Expert system for diagnosis mango diseases using leaf symptoms analysis

This research presents the development of an expert system for diagnosis plant diseases in Barracuda mango (Nam-Dok Mai) which is one of a major export agricultural yield of Thailand. However, Thailand is in a tropical country and the climate causes the variation of plant diseases that affect to the growth of mango trees. Many type of agriculture yield are decreased due to an agriculturist are lacking of knowledge on how to classify type of plant disease correctly. Moreover, there is no suggestion system for a decision-making in choosing a suitable way to prevent or treat the disease that occur in their farm. This causes a lot of error in their infected plant treatments. Therefore, this system has been developed for help an agriculturist to diagnose the infected plant and to solve the problem immediately. The agriculturist should have the application which work in process of specific plant disease diagnosis as an expert human work. The plant diagnosis application applies a knowledge base system in form of rule-based model obtained by data mining technique. This paper present rule-based model with leaf image dataset. Experimental results performed that the rule-based model with 129 leaf images which collected from mango field area under supervision of product quality and standardization, Maejo University and 3 answer of class label (Anthracnose, Algal Spot, normal) has 89.92% of accuracy. From the experimental result indicated that the rules-based model can be applied to the plant diagnosis application.