisp. The proposed Neuro-Fuzzy system was developed through MATLAB software using Adaptive Neuro-Fuzzy Inference System (ANFIS) box. ANFIS hybridizes the learning capacity of neural network with if-then rules of fuzzy logic to learn and design the most fitted membership function for a given set of data and thereby map the input with output. The proposed Neuro-Fuzzy System consisted of five stage: input stage, fuzzification, rule base, inference engine and defuzzification. The output of the system was to produce results for the decision maker to provide solution regarding the treatment of the infected plant for bountiful and quality harvest. Keywords: neuro-fuzzy system; crisp; matlab; fuzzification; de-fuzzification; 1. INTRODUCTION The technological evolution in computing has been the principal tool in increasing agricultural products. Nevertheless, there are numerous problems and constraints working against the bountiful and quality harvest of soybean commercial production [3]. ICTs play vital role in facilitating agricultural growth. The scientific and technological developments, which include e-agriculture, decision support system for farmers and mobile applications, have tremendously delivered relevant services for farmers in tackling all forms of crop diseases. ICTs have promoted new farming techniques and distributed new knowledge using computing technology for facilitating diagnosis and treatment of crop diseases [10]. Since the discovery of artificial intelligence (AI) theories and techniques over a decade, there have been tremendous growths in the development of expert systems in providing solution to some uncertain and imprecise tasks. The capacity and efficiency of expert system in imitating human reasoning process and providing relevant advice similar to human expertise has singled it out as one of the artificial intelligence branches widely embraced in many fields today [11]. Neuro-Fuzzy as one of the artificial intelligence methods found to be more effective in developing medical system that provide optimal solution to problems that are vague and imprecise in nature. Neuro-fuzzy is an efficient technique that combines the strength of two different techniques, namely; artificial neural network (ANN) and fuzzy logic (FL) in which back-propagation algorithm of ANN performs the computation of the fuzzy system parameters. The choice of neuro-fuzzy technique for this work is justified as a result of need to accept six major symptoms of soybean disease as input parameters for disease classification and computation of intensity proportion of a particular disease. In spite of the economical value of soybeans to the farmers, nutritional advantages to the body system of consumers, and the use of organic and inorganic fertilizers to increase total income and bountiful harvest, the main constraints and threats challenging large production of soybean are pests attack and diseases infections. Academic Editor: Muhammad Jehanzeb, APCOMS, Rawalpindi. Pakistan Published: 31 December, 2019 Funding: Authors have no funds. Article Type: Research Article