Intelligent diagnosis of diseases in plants using a hybrid Multi-Criteria decision making technique Wayne Goodridge, Margaret Bernard ⇑ , René Jordan, Reanne Rampersad The University of the West Indies, St. Augustine, Trinidad and Tobago article info Article history: Received 12 August 2016 Received in revised form 23 November 2016 Accepted 1 December 2016 Keywords: Plant disease diagnosis Analytic Hierarchy Process Sensitive-Simple Additive Weighting Multi-Criteria Decision Making Expert system AgriDiagnose abstract This paper describes an Expert System that can intelligently diagnose diseases in plants. The system is dialog-based and uses a Multi-Criteria Decision Making technique that is a hybrid of Analytic Hierarchy Process and Sensitive Simple Additive Weighting. The paper describes an approach for disease modeling that uses a set of characteristics which are weighted for each disease using two types of weights: Relative Weights and Scales. The diagnostic process involves calculating the utility value for each disease based on the utility values of its characteristics. Experimental results show an accuracy of over 95%. The system implemented is called AgriDiagnose and it consists of a web-based pathology tool to model the diseases and a mobile app for farmers to interact with the system for disease diagnosis in the field. Ó 2016 Elsevier B.V. All rights reserved. 1. Introduction Diseases have the potential to destroy large numbers of crops and can result in significant losses and food shortages if not detected and controlled in time. For example, the Papaya Ringspot virus affected the country of St. Kitts and destroyed about 90% of that country’s production (Chin et al., 2007). Many developing countries organize plant clinics for farmers at which farmers can be educated about various pests and diseases and where plant Pathologists can diagnose diseases from samples that farmers bring to the clinic. This is often in addition to visits to the farms by Agriculture Extension Officers. Much work has also been done in trying to automate diagnosis (Barbedo, 2016; Gonzalez- Andujar et al., 2006; Mansingh et al., 2007). These Artificial Intelli- gence systems generally either apply image processing techniques to images of diseased plants or use a data entry dialog system to attempt a diagnosis. In this paper, we present a dialog based system for diagnosis of plant diseases. The system uses a multi-criteria decision making technique that is a hybrid of Analytic Hierarchy Process (AHP) (Saaty, 1977) and Sensitive- Simple Additive Weighting (S-SAW) (Goodridge, 2016) to dynamically put forward questions to the farmers in an optimal way and to reason through their responses returning a diagnosis. A major contribution of the paper is the approach presented for modeling diseases using a consistent set of characteristics (criteria). The AHP is used for determining weights of these characteristics for all diseases in the system. The diagnosis process uses S-SAW for sensitivity analysis. The S-SAW is an extension of the popular SAW method (Hwang and Yoon, 1981) which allows the decision maker to define an objective function which governs the optimization goals of each criterion. This is used in calculating the utility value of each characteristic. This technique was implemented in a system called AgriDiag- nose, a system that consists of a back-end, web-based pathology tool and a front-end mobile app for farmers. The results obtained from experimentation gives a 95.9% accuracy for diagnosing the correct disease and a 100% sensitivity result that the system returns a positive result when the plant is indeed diseased. The rest of the paper is organized in this manner. In Section 2, we review some of the approaches taken in the literature to intel- ligent diagnosis of plant diseases. In Section 3, we describe the dis- ease modeling that is configured in the Pathology tool and in Section 4, we describe the diagnostic process. We trace one case study throughout these two sections so that the reader can follow the process with data. In Section 5, we reveal the results obtained from our simulation exercises and introduce four metrics for mea- suring these results. We conclude in Section 6. http://dx.doi.org/10.1016/j.compag.2016.12.003 0168-1699/Ó 2016 Elsevier B.V. All rights reserved. ⇑ Corresponding author. E-mail address: margaret.bernard@sta.uwi.edu (M. Bernard). Computers and Electronics in Agriculture 133 (2017) 80–87 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag