Incorporating Artificial Human Mentality (Kansei) in Intelligent Monitoring of Production Scheme for Customized Agro-Industrial Produce

A novel Customized Agro-industrial Produce Design (CAPD) scheme is highlighted to monitor the plant factory production systems. The expected outcome is to provide every consumer with a produce that matches his or her unique mentality. The new challenges of CAPD are consisting of non-linear and complex interaction between the bio-response parameters and human mentality process involved. As the solution, the intelligent modeling of Bayesian Belief Network (BBN) and Artificial Neural Network (ANN) were proposed. Artificial human mentality was generated using BBN by Kansei approach. Kansei is defined as consumer mentalities which solicit their preferences reasoning. ANN was utilized to incorporate the mentality in the monitoring of production scheme. The implementation of CAPD scheme is demonstrated via a case study of Eco-produce of moss greening (Rhacomitrium canescens). The produce choices were harvested using the specific modules. The research objectives are: 1) to model the artificial human mentality using BBN by Kansei approach; 2) to incorporate the artificial human mentality in CAPD scheme using ANN. The result indicated that BBN attained satisfied accuracy. ANN was able to classify the modules using the mentality and choices. The modules were characterized by textural features and Likert scale’s criteria. Both of the models were trained and validated based on benchmarking analysis (For BBN), sensitivity analysis (For ANN), minimum learning error and inspection data. Generally, the proposed CAPD is possibly applied for learning, mental simulation and monitoring in the early phase of customized produce development. Specifically it is applicable for Agro-industrial and Eco-produce design.